This document compares the performance of 8 optimization algorithms (SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam, Ftrl) when training a ResNet convolutional neural network on an autonomous driving dataset with 11 categories of vehicle locations. Preliminary results found SGD performed best while Ftrl performed worst, though more analysis is needed to determine the optimal algorithm. The network was trained on 20 epochs with images from the PandaSet database to classify vehicle position using features extracted from front camera images.
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Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Effective Multi-Stage Training Model for Edge Computing Devices in Intrusion ...IJCNCJournal
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)Eswar Publications
Recently machine learning has been introduced into the area of adaptive video streaming. This paper explores a novel taxonomy that includes six state of the art techniques of machine learning that have been applied to Dynamic Adaptive Streaming over HTTP (DASH): (1) Q-learning, (2) Reinforcement learning, (3) Regression, (4) Classification, (5) Decision Tree learning, and (6) Neural networks.
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Autonomous vehicle is one the prominent area of research in computer
vision. In today’s AI world, the concept of autonomous vehicles has become
popular largely to avoid accidents due to negligence of driver. Perceiving the
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autonomous vehicles. Sensors like light detection and ranging can be used
for depth estimation but these sensors are expensive. Hence stereo matching
is an alternate solution to estimate the depth. The main difficulties observed
in stereo matching is to minimize mismatches in the ill-posed regions, like
occluded, texture less and discontinuous regions. This paper presents an
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stereo images in ill-posed regions. The images from Middlebury stereo data
set are used to assess the efficacy of the model proposed. The experimental
outcome dipicts that the proposed model generates reliable results in the
occluded, texture less and discontinuous regions as compared to the existing
techniques.
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The wide usage of the Internet and the availability of powerful computers and high-speed networks as low cost
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Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Effective Multi-Stage Training Model for Edge Computing Devices in Intrusion ...IJCNCJournal
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Effective Multi-Stage Training Model for Edge Computing Devices in Intrusion ...IJCNCJournal
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)Eswar Publications
Recently machine learning has been introduced into the area of adaptive video streaming. This paper explores a novel taxonomy that includes six state of the art techniques of machine learning that have been applied to Dynamic Adaptive Streaming over HTTP (DASH): (1) Q-learning, (2) Reinforcement learning, (3) Regression, (4) Classification, (5) Decision Tree learning, and (6) Neural networks.
A deep learning based stereo matching model for autonomous vehicleIAESIJAI
Autonomous vehicle is one the prominent area of research in computer
vision. In today’s AI world, the concept of autonomous vehicles has become
popular largely to avoid accidents due to negligence of driver. Perceiving the
depth of the surrounding region accurately is a challenging task in
autonomous vehicles. Sensors like light detection and ranging can be used
for depth estimation but these sensors are expensive. Hence stereo matching
is an alternate solution to estimate the depth. The main difficulties observed
in stereo matching is to minimize mismatches in the ill-posed regions, like
occluded, texture less and discontinuous regions. This paper presents an
efficient deep stereo matching technique for estimating disparity map from
stereo images in ill-posed regions. The images from Middlebury stereo data
set are used to assess the efficacy of the model proposed. The experimental
outcome dipicts that the proposed model generates reliable results in the
occluded, texture less and discontinuous regions as compared to the existing
techniques.
AN ENTROPIC OPTIMIZATION TECHNIQUE IN HETEROGENEOUS GRID COMPUTING USING BION...ijcsit
The wide usage of the Internet and the availability of powerful computers and high-speed networks as low cost
commodity components have a deep impact on the way we use computers today, in such a way that
these technologies facilitated the usage of multi-owner and geographically distributed resources to address
large-scale problems in many areas such as science, engineering, and commerce. The new paradigm of
Grid computing has evolved from these researches on these topics. Performance and utilization of the grid
depends on a complex and excessively dynamic procedure of optimally balancing the load among the
available nodes. In this paper, we suggest a novel two-dimensional figure of merit that depict the network
effects on load balance and fault tolerance estimation to improve the performance of the network
utilizations. The enhancement of fault tolerance is obtained by adaptively decrease replication time and
message cost. On the other hand, load balance is improved by adaptively decrease mean job response time.
Finally, analysis of Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization is
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computational grid. Consequently, a significant system utilization improvement was attained. Experimental
results eventually demonstrate that the proposed method's performance surpasses other methods.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
Job Scheduling on the Grid Environment using Max-Min Firefly AlgorithmEditor IJCATR
Grid computing indeed is the next generation of distributed systems and its goals is creating a powerful virtual, great, and
autonomous computer that is created using countless Heterogeneous resource with the purpose of sharing resources. Scheduling is one
of the main steps to exploit the capabilities of emerging computing systems such as the grid. Scheduling of the jobs in computational
grids due to Heterogeneous resources is known as an NP-Complete problem. Grid resources belong to different management domains
and each applies different management policies. Since the nature of the grid is Heterogeneous and dynamic, techniques used in
traditional systems cannot be applied to grid scheduling, therefore new methods must be found. This paper proposes a new algorithm
which combines the firefly algorithm with the Max-Min algorithm for scheduling of jobs on the grid. The firefly algorithm is a new
technique based on the swarm behavior that is inspired by social behavior of fireflies in nature. Fireflies move in the search space of
problem to find the optimal or near-optimal solutions. Minimization of the makespan and flowtime of completing jobs simultaneously
are the goals of this paper. Experiments and simulation results show that the proposed method has a better efficiency than other
compared algorithms.
Noise Removal in Traffic Sign Detection SystemsCSEIJJournal
The application of Traffic sign detection and recognition is growing in traffic assistant driving systems and
automatic driving systems. It helps drivers and automatic driving systems to detect and recognize the
traffic signs effectively. However, it is found that it may be difficult for these systems to work in challenging
environments like rain, haze, hue, etc. To help the detection systems to have better performance in
challenging conditions like rain and haze, we propose the use of a deep learning technique based on a
Convolutional Neural Network to process visual data. The processed data could be used in the detection.
We are using the NoiseNet model [11], a noise reduction network for our architecture. The model is
trained to enhance images in patches instead of as a whole. The training is done using the Challenging
Unreal and Real Environment - Traffic Sign Detection Dataset(CURE-TSD) which contains videos of
different roads in various challenging situations. The
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling. In this paper, a combination of imperialist competition algorithm (ICA) and gravitational attraction is used for to address the
problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results
compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it
converges quickly, finding its optimum solution in less time than the other algorithms.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
A CLOUD BASED ARCHITECTURE FOR WORKING ON BIG DATA WITH WORKFLOW MANAGEMENTIJwest
In real environment there is a collection of many noisy and vague data, called Big Data. On the other hand,
to work on the data middleware have been developed and is now very widely used. The challenge of
working on Big Data is its processing and management. Here, integrated management system is required
to provide a solution for integrating data from multiple sensors and maximize the target success. This is in
situation that the system has constant time constrains for processing, and real-time decision-making
processes. A reliable data fusion model must meet this requirement and steadily let the user monitor data
stream. With widespread using of workflow interfaces, this requirement can be addressed. But, the work
with Big Data is also challenging. We provide a multi-agent cloud-based architecture for a higher vision to
solve this problem. This architecture provides the ability to Big Data Fusion using a workflow management
interface. The proposed system is capable of self-repair in the presence of risks and its risk is low.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the roposed algorithm, in addition to previous parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm will be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.
GRADIENT OMISSIVE DESCENT IS A MINIMIZATION ALGORITHMijscai
This article presents a promising new gradient-based backpropagation algorithm for multi-layer
feedforward networks. The method requires no manual selection of global hyperparameters and is capable
of dynamic local adaptations using only first-order information at a low computational cost. Its semistochastic nature makes it fit for mini-batch training and robust to different architecture choices and data
distributions. Experimental evidence shows that the proposed algorithm improves training in terms of both
convergence rate and speed as compared with other well known techniques.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
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.
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IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
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situation that the system has constant time constrains for processing, and real-time decision-making
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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convergence rate and speed as compared with other well known techniques.
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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%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
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Comparative study of optimization algorithms on convolutional network for autonomous driving
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 6, December 2022, pp. 6363∼6372
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp6363-6372 ❒ 6363
Comparative study of optimization algorithms on
convolutional network for autonomous driving
Fernando Martı́nez, Holman Montiel, Fredy Martı́nez
Facultad Tecnológica, Universidad Distrital Francisco José de Caldas, Bogotá D.C, Colombia
Article Info
Article history:
Received Oct 6, 2021
Revised Jun 4, 2022
Accepted Jul 1, 2022
Keywords:
Autonomous driving
Function optimization
Intelligent sensor
Path planning
Recall
ABSTRACT
The last 10 years have been the decade of autonomous vehicles. Advances in intel-
ligent sensors and control schemes have shown the possibility of real applications.
Deep learning, and in particular convolutional networks have become a fundamental
tool in the solution of problems related to environment identification, path planning,
vehicle behavior, and motion control. In this paper, we perform a comparative study of
the most used optimization strategies on the convolutional architecture residual neu-
ral network (ResNet) for an autonomous driving problem as a previous step to the
development of an intelligent sensor. This sensor, part of our research in reactive
systems for autonomous vehicles, aims to become a system for direct mapping of sen-
sory information to control actions from real-time images of the environment. The
optimization techniques analyzed include stochastic gradient descent (SGD), adap-
tive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propaga-
tion (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated
adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The
training of the deep model is evaluated in terms of convergence, accuracy, recall, and
F1-score metrics. Preliminary results show a better performance of the deep network
when using the SGD function as an optimizer, while the Ftrl function presents the
poorest performances.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Fredy Martı́nez
Facultad Tecnológica, Universidad Distrital Francisco José de Caldas
Carrera 7 No 40B-53, Bogotá D.C., Colombia
Email: fhmartinezs@udistrital.edu.co
1. INTRODUCTION
Robotics, control, intelligent systems, and artificial vision systems have been developing at a dizzying
pace in recent years thanks to the development of deep learning models [1]–[3] These developments have been
integrated into the automotive industry, and in particular, have led to a breakthrough in autonomous driving
systems [4]–[6]. This type of technology offers human benefits such as a reduction in traffic accidents and
congestion in large cities, and therefore an improvement in the mobility and quality of life of citizens [7], [8].
Linked to these advances are also environmental benefits, as autonomous systems have better management of
energy resources [9], [10]. There are five levels of autonomous driving defined according to the SAE J3016
standard [11], with the lowest levels considered basic driver assistance and the highest levels aligned with
autonomous systems that do not require human intervention. An important feature of all these vehicles, regard-
less of their level, is the degree of development of their sensors and intelligent control capabilities [12]. Basic
navigation schemes can be solved from classical motion control schemes supported by distance sensors [13].
However, an autonomous driving system is confronted with a larger number of much more complex situations
Journal homepage: http://ijece.iaescore.com
2. 6364 ❒ ISSN: 2088-8708
in which the well-being of the human being must also be ensured [14]. These are systems that require com-
plex real-time decision-making schemes, which process a large amount of information from different types of
sensors including cameras, radar, light detection and ranging or laser imaging detection and ranging (LiDAR),
ultrasound, and global positioning system (GPS), among others [15]. The information captured by the sensors
must be processed quickly, so the rapid extraction of features of interest among a large amount of data is a
priority, this is the principle of intelligent sensors, which pre-process the information before sending it to the
central control unit [16]. This is where deep network-based models are most applicable, these can be embedded
in an electronic system to form, with the appropriate instrumentation, an intelligent system [17].
Deep neural networks, and of these in particular convolutional neural networks (ConvNet), are the
most advanced learning-based architectures for applications of categorizing input parameters from large num-
bers of learning samples [18], [19]. Training these architectures is always a challenging problem as it involves
high-dimensional optimization [20]. The goal of the designer is to tune the hyperparameters of the model by
minimizing the loss function (error between the model response and the actual category to which the input data
belongs). This process involves the use of an optimization function, which must be selected and tuned to pro-
duce a fast fit with low memory costs and no over- or under-fitting [21]. Other problems with optimizer tuning
lie in avoiding the local minima of the loss function, as well as the curse of dimensionality [22]. As a solution,
different optimizers have been proposed in recent years, generally based on adaptive estimation, which can be
adjusted to a large number of problems. The final choice between one optimizer over another will depend on
the characteristics of the database used to train the model [23].
Many researchers and developers of deep neural models tend to select a priori the adaptive moment
estimation (Adam) optimizer as the optimizer for most cases due to its high performance, particularly when
working with sparse databases. However, it has been shown that Adam (as well as its variants) can become
unstable under certain conditions, producing models with lower generalization capability, so it is not a gener-
alized solution for every optimization problem [24]. The final selection of an optimizer for a given problem
requires an analysis of the performance of all possible options according to the neural model to be trained and
the characteristics of the dataset to be identified. This research is a work in this direction in the search for an
intelligent sensor system for autonomous driving applications. The residual neural network (ResNet) type neu-
ral network has been selected for the model due to its high performance and reduced architecture compared to
structures such as dense convolutional network (DenseNet) and neural architecture search network (NASNet)
[25].
2. PROBLEM FORMULATION
Gradient descent is the most widely used optimization method in machine learning algorithms and
neural networks [26]. In its simplest form, this algorithm seeks to minimize an objective function called J(θ)
according to the θ parameters of a model. Where θ ∈ Rd
, by updating the value of these parameters in the
opposite direction of the gradient of the objective function, i.e.:
∇θJ (θ) (1)
The rate of change of the model in response to the error at each update step is controlled by a parameter
called the learning rate. In this way, the algorithm moves following the direction of the slope of the error surface
(loss function) created by the objective function downhill until it reaches a valley. The accuracy and update time
of the algorithm depend inversely on the amount of data in each update. This is a parameter that differentiates
the variants of gradient descent.
In the traditional gradient descent scheme (also known as batch gradient descent or vanilla gradient
descent) the gradient is calculated as the average gradient for the entire training data set, i.e., the gradient is
calculated as the mean of the gradient for the entire training data set:
∇f =
1
n
X
i
∇loss (xi) (2)
because the gradients of the entire data set are computed to perform each update, this scheme tends to be very
slow and impossible to use for large data sets in memory-constrained systems. This algorithm converges to the
global minimum for convex error surfaces and a local minimum for non-convex surfaces.
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In the case of stochastic gradient gescent (SGD), this optimizer performs the parameter update for a
sample of the data set. For example, with a batch size of one, the gradient can be estimated as (3).
∇f ≈ ∇loss (x∗
) (3)
Where x∗
is a random sample of the entire data set. This is therefore a variant of gradient descent that reduces
the amount of data in favor of the update time. It should be noted that SGD also has many variants, such as
SGD with momentum.
Gradient descent performs redundant calculations for large data sets. In these data sets, there are often
similar samples, so the algorithm spends time on redundant computations. SGD eliminates this redundancy
by performing a single calculation per update. The algorithm is usually faster, and in some cases can be
used for online learning. It has been shown that when the learning rate is reduced the SGD shows the same
convergence behavior as gradient descent. However, like gradient descent, SGD often has difficulty escaping
saddle points, in these cases optimizers such as adaptive gradient (Adagrad), adaptive learning rate (Adadelta),
root mean square propagation (RMSProp), and Adam generally perform much better. Adagrad is a gradient-
based optimization algorithm that adapts the learning rate to the parameters in coherence with the information
contained in them [27]. It tries to make smaller updates (i.e., low learning rates) for parameters associated
with frequently occurring features (parameters with similar information), and larger updates (i.e., high learning
rates) for parameters associated with infrequent or sparse features, so in problems where little data contains
critical information the Adagrad optimizer turns out to be of great importance [28].
Since Adagrad uses different learning rates for each parameter, then one can define the gradient at
time step t for the parameter θi as (4).
∇ft,i = ∇loss (θt,i) (4)
Adagrad calculates the learning rate at each time step t for each parameter according to the gradient passed for
the same parameter. Adadelta is an optimizer that modifies Adagrad in terms of the behavior of this dynamic
learning rate. Adadelta reduces the way this learning rate decreases monotonically without accumulating the
past values of the gradient (it does not store past gradients) but limits it to a fixed value [29]. The RMSprop
optimizer was developed with the same objective but with a different decay rate. Adadelta and RMSProp work
almost similarly, with the only difference being that in Adadelta no initial learning constant is required.
Adam is one of the most recent optimizers, it was proposed in 2014, and like the previous cases, it
is an algorithm that computes adaptive learning rates for each parameter [30]. Adam controls the decay rate
similarly to Adadelta and RMSprop, but also maintains an exponentially decreasing average of past gradients
similar to momentum. In simplified form, Adam can be described as behaving like the RMSprop optimizer
with momentum, while nesterov-accelerated adaptive moment estimation (Nadam) is Adam as RMSprop with
Nesterov momentum (Nesterov accelerated gradient). Adam has set a landmark in terms of deep learning
optimizers, to the point that many researchers recommend it as a de facto solution, it combines the best features
of Adadelta and RMSprop and therefore tends to perform better in most cases. However, the Adam optimizer
can sometimes be unstable and is not easy to control, which is why in these cases it is suggested to use SGD
with some method of learning rate update.
Adam’s update rule includes the gradient of the current cycle to the power defined by the Kingma
norm. High values of this norm tend to generate unstable results, so in 2015 the same authors of Adam proposed
AdaMax, an optimizer that generates more stable convergences [30]. This is because the term corresponding
to the current cycle gradient is essentially ignored when it is small. Thus, parameter updates are influenced by
fewer gradients and are therefore less susceptible to gradient noise. In practical terms, it can be argued that
AdaMax should perform better than Adam when the dataset contains sparse parameter updates, i.e., embedded
information. However, in practice, this is not always true.
There is no defined strategy for the selection of a particular optimizer when approaching a deep learn-
ing model training problem. A low initial learning rate with Adam and its variants does not guarantee algorithm
stability in all cases, and many solutions end up opting for classical structures such as SGD and Adadelta. Con-
sequently, the selection of one optimizer over another should be made as a specific case according to the
performance of each algorithm on the particular problem, with consideration of the degree of dispersion of the
dataset. This research seeks to evaluate the best optimizer for the future design of an intelligent sensor capable
of correctly and incorrectly identifying the position of a vehicle in the lane of the road (autonomous vehicle
prototype).
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Figure 2. Sample of the images within the 11 categories
Table 1. Summary of the ResNet architecture used in the tests
Input Layer: 256×256×3
conv1 (7×7)
Batch Normalization
ReLu
Max Pooling (3×3)
conv2 Block × 3
conv3 Block × 4
conv4 Block × 6
conv4 Block × 3
Average Pooling: 2,048
Softmax: 11
Total params: 23,610,251
Trainable params: 23,557,131
Non-trainable params: 53,120
The training in each case is performed with 80% of the images, and the remaining 20% is used for
validation. The images are always randomly mixed with different seeds for each model. During training,
the loss function, accuracy, and mean squared error (MSE) are calculated at each epoch for both training and
validation images. These metrics will be used to evaluate the performance of each optimizer. Precision, Recall,
and F1-score metrics are also calculated for the validation data, parameters that are also considered in the
comparison.
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4. RESULTS AND DISCUSSION
The version of SGD we used is known as Vanilla. The optimization used the basic algorithm with a
constant learning rate of 0.01, a momentum of zero, and without applying Nesterov momentum. Something
similar was done with the other optimizers, in none of them momentum was used, and in all of them, the same
learning rate of 0.01 was used. In all cases this value can be adjusted throughout the training, however, it was
decided to perform a basic configuration of all the optimizers to compare their capacity and performance with
the dataset used. For the same reason, all training was performed for only 20 epochs.
For each of the final models, Precision, Recall, and F1-Score metrics were calculated for the validation
images as shown in Table 2. These data allow us to assess the final model performance with unknown data,
but do not reflect the behavior of each optimizer throughout the training. To assess this aspect, during the
training of the models, the loss function and accuracy were calculated for each epoch for both training and
validation data, this information is shown in Figure 3. Figure 3(a) shows the behavior throughout training for
the SGD optimizer, Figure 3(b) the corresponding one for the RMSprop function, Figure 3(c) for the Adagrad
function, Figure 3(d) for the Adadelta function, Figure 3(e) for the Adam function, Figure 3(f) for the Adamax
function, Figure 3(g) for the Nadam function, and Figure 3(h) for the Ftrl function. The objective is to observe
the behavior of each optimizer throughout the training, and its ability to reduce the error of the validation data
compared to its final behavior.
For the training process, Adagrad outperformed the other optimizers for the dataset of interest, fol-
lowed closely by SGD. Adagrad obtained the lowest loss with the training data (less than 0.2%), and the
second-lowest loss for the validation data (less than 5%), and its Accuracy for both training and validation
data was almost perfect (close to 100%). This behavior was closely followed by SGD, which obtained a loss
with training data below 1%, with validation data below 3%, and also an almost perfect Accuracy. Among the
other optimizers, behind Adagrad and SGD, it is worth mentioning Adamax with Accuracy higher than 97%
for both training and validation, and with low loss in the two groups of images (around 10%), and Adam with
a little more than 95% Accuracy in the training data and 61% in the validation data, maintaining a low loss for
the training data (less than 20%). Optimizers such as Adadelta, RMSprop, and Nadam performed well on the
training data (Accuracy in the order of 90% and low loss), particularly Adadelta (loss less than 3%), but their
performance on the validation data was very poor (about 30% Accuracy for Adadelta and RMSprop, and just
below 50% for Nadam). Finally, the worst performance with this dataset was obtained with the Ftrl optimizer,
which failed to reduce its loss with training data and much less with validation data.
The Precision, Recall, and F1-Score metrics show similar behavior to that detected throughout the
training. Again the best performance for the whole dataset (in this case considering only images from the
validation group) was for the Adagrad and SGD optimizers. In both cases, these optimizers managed to cor-
rectly categorize most of the unknown images to the models. Precision provides a measurement value related
to the accuracy of the model, with the number of images placed in a category that belongs to it relative to the
total number of images placed within that category. On the other hand, Recall weights the sensitivity of the
classification model by giving a value indicating how many of the items classified in the category belong to it.
F1-Score is a weighted value of these two metrics, so it considers both false positives and false negatives in the
performance assessment. Adagrad and SGD achieve 99% on all three metrics. Behind, but very close to these
two optimizers is Adamax, which achieves 98% on all three metrics. Following but far behind is Adamax with
75% in Precision, 61% in Recall, and 61% in F1-Score. The other optimizers show very poor values in these
metrics (below 50%), again with FTRL in the lowest place.
Table 2. Comparative results of the eight models. The worst performing metrics are highlighted in red, the
best performing metrics are highlighted in green
Optimizer Training Validation Training Validation Validation Validation Validation
Loss Loss Accuracy Accuracy Precission Recall F1-Score
SGD 0.0067 0.0249 1.0000 0.9909 0.99 0.99 0.99
RMSprop 0.2927 45.2431 0.8967 0.3091 0.41 0.31 0.24
Adagrad 0.0017 0.0474 1.0000 0.9909 0.99 0.99 0.99
Adadelta 0.0295 6.2389 1.0000 0.3000 0.14 0.30 0.19
Adam 0.1615 2.3984 0.9535 0.6136 0.75 0.61 0.61
Adamax 0.1000 0.1063 0.9715 0.9773 0.98 0.98 0.98
Nadam 0.5207 4.7732 0.8493 0.4818 0.43 0.48 0.40
Ftrl 2.3979 2.3990 0.1016 0.0500 0.00 0.05 0.00
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(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 3. Behavior of the loss function and the Accuracy of the optimizer throughout the training for training
and validation data (a) SGD, (b) RMSprop, (c) Adagrad, (d) Adadelta, (e) Adam, (f) Adamax, (g) Nadam, and
(h) Ftrl
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Figure 3 also shows that the best performance throughout the training was shown by the optimizers
Adagrad, SGD, and Adamax. Unlike the latter, the other optimizers fail to reduce the loss for the validation
data. The curves corresponding to RMSprop, Adam, and Nadam do not show much detail in this respect due
to the scale, but Table 2 does show the poor final performance at these values. To reach a conclusion regarding
the best optimizer, we identify which of the three chosen optimizers reduces the loss the fastest, which is why
Figure 4 shows a detail of this behavior during the final stage of the training. Here it is observed that Adamax,
despite not having a smooth decreasing behavior, manages to reduce the loss value faster than Adagrad and
SGD. It is also observed that SGD has the best decreasing trend.
Figure 4. Detail of the behavior of the loss function of the validation data in the last training epochs for the
best performing optimizers: Adagrad, SGD, and Adamax
5. CONCLUSION
This paper shows a performance comparison of eight optimization algorithms used in the fitting of
a convolutional model for image classification in tasks related to autonomous driving. The research has the
purpose of identifying high-performance structures for the development of intelligent sensors for autonomous
driving systems, which is why different parameters of the models are evaluated, in particular, the capability of
different optimizers on deep topologies in image classification tasks related to the autonomous identification
of the environment by a vehicle. We use the open-source database PandaSet by Hesai and Scale AI to build a
custom dataset with 1,100 images uniformly distributed in 11 categories. These categories correspond to typical
locations of a car on a public road, in our case on California roads. A ResNet with 50 layers of depth was used
as a deep network, and categorical cross-entropy was used as a loss function in all cases, training each model
over 20 epochs. Our study showed that the performance of each optimizer is affected by the characteristics of
our dataset. Recent schemes that were rated as excellent performers such as Adam were strongly outperformed
by classics such as SGD. From the results, Adagrad, SGD, and Adamax outperformed the other optimizers in
performance, particularly when evaluating their behavior with images not used in the training. It should be
noted that no convergence acceleration strategy such as momentum and nesterov acceleration gradient (NAG)
was used to identify the structure with the highest improvement capability. In principle, the SGD optimizer is
selected as the first option for the development of an automatic embedded system for scenario identification, but
first, the three models will undergo fine-tuning with a larger dataset, activities that are planned as a continuation
of the research.
ACKNOWLEDGEMENT
This work was supported by the Universidad Distrital Francisco José de Caldas, in part through CIDC,
and partly by the Facultad Tecnológica. The views expressed in this paper are not necessarily endorsed by Uni-
versidad Distrital. The authors thank the research group ARMOS for the evaluation carried out on prototypes
of ideas and strategies.
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BIOGRAPHIES OF AUTHORS
Fernando Martı́nez is a doctoral researcher at the Universidad Distrital Francisco José
de Caldas focusing on the development of navigation strategies for autonomous vehicles using hi-
erarchical control schemes. In 2009 he completed his M.Sc. degree in Computer and Electronics
Engineering at Universidad de Los Andes, Colombia. He is a researcher of the ARMOS research
group (Modern Architectures for Power Systems) supporting the lines of electronic instrumentation,
control and robotics. He can be contacted at email: fmartinezs@udistrital.edu.co.
Holman Montiel is a professor of algorithms, embedded systems, instrumentation,
telecommunications, and computer security at the Universidad Distrital Francisco José de Caldas
(Colombia) and a researcher in the ARMOS research group (Modern Architectures for Power Sys-
tems). His research interests are encryption schemes, embedded systems, electronic instrumentation,
and telecommunications. Montiel holds a master’s degree in computer security. He can be contacted
at email: hmontiela@udistrital.edu.co.
Fredy Martı́nez is a professor of control, intelligent systems, and robotics at the Universi-
dad Distrital Francisco José de Caldas (Colombia) and director of the ARMOS research group (Mod-
ern Architectures for Power Systems). His research interests are control schemes for autonomous
robots, mathematical modeling, electronic instrumentation, pattern recognition, and multi-agent sys-
tems. Martinez holds a Ph.D. in Computer and Systems Engineering from the Universidad Nacional
de Colombia. He can be contacted at email: fhmartinezs@udistrital.edu.co.
Int J Elec & Comp Eng, Vol. 12, No. 6, December 2022: 6363–6372