Previous research only focus on maximizing revenue for pricing strategies for information good with regardless the marginal and monitoring costs. This paper aims to focus on the addition of marginal and monitoring costs into the pricing strategies to maintain the maximal revenue while introduce the costs incurred in adopting the strategies. The well-known utility functions applied to also consider the consumer’s satisfaction towards the service offered. The results show that the addition costs incurred for setting up the strategies can also increase the profit for the providers rather than neglecting the costs. It is also showed that the Cobb-Douglas utility functions used can enhance the notion of provider to optimize the revenue compared to quasi linear and perfect substitutes.
Comprehensive Evaluation Model for the Implementation Effect of China's Trans...AI Publications
This document proposes a comprehensive evaluation model to assess the implementation effect of China's transmission and distribution tariff reform. It establishes a four-dimensional index system including 12 indicators to evaluate economic efficiency, safety and reliability, energy saving and environment protection, and satisfaction. A hybrid model is proposed using the Best-Worst Method to determine indicator weights and the Matter-Element Extension Model to rank performance. This evaluation model can provide references for policymakers to improve tariff reform policies.
This document describes a collaborative filtering approach using co-clustering with augmented data matrices (CCAM). CCAM extends a co-clustering algorithm based on information theory to simultaneously cluster users, items, and additional data (e.g. user profiles, item features). The authors apply CCAM to collaborative filtering by using the co-clusters as prototypes for predicting user ratings. They tune CCAM's parameters on a dataset from an online advertising site and compare its mean absolute error to other collaborative filtering methods. CCAM outperforms k-means clustering, k-nearest neighbors, and information-theoretic co-clustering on this task.
Benchmarking medium voltage feeders using data envelopment analysis: a case s...TELKOMNIKA JOURNAL
Feeder performance evaluation is a key component in improving the power system network.
Currently there is no proper method to find the performance of Medium Voltage Feeders (MVF) except the
number of feeder failures. Performance benchmarking may be used to identify actual performance of
feeders. The results of such benchmarking studies allow the organization to compare feeders with
themselves and identify poorly performing feeders. This paper focuses on prominent benchmarking
techniques used in international regulatory regime and analyses the applicability to MVFs.
Data Envelopment Analysis (DEA) method is selected to analyze the MVFs. Correlation analysis and DEA
analysis are carried out on different models and then the base model is selected for the analysis.
The relative performance of the 32 MVFs of Western Province, Sri Lanka is evaluated using the DEA.
Relative efficiency scores are identified for each feeder. Also the feeders are classified according to the
sensitivity analysis. The results indicate that the DEA analysis may be conveniently employed to evaluate
the performance of the MVFs. The evaluation is carried out once or twice a year with the MV distribution
development plan in order to identify the performance of the feeders and to utilize the available limited
resources efficiently.
Loss allocation in distribution networks with distributed generators undergoi...IJECEIAES
In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing “Quadratic method” using a 33-bus radial distribution system with/without DGs.
Cost Allocation of Reactive Power Using Matrix Methodology in Transmission Ne...IJAAS Team
This document presents a matrix methodology for allocating the costs of reactive power flows in transmission networks. The methodology traces reactive power flows through the network using Kirchhoff's current law and proportional sharing principles. It then allocates the costs of reactive power to generators and loads using the MVAr-mile method. The methodology is demonstrated on sample 6-bus and IEEE 14-bus test systems. Results show the allocated reactive power flows to different loads and the costs allocated to loads for recovery of total reactive power costs in the transmission network.
Recent many works have concentrated on
dynamically turning on/off some base stations (BSs) in order to
improve energy efficiency in radio access networks (RANs). In
this survey, we broaden the research over BS switching
operations, which should competition up with traffic load
variations. The proposed method formulate the traffic variations
as a Markov decision process which should differ from dynamic
traffic loads which are still quite challenging to precisely forecast.
A reinforcement learning framework based BS switching
operation scheme was designed in order to minimize the energy
consumption of RANs. Furthermore a transfer actor-critic
algorithm (TACT) is used to speed up the ongoing learning
process, which utilizes the transferred learning expertise in
historical periods or neighboring regions. The proposed TACT
algorithm performs jumpstart and validates the feasibility of
significant energy efficiency increment.
techTransmission usage and cost allocation using shapley value and tracing me...elelijjournal
In the deregulated power system, transmission pricing has become a very important task because it is necessary to develop an efficient, feasible and reliable pricing scheme that can generate the useful economic signals to network users such as generating companies, transmission companies, distribution companies and customers. The objective of this paper is to compare transmission usage and cost allocation scheme to loads (and/or generators) based on Shapley value method and power flow tracing method.Modified Kirchhoff matrix is used for power flow tracing. A comparison is done between the both methods.
A case study based on sample 6 bus power system is applied to check the feasibility and reliability of the proposed usage and cost allocation methodology.
This document proposes using a novel dynamic fuzzy c-means (dFCM) clustering technique combined with an artificial neural network (ANN) approach to reconfigure power distribution networks and minimize active power loss. The dFCM clustering is used to preprocess the input data for the ANN by transforming the input space into kernels, reducing the size of the ANN needed. The proposed framework combines dFCM clustering and ANN and is tested on two power networks, showing benefits like very short processing time, high accuracy, and a simple ANN structure with few neurons compared to other traditional reconfiguration methods.
Comprehensive Evaluation Model for the Implementation Effect of China's Trans...AI Publications
This document proposes a comprehensive evaluation model to assess the implementation effect of China's transmission and distribution tariff reform. It establishes a four-dimensional index system including 12 indicators to evaluate economic efficiency, safety and reliability, energy saving and environment protection, and satisfaction. A hybrid model is proposed using the Best-Worst Method to determine indicator weights and the Matter-Element Extension Model to rank performance. This evaluation model can provide references for policymakers to improve tariff reform policies.
This document describes a collaborative filtering approach using co-clustering with augmented data matrices (CCAM). CCAM extends a co-clustering algorithm based on information theory to simultaneously cluster users, items, and additional data (e.g. user profiles, item features). The authors apply CCAM to collaborative filtering by using the co-clusters as prototypes for predicting user ratings. They tune CCAM's parameters on a dataset from an online advertising site and compare its mean absolute error to other collaborative filtering methods. CCAM outperforms k-means clustering, k-nearest neighbors, and information-theoretic co-clustering on this task.
Benchmarking medium voltage feeders using data envelopment analysis: a case s...TELKOMNIKA JOURNAL
Feeder performance evaluation is a key component in improving the power system network.
Currently there is no proper method to find the performance of Medium Voltage Feeders (MVF) except the
number of feeder failures. Performance benchmarking may be used to identify actual performance of
feeders. The results of such benchmarking studies allow the organization to compare feeders with
themselves and identify poorly performing feeders. This paper focuses on prominent benchmarking
techniques used in international regulatory regime and analyses the applicability to MVFs.
Data Envelopment Analysis (DEA) method is selected to analyze the MVFs. Correlation analysis and DEA
analysis are carried out on different models and then the base model is selected for the analysis.
The relative performance of the 32 MVFs of Western Province, Sri Lanka is evaluated using the DEA.
Relative efficiency scores are identified for each feeder. Also the feeders are classified according to the
sensitivity analysis. The results indicate that the DEA analysis may be conveniently employed to evaluate
the performance of the MVFs. The evaluation is carried out once or twice a year with the MV distribution
development plan in order to identify the performance of the feeders and to utilize the available limited
resources efficiently.
Loss allocation in distribution networks with distributed generators undergoi...IJECEIAES
In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing “Quadratic method” using a 33-bus radial distribution system with/without DGs.
Cost Allocation of Reactive Power Using Matrix Methodology in Transmission Ne...IJAAS Team
This document presents a matrix methodology for allocating the costs of reactive power flows in transmission networks. The methodology traces reactive power flows through the network using Kirchhoff's current law and proportional sharing principles. It then allocates the costs of reactive power to generators and loads using the MVAr-mile method. The methodology is demonstrated on sample 6-bus and IEEE 14-bus test systems. Results show the allocated reactive power flows to different loads and the costs allocated to loads for recovery of total reactive power costs in the transmission network.
Recent many works have concentrated on
dynamically turning on/off some base stations (BSs) in order to
improve energy efficiency in radio access networks (RANs). In
this survey, we broaden the research over BS switching
operations, which should competition up with traffic load
variations. The proposed method formulate the traffic variations
as a Markov decision process which should differ from dynamic
traffic loads which are still quite challenging to precisely forecast.
A reinforcement learning framework based BS switching
operation scheme was designed in order to minimize the energy
consumption of RANs. Furthermore a transfer actor-critic
algorithm (TACT) is used to speed up the ongoing learning
process, which utilizes the transferred learning expertise in
historical periods or neighboring regions. The proposed TACT
algorithm performs jumpstart and validates the feasibility of
significant energy efficiency increment.
techTransmission usage and cost allocation using shapley value and tracing me...elelijjournal
In the deregulated power system, transmission pricing has become a very important task because it is necessary to develop an efficient, feasible and reliable pricing scheme that can generate the useful economic signals to network users such as generating companies, transmission companies, distribution companies and customers. The objective of this paper is to compare transmission usage and cost allocation scheme to loads (and/or generators) based on Shapley value method and power flow tracing method.Modified Kirchhoff matrix is used for power flow tracing. A comparison is done between the both methods.
A case study based on sample 6 bus power system is applied to check the feasibility and reliability of the proposed usage and cost allocation methodology.
This document proposes using a novel dynamic fuzzy c-means (dFCM) clustering technique combined with an artificial neural network (ANN) approach to reconfigure power distribution networks and minimize active power loss. The dFCM clustering is used to preprocess the input data for the ANN by transforming the input space into kernels, reducing the size of the ANN needed. The proposed framework combines dFCM clustering and ANN and is tested on two power networks, showing benefits like very short processing time, high accuracy, and a simple ANN structure with few neurons compared to other traditional reconfiguration methods.
ETOU electricity tariff for manufacturing load shifting strategy using ACO al...journalBEEI
This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
IRJET- Reducing electricity usage in Internet using transactional dataIRJET Journal
This document summarizes a research paper that proposes a method to reduce electricity usage and costs for internet services by optimizing how transactional data is mapped across geographically distributed data centers. It formulates the problem as a stochastic programming problem to maximize energy utilization within a cost budget. An efficient online algorithm is developed using Lyapunov optimization to map user requests to data centers based on changing factors like electricity prices and workload, with the goal of significantly reducing costs compared to baseline strategies. The system architecture involves front-end servers collecting user requests and dispatching them to appropriate back-end data centers for processing.
Content-aware resource allocation model for IPTV delivery networksIJECEIAES
This document proposes a content-aware resource allocation model (CARAM) for IPTV delivery networks. CARAM considers content characteristics like size, popularity, and interactivity to estimate content load and decide required network resources. It integrates resource allocation and replica placement to efficiently allocate storage, bandwidth, and CPU based on replication needs. The paper formulates the content-aware resource allocation problem as a constrained binary optimization problem and uses a hybrid genetic algorithm to evaluate the effect of content status on resource allocation under different popularity distributions.
FORECASTING THE WIMAX TRAFFIC VIA MODIFIED ARTIFICIAL NEURAL NETWORK MODELSijaia
This paper attempts to present a new approach of forecasting the WiMAX traffic by exploiting Artificial
Neural Networks (ANN). To develop the model, actual data is gathered from the LibyaMax network that spans the duration of 180 days in total. Traffic data is separated into three cases based on the base stations involved (A, B and AB). The model implements traffic prediction by emphasizing on the maximum and minimum number of online user whereby two different learning algorithms are tested upon. to find the optimal one. Overall, the experimentation shows promising results of which the most severe error of prediction is not more than 0.0014. This indicates the feasibility of making accurate forecasting of both daily and weekly traffic of the WiMAX network based solely on the maximum and minimum number of users online.
Bit Error Rate Analysis in Multicast Multiple Input Multiple Output Systemsrahulmonikasharma
At the present time whole information and communication technology industry contributes to the global carbon emission. With the aim of reducing the carbon footprint and the operating cost of wireless networks, overall energy reduction is required in the region of two to three orders of magnitude. Meanwhile, significant increase of the network spectrum efficiency is needed to cope with the exponentially increasing traffic loads. Due to this factors spatial modulation (SM) has recently established itself as promising transmission concept which belongs to single-radio frequency large scale multiple input multiple output (MIMO) wireless system. Spatial modulation MIMO takes advantage of whole antenna array at the transmitter, while using limited number of radio frequency chains. The multiple input multiple output multiplies capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing (OFDM), which divides a radio channel into many closely spaced sub channels to provide more reliable communication at high speeds. The system calculate the bit error rate (BER) for multicast multiple input multiple output system with the spatial modulation (SM) and study the effect of signal to noise ratio on bit error rate. MATLAB software is use to simulate system. The simulation results show that bit error rate decreases as signal to noise ratio increases. System reaches zero bit error rate for the value of signal to noise ratio greater than 18dB. System has provided less bit error rate for large signal to noise ratio which improves system performance.
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRETELKOMNIKA JOURNAL
Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives.
This document discusses the development of a probabilistic network-level pavement management system using Markov processes. Pavements are grouped into families with similar characteristics. Markov transition probability matrices are developed for each family to model the uncertain deterioration of pavements over time. The Markov models are integrated with dynamic programming and prioritization programs to determine optimal maintenance and rehabilitation recommendations under budget constraints and provide the highest network performance. The developed system is applied to an airport pavement network as an example.
Comparative analysis of various data stream mining procedures and various dim...Alexander Decker
This document provides a comparative analysis of various data stream mining procedures and dimension reduction techniques. It discusses 10 different data stream clustering algorithms and their working mechanisms. It also compares 6 dimension reduction techniques and their objectives. The document proposes applying a dimension reduction technique to reduce the dimensionality of a high-dimensional data stream, before clustering it using a weighted fuzzy c-means algorithm. This combined approach aims to improve clustering quality and enable better visualization of streaming data.
Ant-colony and nature-inspired heuristic models for NOMA systems: a reviewTELKOMNIKA JOURNAL
The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
1) The document discusses a progressive domain expansion method for solving optimal control problems and applying it to managing water flows between the Kainji and Jebba hydropower reservoirs in Nigeria.
2) It presents the system dynamics model and formulates an optimal control problem to determine the best water inflow control from Kainji to keep Jebba's operating head at a nominal level.
3) Solving the optimal control problem results in a two-point boundary value problem that is solved using progressive domain expansion, which is a modification of the shooting method. The method determines the optimal inflow control to maximize power generation at Jebba.
Ying hua, c. (2010): adopting co-evolution and constraint-satisfaction concep...ArchiLab 7
This document summarizes a research paper that proposes using genetic algorithms combined with co-evolution and constraint-satisfaction concepts to solve supply chain network design problems. The paper first reviews supply chain integration models, genetic algorithms, co-evolutionary modes, and constraint-satisfaction modes from previous literature. It then presents a supply chain network model with multiple levels and costs. The model aims to minimize total costs while meeting demand and resource constraints. Finally, the paper proposes a genetic algorithm approach that uses co-evolution to consider multiple criteria dynamically and constraint-satisfaction to narrow the search space, helping find optimal solutions to complex supply chain network design problems.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
A Genetic Algorithm Based Approach for Solving Optimal Power Flow ProblemShubhashis Shil
This document describes a study that uses a genetic algorithm to solve the optimal power flow problem. The optimal power flow problem aims to minimize operating costs in a power system by optimizing generator outputs while meeting demand and constraints. The study develops a genetic algorithm approach and compares its results and computation time to traditional derivative-based methods on some example power flow cases. It finds that the genetic algorithm approach produces nearly equivalent results to traditional methods, but requires significantly less computation time to solve the optimal power flow problem, especially as more constraints are added.
Generalized optimal placement of PMUs considering power system observability,...IJECEIAES
This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
IMPROVED PROPAGATION MODELS FOR LTE PATH LOSS PREDICTION IN URBAN & SUBURBAN ...ijwmn
To maximize the benefits of LTE cellular networks, careful and proper planning is needed. This requires the use of accurate propagation models to quantify the path loss required for base station deployment. Deployed LTE networks in Ghana can barely meet the desired 100Mbps throughput leading to customer dissatisfaction. Network operators rely on transmission planning tools designed for generalized environments that come with already embedded propagation models suited to other environments. A challenge therefore to Ghanaian transmission Network planners will be choosing an accurate and precise propagation model that best suits the Ghanaian environment. Given this, extensive LTE path loss measurements at 800MHz and 2600MHz were taken in selected urban and suburban environments in Ghana and compared with 6 commonly used propagation models. Improved versions of the Ericson, SUI, and ECC-33 developed in this study predict more precisely the path loss in Ghanaian environments compared with commonly used propagation models.
Efficient P2P data dissemination in integrated optical and wireless networks ...TELKOMNIKA JOURNAL
The Quality of Service (QoS) resource consumption is always the tricky problem and also
the on-going issue in the access network of mobile wireless part because of its dynamic nature of network
wireless transmissions. It is very critical for the infrastructure-less wireless mobile ad hoc network that is
distributed while interconnects in a peer-to-peer manner. Toward resolve the problem, Taguchi method
optimization of mobile ad hoc routing (AODVUU) is applied in integrated optical and wireless networks
called the adLMMHOWAN. Practically, this technique was carry out using OMNeT++ software by building
a simulation based optimization through design of experiment. Its QoS network performance is examined
based on packet delivery ratio (PDR) metric and packet loss probabilities (PLP) metric that consider
the scenario of variation number of nodes. During the performing stage with random mobile connectivity
based on improvement in optimized front-end wireless domain of AODVUU routing, the result is performing
better when compared with previous study called the oRia scheme with the improvement of 14.1% PDR
and 43.3% PLP in this convergence of heterogeneous optical wireless network.
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Mixed integer nonlinear programming (MINLP)-based bandwidth utility function ...IJECEIAES
The development of the internet in this era of globalization has increased fast. The need for internet becomes unlimited. Utility functions as one of measurements in internet usage, were usually associated with a level of satisfaction of users for the use of information services used. There are three internet pricing schemes used, that are flat fee, usage based and two-part tariff schemes by using one of the utility function which is Bandwidth Diminished with Increasing Bandwidth with monitoring cost and marginal cost. Internet pricing scheme will be solved by LINGO 13.0 in form of non-linear optimization problems to get optimal solution. The optimal solution is obtained using the either usage-based pricing scheme model or two-part tariff pricing scheme model for each services offered, if the comparison is with flat-fee pricing scheme. It is the best way for provider to offer network based on usage based scheme. The results show that by applying two part tariff scheme, the providers can maximize its revenue either for homogeneous or heterogeneous consumers.
Cost Analysis of ComFrame: A Communication Framework for Data Management in ...IOSR Journals
The document discusses a cost-benefit analysis of ComFrame, a communication framework for data management in mobile location-based services. It analyzes the costs of installing and operating ComFrame servers over an 8-year period and finds the net present value is positive, indicating the benefits outweigh the costs. The analysis groups costs and benefits into a table to calculate the total costs, benefits, and net benefit for each year. It determines ComFrame's purchase and use is reasonable as the servers will save costs each month going forward.
ETOU electricity tariff for manufacturing load shifting strategy using ACO al...journalBEEI
This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
IRJET- Reducing electricity usage in Internet using transactional dataIRJET Journal
This document summarizes a research paper that proposes a method to reduce electricity usage and costs for internet services by optimizing how transactional data is mapped across geographically distributed data centers. It formulates the problem as a stochastic programming problem to maximize energy utilization within a cost budget. An efficient online algorithm is developed using Lyapunov optimization to map user requests to data centers based on changing factors like electricity prices and workload, with the goal of significantly reducing costs compared to baseline strategies. The system architecture involves front-end servers collecting user requests and dispatching them to appropriate back-end data centers for processing.
Content-aware resource allocation model for IPTV delivery networksIJECEIAES
This document proposes a content-aware resource allocation model (CARAM) for IPTV delivery networks. CARAM considers content characteristics like size, popularity, and interactivity to estimate content load and decide required network resources. It integrates resource allocation and replica placement to efficiently allocate storage, bandwidth, and CPU based on replication needs. The paper formulates the content-aware resource allocation problem as a constrained binary optimization problem and uses a hybrid genetic algorithm to evaluate the effect of content status on resource allocation under different popularity distributions.
FORECASTING THE WIMAX TRAFFIC VIA MODIFIED ARTIFICIAL NEURAL NETWORK MODELSijaia
This paper attempts to present a new approach of forecasting the WiMAX traffic by exploiting Artificial
Neural Networks (ANN). To develop the model, actual data is gathered from the LibyaMax network that spans the duration of 180 days in total. Traffic data is separated into three cases based on the base stations involved (A, B and AB). The model implements traffic prediction by emphasizing on the maximum and minimum number of online user whereby two different learning algorithms are tested upon. to find the optimal one. Overall, the experimentation shows promising results of which the most severe error of prediction is not more than 0.0014. This indicates the feasibility of making accurate forecasting of both daily and weekly traffic of the WiMAX network based solely on the maximum and minimum number of users online.
Bit Error Rate Analysis in Multicast Multiple Input Multiple Output Systemsrahulmonikasharma
At the present time whole information and communication technology industry contributes to the global carbon emission. With the aim of reducing the carbon footprint and the operating cost of wireless networks, overall energy reduction is required in the region of two to three orders of magnitude. Meanwhile, significant increase of the network spectrum efficiency is needed to cope with the exponentially increasing traffic loads. Due to this factors spatial modulation (SM) has recently established itself as promising transmission concept which belongs to single-radio frequency large scale multiple input multiple output (MIMO) wireless system. Spatial modulation MIMO takes advantage of whole antenna array at the transmitter, while using limited number of radio frequency chains. The multiple input multiple output multiplies capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing (OFDM), which divides a radio channel into many closely spaced sub channels to provide more reliable communication at high speeds. The system calculate the bit error rate (BER) for multicast multiple input multiple output system with the spatial modulation (SM) and study the effect of signal to noise ratio on bit error rate. MATLAB software is use to simulate system. The simulation results show that bit error rate decreases as signal to noise ratio increases. System reaches zero bit error rate for the value of signal to noise ratio greater than 18dB. System has provided less bit error rate for large signal to noise ratio which improves system performance.
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRETELKOMNIKA JOURNAL
Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives.
This document discusses the development of a probabilistic network-level pavement management system using Markov processes. Pavements are grouped into families with similar characteristics. Markov transition probability matrices are developed for each family to model the uncertain deterioration of pavements over time. The Markov models are integrated with dynamic programming and prioritization programs to determine optimal maintenance and rehabilitation recommendations under budget constraints and provide the highest network performance. The developed system is applied to an airport pavement network as an example.
Comparative analysis of various data stream mining procedures and various dim...Alexander Decker
This document provides a comparative analysis of various data stream mining procedures and dimension reduction techniques. It discusses 10 different data stream clustering algorithms and their working mechanisms. It also compares 6 dimension reduction techniques and their objectives. The document proposes applying a dimension reduction technique to reduce the dimensionality of a high-dimensional data stream, before clustering it using a weighted fuzzy c-means algorithm. This combined approach aims to improve clustering quality and enable better visualization of streaming data.
Ant-colony and nature-inspired heuristic models for NOMA systems: a reviewTELKOMNIKA JOURNAL
The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
1) The document discusses a progressive domain expansion method for solving optimal control problems and applying it to managing water flows between the Kainji and Jebba hydropower reservoirs in Nigeria.
2) It presents the system dynamics model and formulates an optimal control problem to determine the best water inflow control from Kainji to keep Jebba's operating head at a nominal level.
3) Solving the optimal control problem results in a two-point boundary value problem that is solved using progressive domain expansion, which is a modification of the shooting method. The method determines the optimal inflow control to maximize power generation at Jebba.
Ying hua, c. (2010): adopting co-evolution and constraint-satisfaction concep...ArchiLab 7
This document summarizes a research paper that proposes using genetic algorithms combined with co-evolution and constraint-satisfaction concepts to solve supply chain network design problems. The paper first reviews supply chain integration models, genetic algorithms, co-evolutionary modes, and constraint-satisfaction modes from previous literature. It then presents a supply chain network model with multiple levels and costs. The model aims to minimize total costs while meeting demand and resource constraints. Finally, the paper proposes a genetic algorithm approach that uses co-evolution to consider multiple criteria dynamically and constraint-satisfaction to narrow the search space, helping find optimal solutions to complex supply chain network design problems.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
A Genetic Algorithm Based Approach for Solving Optimal Power Flow ProblemShubhashis Shil
This document describes a study that uses a genetic algorithm to solve the optimal power flow problem. The optimal power flow problem aims to minimize operating costs in a power system by optimizing generator outputs while meeting demand and constraints. The study develops a genetic algorithm approach and compares its results and computation time to traditional derivative-based methods on some example power flow cases. It finds that the genetic algorithm approach produces nearly equivalent results to traditional methods, but requires significantly less computation time to solve the optimal power flow problem, especially as more constraints are added.
Generalized optimal placement of PMUs considering power system observability,...IJECEIAES
This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
IMPROVED PROPAGATION MODELS FOR LTE PATH LOSS PREDICTION IN URBAN & SUBURBAN ...ijwmn
To maximize the benefits of LTE cellular networks, careful and proper planning is needed. This requires the use of accurate propagation models to quantify the path loss required for base station deployment. Deployed LTE networks in Ghana can barely meet the desired 100Mbps throughput leading to customer dissatisfaction. Network operators rely on transmission planning tools designed for generalized environments that come with already embedded propagation models suited to other environments. A challenge therefore to Ghanaian transmission Network planners will be choosing an accurate and precise propagation model that best suits the Ghanaian environment. Given this, extensive LTE path loss measurements at 800MHz and 2600MHz were taken in selected urban and suburban environments in Ghana and compared with 6 commonly used propagation models. Improved versions of the Ericson, SUI, and ECC-33 developed in this study predict more precisely the path loss in Ghanaian environments compared with commonly used propagation models.
Efficient P2P data dissemination in integrated optical and wireless networks ...TELKOMNIKA JOURNAL
The Quality of Service (QoS) resource consumption is always the tricky problem and also
the on-going issue in the access network of mobile wireless part because of its dynamic nature of network
wireless transmissions. It is very critical for the infrastructure-less wireless mobile ad hoc network that is
distributed while interconnects in a peer-to-peer manner. Toward resolve the problem, Taguchi method
optimization of mobile ad hoc routing (AODVUU) is applied in integrated optical and wireless networks
called the adLMMHOWAN. Practically, this technique was carry out using OMNeT++ software by building
a simulation based optimization through design of experiment. Its QoS network performance is examined
based on packet delivery ratio (PDR) metric and packet loss probabilities (PLP) metric that consider
the scenario of variation number of nodes. During the performing stage with random mobile connectivity
based on improvement in optimized front-end wireless domain of AODVUU routing, the result is performing
better when compared with previous study called the oRia scheme with the improvement of 14.1% PDR
and 43.3% PLP in this convergence of heterogeneous optical wireless network.
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Mixed integer nonlinear programming (MINLP)-based bandwidth utility function ...IJECEIAES
The development of the internet in this era of globalization has increased fast. The need for internet becomes unlimited. Utility functions as one of measurements in internet usage, were usually associated with a level of satisfaction of users for the use of information services used. There are three internet pricing schemes used, that are flat fee, usage based and two-part tariff schemes by using one of the utility function which is Bandwidth Diminished with Increasing Bandwidth with monitoring cost and marginal cost. Internet pricing scheme will be solved by LINGO 13.0 in form of non-linear optimization problems to get optimal solution. The optimal solution is obtained using the either usage-based pricing scheme model or two-part tariff pricing scheme model for each services offered, if the comparison is with flat-fee pricing scheme. It is the best way for provider to offer network based on usage based scheme. The results show that by applying two part tariff scheme, the providers can maximize its revenue either for homogeneous or heterogeneous consumers.
Cost Analysis of ComFrame: A Communication Framework for Data Management in ...IOSR Journals
The document discusses a cost-benefit analysis of ComFrame, a communication framework for data management in mobile location-based services. It analyzes the costs of installing and operating ComFrame servers over an 8-year period and finds the net present value is positive, indicating the benefits outweigh the costs. The analysis groups costs and benefits into a table to calculate the total costs, benefits, and net benefit for each year. It determines ComFrame's purchase and use is reasonable as the servers will save costs each month going forward.
Pricing Optimization using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to optimize pricing. Specifically:
1. It reviews previous research applying machine learning to price prediction and optimization in various industries like e-commerce, real estate, and insurance. Methods discussed include linear regression, clustering, random forests, and integer linear programming.
2. It then introduces using machine learning like regression trees and random forests to forecast demand and maximize revenue by setting optimal prices. Variables like holidays, promotions, and inventory are considered.
3. The goal of the paper is to develop a pricing algorithm that can predict and optimize daily prices in response to changing demand using machine learning techniques. Outcomes will demonstrate machine learning's ability to optimize pricing.
K-Nearest neighbor algorithm on implicit feedback to determine SOPTELKOMNIKA JOURNAL
The document describes research applying the k-nearest neighbor (KNN) algorithm to user log data to determine which standard operating procedures (SOPs) documents are most relevant to a user's preferences. The researchers collected implicit feedback data on users' searches of 11 SOP documents over 5 weeks. They applied the KNN algorithm to calculate distances between user data and each SOP document to classify them as suitable or unsuitable based on the user's profile. The results found a ratio of 3:2 for suitable vs. unsuitable classifications, indicating the KNN approach accurately identified SOPs matching 80% of user profiles based on their implicit feedback behavior.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
User-Centric Optimization for Constraint Web Service Composition using a Fuzz...ijwscjournal
ABSTRACT
Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed
environments. In such systems, however each service responds the user request independently, it is
essential to compose them for delivering a compound value-added service. Since, there may be a number of
compositions to create the requested service, it is important to find one which its properties are close to
user’s desires and meet some non-functional constraints and optimize criteria such as overall cost or
response time. In this paper, a user-centric approach is presented for evaluating the service compositions
which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on
quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem.
Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of
web service composition easier and more efficient.
Collaborative Filtering Approach For QoS PredictionEditor IJMTER
Many researchers propose that, not only functional but also non-functional properties, also
known as quality of service (QoS), should be taken into consideration when consumers select
services. Consumers need to make prediction on quality of unused web services before selecting.
Usually, this prediction is based on other consumers’ experiences. Being aware of different QoS
experiences of consumers, this paper proposes a collaborative filtering based approach to making
similarity mining and prediction from consumers’ experiences. Experimental results demonstrate that
this approach can make significant improvement on the effectiveness of QoS prediction for web
services.
USER-CENTRIC OPTIMIZATION FOR CONSTRAINT WEB SERVICE COMPOSITION USING A FUZZ...ijwscjournal
This document summarizes a research paper that proposes a user-centric approach for evaluating and optimizing constraint-based web service compositions using fuzzy logic and genetic algorithms. The approach uses fuzzy logic to model user preferences through quality criteria rankings. It then uses a genetic algorithm to optimize the composition process to maximize user satisfaction based on those fuzzy preferences. The results showed that the fuzzy-genetic algorithm system enables users to more easily and efficiently participate in and guide the web service composition process according to their needs and preferences.
USER-CENTRIC OPTIMIZATION FOR CONSTRAINT WEB SERVICE COMPOSITION USING A FUZZ...ijwscjournal
Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed environments. In such systems, however each service responds the user request independently, it is essential to compose them for delivering a compound value-added service. Since, there may be a number of compositions to create the requested service, it is important to find one which its properties are close to user’s desires and meet some non-functional constraints and optimize criteria such as overall cost or response time. In this paper, a user-centric approach is presented for evaluating the service compositions
which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem. Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of web service composition easier and more efficient.
Providing highly accurate service recommendation for semantic clustering over...IRJET Journal
This document proposes an adaptive recommendation system to provide accurate service recommendations for semantic clustering over big data. It combines item-based collaborative filtering and content-based filtering techniques to address issues like cold starts, sparsity, and scalability. The system first performs clustering to group similar services and reduce data size. It then uses the collaborative filtering approach of finding similar users to the target user and their item ratings to generate recommendations. The content-based filtering technique considers a user's past ratings to recommend related items. Combining these techniques improves accuracy and performance for big data applications. Evaluation of this adaptive recommendation system shows it can provide highly accurate recommendations and address current limitations.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
Web Service Recommendation using Collaborative FilteringIRJET Journal
This document summarizes a research paper on using collaborative filtering techniques for web service recommendation. It discusses two collaborative filtering methods - Pearson Correlation Coefficient (PCC) and Normalized Recovery Collaborative Filtering (NRCF). PCC and NRCF calculate similarity between users or services based on quality of service (QoS) metrics to find similar users/services and recommend services. The document explains the techniques used in PCC and NRCF for similarity computation and QoS value prediction.
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JAVA 2013 IEEE NETWORKING PROJECT Price differentiation for communication net...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Com...nexgentechnology
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A PROFIT MAXIMIZATION SCHEME WITH GUARANTEED QUALITY OF SERVICE IN CLOUD COMP...Nexgen Technology
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Puducherry.
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www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Com...nexgentechnology
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Puducherry.
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www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
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A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Com...nexgentechnology
1) A double resource renting scheme is proposed that combines short-term and long-term server renting to guarantee quality of service while maximizing cloud provider profits.
2) An M/M/m+D queuing model is used to represent the cloud system and analyze key performance indicators.
3) An optimal configuration problem is formulated and solved to determine the profit-maximizing number of long-term servers, balancing rental costs against meeting service-level agreements.
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Similar to Utility Function-based Pricing Strategies in Maximizing the Information Service Provider’s Revenue with Marginal and Monitoring Costs (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
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structure for the company. One possibility that some research already mentioned for applying fuzzy system
[10] and its approximation is the attempt to also include fuzzy utility function to model of pricing strategies.
Research on the theory of the pricing plan has been widely available, but only a few pricing plan
involving utility functions as an indicator of consumer satisfaction. Wu and Banker [9] has analyzed the
pricing scheme Internet by using one of the utility function is Cobb-Douglas modified utility function to
maximize benefits to the ISP. In their research, the usage of three schemes of pricing for information services
namely flat fee, usage-based, and the two-part tariff pricing schemes. Results of the analysis showed that the
pricing scheme of the flat fee and a two-part tariff generate more optimal solution than the usage-based
pricing schemes.
Then, further research on the pricing scheme internet has now will be involving other utility
functions such as original Cobb-Douglas, quasi linear, perfect substitute utility functions that are used in
three types of pricing schemes for information services that are flat fee, usage based and two-part tariff both
analytically [11], and numerically with the help of LINGO 11.0 software applications [12], [13]. Based on
these results, a new method of searching information services by considering the function of the precise
utility function have proven to generate huge profits for ISPs to adopt this type of pricing schemes, are
available, but the study only on the selection of utility functions that can maximizing profits for ISPs and
ignore the marginal costs and monitoring costs. Other research also consider pricing scheme of information
services with regard to perfect substitute [14] and Cobb-Douglas utility functions [15] where by using three
pricing strategies, and considering marginal and monitoring costs, the optimal case for each consumer can be
obtained. Based on that, the authors attempt to proceed with other utility function to be analyzed to flat fee,
usage based pricing strategies.
In general, the marginal costs are defined as the costs adjusted to the level of production of goods
which is resulting differences in fixed costs due to the addition of the number of units produced, while the
cost of monitoring is the cost incurred by the company to monitor and control the activities carried out by the
agency in managing company. In fact, the marginal cost and the cost of monitoring is also an important issue
in the development of information services primarily affects the maximum objective function for three
pricing schemes are flat fee, usage-based and two-part tariff. To that end, it is necessary study on marginal
costs and the cost of monitoring the pricing schemes involving information services utility functions that are
often used, which is perfect substitute utility function, quasi linear utility function, and Cobb-Douglas
function.
Then the main controbution of this paper is basically to extend the application of the pricing scheme
of pricing strategies of information service with regard to marginal and monitoring costs to enable provider to
have other insight on the advantage of applying marginal and monitoring costs to information service pricing
scheme and with the usage of quasi linear utility function.
2. RESEARCH METHOD
Steps conducted in this research are as follows.
1. Determine the information service pricing scheme models according to quasi linear, utility functions with
flat fee, usage-based, dan two-part tariff pricing scheme for homogeneous and heterogeneous consumers.
a. For flat fee pricing scheme, , and P adalah is positive.
b. For usage-based scheme, and are positive and P = 0.
c. For two-part tariff scheme, P, and are positive.
2. Formulate quasi linear utility function according to flat fee, usage-based, dan two-part tariff pricing
schemes for homogeneous and heterogeneous consumers with paying attention to marginal and
monitoring cots.
5. Process mail from local server.
6. Apply the optimal pricing scheme of local data server of mail traffic data.
7. Compare the pricing scheme models to each utility function previously described in previous research
proposed by [14], [15].
8. Conclude and obtain the best solution of information service pricing scheme.
3. RESULTS AND ANALYSIS
This section discusses other utility function that is also well kwown namely quasi linear utility
function. Original Cobb-Douglas [15], perfect substitute [16] and modified Cobb-Douglas utility
functions [9], [17] are already discussed in previous research, but the comparison from all these utility
functions are showed to explain the best utility function that can maximize the profit of ISP.
3. IJECE ISSN: 2088-8708
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879
3.1. Model of Pricing Scheme Based Quasi linear Utility Function
The general form of utility function based on the quasi linear U (X, Y) = a X + f (Y), where f (Y)= Y b
Here, quasi linear utility function analyzed for homogeneous and heterogeneous consumers (high-end and
low-end) as well as heterogeneous (high-demand and low-demand) consumers are based on three strategies
of pricing schemes that pricing schemes flat-fee, pricing schemes usage -based, two-part pricing scheme
tariff.
3.2. Homogeneous Consumer
Consumer Optimization Problems will be as follows.
( ) ( ) (1)
Subject to
̅ (2)
̅ (3)
( ) ( ) (4)
(5)
Optimization Problems of the providers will be as follows.
∑ ( ) (6)
with (X*,Y*,Z*) = argmax ( ) – PxX – PyY– PZ ( )
Subject to
̅
̅
( ) – – – ( )
For usage-based pricing scheme and a two-part tariff:
Consumer Optimization Problems:
( ) ( ) ( ) (7)
with constraints :
̅ (8)
̅ (9)
( ) ( ) ( ) (10)
(11)
Optimization Problems of Providers:
∑ ( ) (12)
with (X*,Y*,Z*) = argmax ( ) – PxX – PyY– PZ ( ) ( )
4. ISSN: 2088-8708
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with constraints:
̅
̅
( ) – – – ( ) ( )
Case 1. If the ISP is using flat-fee pricing scheme by setting , and . Optimization
problems consumers for flat-fee pricing scheme be :
( ) ( ) ( ) ( ) ( )
( ) ( )
By using Constraint (4), then
( ) ( )
( ) ( ) ( ) ( ) ( ) ( ) ( )
Optimization of provider became:
∑( ) ∑(( ) ( ) ( ))
∑ (( ) ( ) ( ) ( ) )
∑ ( ) ( )
This means that if the ISP provides this price, the level of consumer spending into ̅ and
̅ with maximum utility, consumers can get ̅ (̅) ( ̅ ̅) . ISP optimal price used is ̅
(̅) ( ̅ ̅) , the maximum benefit is ∑ , ̅ (̅) ( ̅ ̅) -.
Based on this case Lemma 1 can be stated as follows.
Lemma 1: If the ISP is using flat-fee pricing scheme, the optimal price is ̅ (̅) ( ̅ ̅) and the
maximum profit to be ∑ , ̅ (̅) ( ̅ ̅) -
Case 2. If ISPs use usage-based pricing scheme by setting , and . Optimization
problems consumers on usage-based pricing scheme be:
( ) ( ) ( ) (13)
To maximize Eq. (13), do differentiation of the X and Y:
( ( ) ( ) ( ) )
then
( ) ̅ (14)
and
( ( ) ( ) ( ) )
̅
and
(̅) ( ) ̅ (15)
5. IJECE ISSN: 2088-8708
Utility Function-Based Pricing Strategies in Maximizing the Information Service .... (Robinson Sitepu)
881
Optimization of production problems became:
∑( ) ∑, ( ̅) (̅)-
∑ [( ( )) ̅ ( (̅) ( ))̅]
∑, ̅ ̅ (̅) ( ) ̅ ( )̅-
To maximize the function optimization problem providers, ISPs should minimize and . If
known and decline, then X* and Y* increase, if X and Y are restricted, then X*= ̅ and Y*=̅. and
yang optimal into ( ) and (̅) ( ) with maximum profit ∑ , ̅ ̅ (̅)
( ) ̅ ( )̅-.
Then proceed to next lemma as follows.
Lemma 2: If ISPs use usage-based pricing scheme, the optimal price is ( ) and (̅)
( ), the maximum benefit is:∑ , ̅ ̅ (̅) ( ) ̅ ( )̅-.
Case 3. If the ISP uses a two-part pricing scheme tariff by setting , and . By using Eq.
(8)-(9). If these equation are substituted into Eq. (10) and to maximize the objective function (7), then:
For two-part tariff will be
( ) – ( ) ( )
( ) ( ( )) ̅ ( (̅) ( ))̅ ( ) ( )
̅ ( ) ̅ (̅) ( ) ̅ ( )̅ ( ) ( )
By substituting the value to the objective function (7)), optimization problems of providers being:
∑( ) ∑, ( ̅) (̅) -
∑,( ( )) ̅ ( (̅) ( ))̅
( ̅ (̅) ̅ (̅) ( ) ̅ ( )̅ ( ) ( ) )-
∑, ̅ (̅) ( ) ̅ ( )̅-
To maximize the function optimization problem providers, ISPs should minimize and . if
known and decline, then X* and Y* increase, if X and Y are bounded, then X*≤ ̅ and Y*≤ ̅ . In other
words, and optimal will be ( ) and (̅) ( ) The maximum gain is
achieved ∑ , ̅ (̅) ( ) ̅ ( )̅-
Based on this case, next lemma was obtained.
Lemma 3: If the ISP uses a two-part tariff rates, then best and be and (̅). Maximum
profit,∑ , ̅ (̅) ( ) ̅ ( )̅-
If it is assumed ̅ (̅) (̅); ̅ and function (̅)= is a non linear function, then
̅ ̅ (̅) ( ) ̅ ( )̅ ∑ , ̅ (̅) ( ̅ ̅) - ̅ (̅) ( ) ̅ ( )̅,
usage-based pricing schemes generate greater profits than the flat-fee and two-part tariff pricing schemes for
homogeneous consumer.
3.3. Heterogeneous Consumer
Suppose that there are m high-end consumer (the upper class) (i = 1) and n low-end consumer
(lower class) (i = 2). To find heterogeneous consumers' willingness to pay a given price scheme
affect881881881 service providers, it is assumed every consumer in both segments have an upper limit on the
same X and Y at peak hours, and
For flat-fee pricing scheme
Consumer Optimization Problems :
( ) ( ) (16)
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Subject to
̅ (17)
̅ (18)
( ) ( ) (19)
or 1 (20)
Optimization Problems of Providers :
( ) ( ) (21)
with ( ) ( )
subject to
̅
̅
( )
For usage-based pricing schemes and two-part tariff
Consumer Optimization Problems:
( ) ( ) ( ) (22)
Subject to
̅ (23)
̅ (24)
( ) ( ) (25)
or 1 (26)
Optimization Problems of Providers:
( ) ( ) (27)
with ( ) ( )
subject to
̅
̅
( )
Steps to get the maximum profit on any pricing scheme used by ISP.
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Case 4. If the ISP using flat-fee pricing scheme by setting , and , where the price used
by the ISP has no effect on the time of peak hours use or off-peak hours, then consumers choose the
maximum consumption ̅, ̅, ̅ and ̅. Thus, any high-end consumer cost no more
than ̅ (̅) ( ̅ ̅) and low-end consumer is not over ̅ (̅) ( ̅ ̅) . Case 4 is a flat-
fee pricing scheme so that P is equivalent for both types of heterogeneous consumers. If it is established
then for the provision of high-end consumer costs will follow the price for the cost of low-end
consumer so ( ) ( )
( )
.
This means that if the consumer is charged at ̅ (̅) ( ̅ ̅) , then only the high-end
consumer who can follow this service. If consumers are charged a fee of ̅ (̅) ( ̅ ̅) , then both
types of consumers can follow this service, namely the consumers of high-end and low-end consumer. To
maximize benefits, ISP charge ̅ (̅) ( ̅ ̅) In this case for Optimization Problems of Providers:
( ) ( ) * ̅ (̅) ( ̅ ̅) + * ̅ (̅) ( ̅ ̅) +
= ( ) , ̅ (̅) ( ̅ ̅) -
Maximum profit yang obtainable produsen is ( ) , ̅ (̅) ( ̅ ̅) -. Based on this,
the lemma was obtained.
Lemma 4: If ISPs use pricing schemes flat-fee, then harga yang dikenakan adalah ̅ (̅) ( ̅ ̅)
with maximum profit obtained amounted ( ) , ̅ (̅) ( ̅ ̅) -
Case 5. If ISPs use pricing schemes usage-based by setting , and then :
Optimization problems for high-end heterogeneous consumers:
( ) – ( ) ( )
To maximize functionality on Consumer Optimization Problems for heterogeneous high-end
consumer, do differentiation agains and :
( ) ̅ (28)
and
( ) ( ) ̅ (29)
Optimization problems for heterogeneous low-end consumer:
Functions in Consumer Optimization Problems:
( ) – ( ) ( )
To maximize functionality on Consumer Optimization Problems for heterogeneous low-end
consumers, do differentiation against and :
( ( ) – ( ) ( ))
, then
( ) ̅ (30)
and
( ) ( ) ̅ (31)
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Optimization Problems of Providers is as follows.
( ) ( ) , ( ̅) (̅)- , ( ̅) (̅ )-
, ̅ ̅ (̅) ( ) ̅ ( )̅ - , ̅ ̅ (̅) ( ) ̅ ( )̅-
If applied to the problem at peak hours, to maximize the function, the ISP must minimize and
hence best price can not be greater than can not be greater than ( ). On the other hand, if the ISP
set prices below ( ), profit is not optimal. If applied to problems in off-peak hours, the best prices
̅ ( ) ( ) On the other hand, if the ISP set prices below ̅ ( ) ( ), then profit is not
optimal when *≤ ̅ and *≤ ̅. Because by the, price is best ( ) ( ) ( ) (
). If the price is this interval, the demand from high-end consumer remains on ̅ and ̅, Thus the optimal
price is given for the rush hour is ( ) and optimal prices in off-peak hours is (̅)
( ) maximum profit is ( ̅ ̅ (̅) ( ) ̅ ( )̅).
Based on this case the lemma was obtained.
Lemma 5: If ISPs use usage-based price, then the optimal price is given for the rush hour is (
) and optimal prices in off-peak hours is (̅) ( ) with maximum profit is
( )( ̅ ̅ (̅) ( ) ̅ ( )̅).
Case 6. If ISPs use pricing schemes two-part tariff, then , , and where there is a cost
incurred if the consumer chooses to join the service and the prices charged during peak hours and off-peak
hours, the first order condition for equality Consumer Optimization Problems of high-end and low-end
consumer. If it is established then it can be assumed that ( ) ( )
( )
.
This means that if the consumer is charged at ( ) and ( ) ( ) and
̅ ( ) ̅ (̅) ( ) ̅ ( )̅ ( ) ( ) then only the high-end consumer who
can follow this service. If consumers are charged a fee of ( ) and (̅) ( ) then
both types of consumers can follow the service, namely the consumers of high-end and low-end consumer.
ISPs may choose to declinekan dibecausekan cost many consumers intokan subscription fees as a barrier so
that it can attract more consumers, ISPs can provide prices ( ) ( ) ( ) and
minimize the cost of subscription.
Optimization Problems Providers into:
( ) ( )
( ), ̅ (̅) ( ) ̅ ( )̅)-
Thus the maximum profit is achieved ( )( ̅ (̅) ( ) ̅ ( )̅)
Based on this case was obtained.
Lemma 6: If the ISP uses a two-part tariff rates, optimal respectively are ( )and
(̅) ( ), and with maximum profit obtainable is( ), ̅ (̅) ( ) ̅ ( )̅-.
If it is assumed that ̅ (̅) (̅), with ̅ and (̅)is non linear function, (̅)= ,
, then ( )( ̅ ̅ (̅)) ( )( ̅ (̅)) Because by then, usage-based pricing
scheme is better than the flat-fee pricing schemes and two-part tariff for heterogeneous high-end and low-end
consumer issues.
Using similar proof for next three lemma, these results are achieved.
Lemma 7: If ISPs use pricing schemes flat-fee, then ̅ (̅ ) ( ̅ ̅ ) with maximum profit is
achieved( ), ̅ (̅ ) ( ̅ ̅ ) -
Lemma 8:If ISPs use pricing schemes usage-based, then optimal price at peak hours is ( )
and optimal prices in off-peak hours is ( ̅ ) ( ) with maximum profit is
( ) , ̅ ̅ (̅ ) ( ) ̅ ( )̅ -
Lemma 9:If the ISP uses a two-part tariff rates, then ( ) ( ̅ ) ( ),
(̅ ) (̅ )̅ , P is subscription fee equal to consumer surplus from consumers evels are low, with
maximum profit , ̅ (̅ )(̅ ̅ ) (̅ ) - , ̅ (̅ ) ( ) ̅ ( )̅ - greater
than the price of flat-fee or usage-based.
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, , ̅ (̅ )(̅ ̅ ) (̅ ) - , ̅ (̅ ) ( ) ̅ (
)̅ - ( ), ̅ ̅ ( ̅ ) ( ) ̅ ( ) ̅ - ( ), ̅ (̅ ) ( ̅ ̅ ) -
means that a two part tariff scheme is more optimal than usage-based pricing schemes and flat-fee for
heterogeneous consumer based on high-demand and low-demand.
Based on case 1- case 9 analysis for quasi linear utility function, the comparison among utility
functions that have already discussed in previous research conducted by [9], [15], [16] can be performed.
Table 1-3 are the recapitulation of pricing scheme for each type of consumer in mail data traffic by
comparing with previous research. In the perfect substitute utility function, in the case of homogeneous and
heterogeneous consumers (high-end and low-end) the optimal pricing scheme is contained in the flat-fee
pricing schemes, and for heterogeneous (high-demand and low-demand) consumers, usage- based and two-
part tariff pricing schemes are the most optimal. In the quasi linear utility function, in the case of consumers
homogeneous and consumer heterogeneous (high-end and low-end) pricing schemes optimally located on the
usage-based pricing scheme, and for consumers heterogeneous (high-demand and low-demand) pricing
scheme the most optimal is a two-part tariff scheme.
Table 1. Homogeneous Consumer for Three Utility Functions in Mail Traffic Data
Scheme Perfect Substitute[16] Quasi linear Cobb-Douglas[15]
Flat-fee
∑(
)
∑, ( )
-
∑,
(
) -
Usage-based
∑(
( ))
∑,
( ) (
)-
∑ *( ), -
( )
( ) +
Two-part tariff
∑,
( )
( )-
∑ , (
) ( ) -
Table 2. Heterogeneous Consumer High-End and Low-End) for Three Utility Functions in Mail Traffic Data
Scheme Perfect Substitute[16] Quasi linear Cobb-Douglas[15]
Flat-fee
( )(
( ) )
( )(
( ) )
( ) ,
( ) -
Usage-based (
)(
( )
( ))
( ) .
( )
( )/
(
)[(
)(
( )
( ) )]
Two-part tariff
( ) .
( ) ( )/
( ) ,
( ) -
Table 3. Heterogeneous Consumer High-Demand and Low-Demand for Three Utility Functions in Mail
Traffic Data
Scheme Perfect Substitute[16] Quasi linear Cobb-Douglas[15]
Flat-fee
( )(
)
( ),
( ) -
( )(
(
) )
Usage-based
(
( ))
(
( ))
( ) , ( )
(
) -
( ),( )( )
( ) ( ) -
Two-part
tariff
,
( ) ( )
( ) - ,
( ) ( ) -
,( )(
)
( )(
) ( )(
)- ,( )( )-
( ) ( )
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Based on the interpretations of Table 1 to Table 3, by applying data obtained from local server, the
original Cobb-Douglas utility function [15] is more optimal than the perfect substitute discussed previously
[16] and quasi linear utility function, based on the type of pricing scheme usage-based, which is used to
obtain the maximum profit with optimal price for homogeneous and heterogeneous (high-end and low-end)
consumers; for heterogeneous consumers (high-demand and low-demand), the type of two-part tariff pricing
scheme obtain maximum profit with optimal price.
The fact of these results is due to the general form of Cobb-Douglas function is in exponential form.
It is nonlinear function, in nature. If quasi linear or perfect substitues functions which are linear or half linear
functions are compared, this graph of Cobb-Douglas function has many possible local optimal in certain
range. Also, comparing to previous research conducted [9] that stated for homogenous case, usage based
reached highest optimal solution, the original Cobb-Douglas has also the same results. But the original Cobb-
Douglas is still powerfull with the highest optimal solution compared to modified Cobb-Douglas function.
This is again, due to the fact that exponential form of function has more peaks on certain range of local
optimal solutions. Other aspect that can be showed that, also in previous research [9], [17] marginal and
monitoring cost for heterogeneous consumers, are not really discussed, the research mostly focused on
pricing strategies for information service without marginal and monitoring cost.
4. CONCLUSION
From the results, it can be concluded that by applying Cobb-Douglas utility function, will impact on
higher revenue for ISP on usage based pricing strategies for homogeneous and heterogeneous consumers for
high end and low end users. Two part tariff pricing strategy is best strategy to be applied in heterogeneous
case of high and low demand users.
ACKNOWLEDGEMENTS
The research leading to this study was financially supported by Directorate of Higher
Education Indonesia (DIKTI) for support through “Hibah Fundamental Tahun I”, 2016.
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BIOGRAPHIES OF AUTHORS
Robinson Sitepu received his Bachelor of Science in Statistics from Padjajaran University,
Western Java, Indonesia. Then he received his M.Si in Mathematics from University of North
Sumatera. He has been a Mathematics Department member at Faculty mathematics and Natural
Sciences Sriwijaya University South Sumatera Indonesia since 1985. His research interests
include operation research and its applications and statistics.
Fitri Maya Puspita received her S.Si degree in Mathematics from Sriwijaya University, South
Sumatera, Indonesia in 1997. Then she received her M.Sc in Mathematics from Curtin
University of Technology (CUT) Western Australia in 2004. She reveived his Ph.D in Science
and Technology in 2015 from Universiti Sains Islam Malaysia. She has been a Mathematics
Department member at Faculty mathematics and Natural Sciences Sriwijaya University South
Sumatera Indonesia since 1998. Her research interests include optimization and its applications
such as vehicle routing problems and QoS pricing and charging in third generation internet.
Anggi Nurul Pratiwi currently is an undergraduate student at Mathematics Department, Faculty
of Mathematics and Natural Sciences, Sriwijaya University. She is currently on final stage of her
thesis submission. Her topic interest includes Optimization and its application on pricing of
information service with marginal dan monitoring cost.
Icha Puspita Novyasti currently is an undergraduate student at Mathematics Department,
Faculty of Mathematics and Natural Sciences, Sriwijaya University. She is currently on final
stage of her thesis submission. Her topic interest includes Optimization and its application on
pricing of information service with marginal dan monitoring cost.