This document proposes a decentralized supervision system using Petri nets to control mobile sensor networks involving human operators. Mobile sensor networks are used for tasks like charging static sensors, repairing/replacing sensors, and investigating sensor alerts. The proposed system uses a decentralized approach with local supervisors at each robot/agent to filter human-issued commands and prevent undesirable or unsafe executions. As an example, the document models a system with 5 robots and 8 rooms using individual Petri net models for each robot. The overall system model is the composition of these individual models with added control places to enforce capacity constraints on rooms. The decentralized approach allows modeling more complex environments without a single centralized model.
DECENTRALIZED SUPERVISION OF MOBILE SENSOR NETWORKS USING PETRI NETijcseit
In semiautonomous mobile sensor networks, since human operators may be involved in the control loop,particular improper actions may cause accidents and result in catastrophes. For such systems, this paper proposes a decentralized supervisory control system to accept or reject the human-issued commands so that undesirable executions never be performed. In the present approach, Petri nets are used to model the operated behaviors and to synthesize the decentralized supervisory system. The presented technique could be applied to large-scale and complicated wireless mobile sensor networks.
RELIABILITY ASSESSMENT OF EMBEDDED SYSTEMS USING STOPWATCH PETRI NETSIJCSEA Journal
In this paper, we propose a reliability approach in which feared events define reliability requirements and
taking them into account allows to design systems which will be able to avoid the drift towards a feared
state. The description of feared scenarios since the system design phase enables us to understand the
reasons of the feared behavior in order to envisage the necessary reconfigurations and choose safe
architectures. In order to face the increasing complexity of embedded systems and to represent the
suspension and resumption of task execution we propose to extract directly feared scenarios from
Stopwatch Petri net model avoiding the generation of the associated reachability graph and the eternal
combinative explosion problem.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
COMPARING THE IMPACT OF MOBILE NODES ARRIVAL PATTERNS IN MANETS USING POISSON...ijwmn
This paper compares the impact of mobile node arrival patterns in mobile ad hoc networks (MANETs) using Poisson and Pareto distribution models. Through MATLAB simulations, the study investigates how the arrival rate and size of the mobile node population are affected by these distributions. The results indicate that higher arrival rates can influence larger mobile node populations in a given area. Additionally, the Pareto distribution is found to better model mobile node mobility in MANETs compared to the Poisson distribution.
Activity Recognition From IR Images Using Fuzzy Clustering TechniquesIJTET Journal
Infrared sensors ensures that activity recognition is possible in the day and night times. It is used especially for activity monitoring of older adults as falls are more prevalent at night than the day. This paper focus on an application of fuzzy set techniques and it is capable of accurately detecting several different activity states related to fall detection and fall risk assessment and it also includes sitting, standing and being on the floor to ensure that elderly residents gets the help they need quickly in case of emergencies. Fall detection and fall risk assessment is used for an aging in place facility for the elderly people. It describes the silhouette extraction process, the image features , and the fuzzy clustering technique.
VARIATIONAL MONTE-CARLO APPROACH FOR ARTICULATED OBJECT TRACKINGcsandit
In this paper, we describe a novel variational Monte Carlo approach for modeling and tracking
body parts of articulated objects. An articulated object (human target) is represented as a
dynamic Markov network of the different constituent parts. The proposed approach combines
local information of individual body parts and other spatial constraints influenced by
neighboring parts. The movement of the relative parts of the articulated body is modeled with
local information of displacements from the Markov network and the global information from
other neighboring parts. We explore the effect of certain model parameters (including the
number of parts tracked; number of Monte-Carlo cycles, etc.) on system accuracy and show that
ourvariational Monte Carlo approach achieves better efficiency and effectiveness compared to
other methods on a number of real-time video datasets containing single targets.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
DECENTRALIZED SUPERVISION OF MOBILE SENSOR NETWORKS USING PETRI NETijcseit
In semiautonomous mobile sensor networks, since human operators may be involved in the control loop,particular improper actions may cause accidents and result in catastrophes. For such systems, this paper proposes a decentralized supervisory control system to accept or reject the human-issued commands so that undesirable executions never be performed. In the present approach, Petri nets are used to model the operated behaviors and to synthesize the decentralized supervisory system. The presented technique could be applied to large-scale and complicated wireless mobile sensor networks.
RELIABILITY ASSESSMENT OF EMBEDDED SYSTEMS USING STOPWATCH PETRI NETSIJCSEA Journal
In this paper, we propose a reliability approach in which feared events define reliability requirements and
taking them into account allows to design systems which will be able to avoid the drift towards a feared
state. The description of feared scenarios since the system design phase enables us to understand the
reasons of the feared behavior in order to envisage the necessary reconfigurations and choose safe
architectures. In order to face the increasing complexity of embedded systems and to represent the
suspension and resumption of task execution we propose to extract directly feared scenarios from
Stopwatch Petri net model avoiding the generation of the associated reachability graph and the eternal
combinative explosion problem.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
COMPARING THE IMPACT OF MOBILE NODES ARRIVAL PATTERNS IN MANETS USING POISSON...ijwmn
This paper compares the impact of mobile node arrival patterns in mobile ad hoc networks (MANETs) using Poisson and Pareto distribution models. Through MATLAB simulations, the study investigates how the arrival rate and size of the mobile node population are affected by these distributions. The results indicate that higher arrival rates can influence larger mobile node populations in a given area. Additionally, the Pareto distribution is found to better model mobile node mobility in MANETs compared to the Poisson distribution.
Activity Recognition From IR Images Using Fuzzy Clustering TechniquesIJTET Journal
Infrared sensors ensures that activity recognition is possible in the day and night times. It is used especially for activity monitoring of older adults as falls are more prevalent at night than the day. This paper focus on an application of fuzzy set techniques and it is capable of accurately detecting several different activity states related to fall detection and fall risk assessment and it also includes sitting, standing and being on the floor to ensure that elderly residents gets the help they need quickly in case of emergencies. Fall detection and fall risk assessment is used for an aging in place facility for the elderly people. It describes the silhouette extraction process, the image features , and the fuzzy clustering technique.
VARIATIONAL MONTE-CARLO APPROACH FOR ARTICULATED OBJECT TRACKINGcsandit
In this paper, we describe a novel variational Monte Carlo approach for modeling and tracking
body parts of articulated objects. An articulated object (human target) is represented as a
dynamic Markov network of the different constituent parts. The proposed approach combines
local information of individual body parts and other spatial constraints influenced by
neighboring parts. The movement of the relative parts of the articulated body is modeled with
local information of displacements from the Markov network and the global information from
other neighboring parts. We explore the effect of certain model parameters (including the
number of parts tracked; number of Monte-Carlo cycles, etc.) on system accuracy and show that
ourvariational Monte Carlo approach achieves better efficiency and effectiveness compared to
other methods on a number of real-time video datasets containing single targets.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
This document discusses modeling and identifying spacecraft systems using adaptive neuro fuzzy inference systems (ANFIS). It presents ANFIS as a framework for controlling nonlinear multi-input multi-output systems with uncertainties. The document analyzes four cases of identifying a spacecraft system: deterministic models without and with noise, and ANFIS models without and with noise. It describes using ANFIS to represent a multi-input multi-output system as coupled input-output models. Experimental results demonstrate ANFIS's effectiveness in system identification.
Comparison of Neural Network Training Functions for Hematoma Classification i...IOSR Journals
Classification is one of the most important task in application areas of artificial neural networks
(ANN).Training neural networks is a complex task in the supervised learning field of research. The main
difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training
function for the classification task. We compared the performances of three types of training algorithms in feed
forward neural network for brain hematoma classification. In this work we have selected Gradient Descent
based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under
conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with
Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The
last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are
selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of
convergence and correctness of the classification. Our conclusion about the training functions is based on the
simulation results
Neural Network Implementation Control Mobile RobotIRJET Journal
This document describes the design and implementation of a neural network controlled mobile robot. The robot is equipped with IR sensors to detect obstacles and a microcontroller runs a neural network program. The neural network is trained offline using a backpropagation algorithm and sensor input patterns to navigate around obstacles. Experimental results showed the robot could successfully react to new obstacle configurations not in its training set. Potential applications of neural networks discussed include industrial process control, sales forecasting, and target marketing. The design could be improved by adding GPS and speed control to allow the robot to navigate to a target destination avoiding obstacles.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Analysis of intelligent system design by neuro adaptive control no restrictioniaemedu
This document discusses using neuro-adaptive control to analyze the design of intelligent systems. It begins by introducing the topic and noting that conventional adaptive control techniques assume explicit system models or dynamic structures based on linear models, which may not be valid for complex nonlinear systems. Neural networks and other intelligent control approaches that do not require explicit mathematical modeling are presented as alternatives. The paper then focuses on using time-delay neural networks for system identification and control of nonlinear dynamic systems. Various neural network architectures and learning algorithms for system modeling and control are described.
Analysis of intelligent system design by neuro adaptive controliaemedu
This document summarizes the analysis of intelligent system design using neuro-adaptive control methods. It discusses using neural networks for system identification through series-parallel and parallel models. It also discusses supervised control using a neural network trained by an expert operator, inverse control using a neural network trained on the inverse system model, and neuro-adaptive control using two neural networks - one for system identification and one for control. Neuro-adaptive control allows handling nonlinear system behavior without linear approximations.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
This paper presents a new methodology for designing a neuro-fuzzy control for complex physical systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The advantage of this technique is that, produce a simple and well-performing system because it selects the fuzzy sets and the numerical numbers and process both numerical and linguistic information. This approach is able to process and learn numerical information as well as linguistic information. The proposed control scheme is applied to a multi-area power system with hydraulic and thermal turbines.
1. Self-organizing maps (SOM) are an unsupervised learning algorithm that transform high-dimensional data into lower dimensions for visualization while preserving topological properties.
2. The SOM network has an input layer fully connected to an output layer arranged in a grid, with each node containing a weight vector of the same dimension as inputs.
3. During training, the best matching unit (BMU) and its neighbors on the grid have their weight vectors adjusted to better match the input based on their distance from the BMU, with learning rates decreasing over time.
Development of a virtual linearizer for correcting transducer static nonlinea...ISA Interchange
This document describes the development of a virtual linearizer to correct for static nonlinearity in transducers. It involves developing two integrated software modules: 1) a data acquisition and management software using test-point software to acquire transducer input-output data and display results, and 2) a soft compensator using a multilayer feedforward backpropagation neural network trained with the Levenberg-Marquardt algorithm to model the inverse response of the transducer and provide compensation for nonlinearity. The virtual linearizer is able to acquire transducer data, train the neural network model, validate the model's performance, and provide a digital readout of the measured value compensated for nonlinearity.
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
This document analyzes node localization in wireless sensor networks. It compares three range-based localization algorithms (TOA, AOA, RSSI) based on their standard deviation of localization error under varying network parameters. Through simulations, it finds that the TOA algorithm generally provides the lowest error compared to the other two algorithms. Specifically, it finds that standard deviation decreases with increasing network density and anchor node density, but first decreases and then increases with network size. It concludes that the TOA algorithm provides the best accuracy for localization based on its analysis of parameter effects.
This document summarizes research on improving image classification results using neural networks. It compares common image classification methods like support vector machines (SVM) and K-nearest neighbors (KNN). It then evaluates the performance of multilayer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on image classification. The document tests various configurations of MLP and RBF networks on a dataset containing 2310 images across 7 classes. It finds that a MLP network with two hidden layers of 10 neurons each achieves the best results, with an average accuracy of 98.84%. This is significantly higher than the 84.47% average accuracy of RBF networks and outperforms KNN classification as well. The research concludes that neural
Integrating Fuzzy Mde- AT Framework For Urban Traffic SimulationWaqas Tariq
This document summarizes a research paper that proposes integrating fuzzy modeling concepts with Model Driven Engineering (MDE) and Activity Theory (AT) to develop a framework for simulating urban traffic systems. The framework uses AT concepts like activity, subject, object, tools etc. to model the traffic system. It then applies fuzzy set theory to quantify uncertainty in the modeling. MDE is used to successively refine models from analysis to design. The framework was applied to develop a platform independent model of an urban traffic control system using UML. Fuzzy relationships were defined between model elements to represent uncertainty in message passing between system entities. The framework allows modeling both behavioral and structural aspects of the traffic system using fuzzy concepts integrated with MDE and
Mobility models for delay tolerant network a surveyijwmn
Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the
environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route
data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and
application protocols are highly dependent on the underlying mobility of nodes and its characteristics.
Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate DTN
protocols across many scenarios. In DTN mobility modelling literature, a number of mobility models have
been developed based on synthetic theory and real world mobility traces. Furthermore, many researchers
have developed specific application oriented mobility models. All these models do not provide accurate
evaluation in the all scenarios. Therefore, model selection is an important issue in DTN protocol
simulation. In this study, we have summarized various widely used mobility models and made a comparison
of their performances. Finally, we have concluded with future research directions in mobility modelling for
DTN simulation.
Develop a mobility model for MANETs networks based on fuzzy Logiciosrjce
The study and research in the field of networks MANETs depends alleged understand the protocols
well of the simulation process before they are applied in the real world, so that we create an environment
similar to these networks. The problem of a set of nodes connected with each other wirelessly, this requires the
development of a comprehensive model and full and real emulator for the movement of the contract on behalf of
stochastic models. Many models came to address the problems of random models that restricted the movement
of decade barriers as well as the signals exchanged between them, but these models were not receiving a lot of
light on the movement of the contract, such as direction, speed and path that is going by the node. The main
goal is to get a comprehensive model and simulator for all parts of the environment of the barriers and
obstacles to the movement of the nodes and the mobile signal between them as well as to focus on the movement
transactions for the node of the direction, speed, and best way. . This research aims to provide a realistic
mobility model for MANET networks. It also addresses the problem of imprecision in social relationships and
the location where we apply Fuzzy logic.
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
Model Based Hierarchical and Distributed Control of Discrete Event Robotic Sy...Waqas Tariq
This paper deals with the modeling and control of discrete event robotic systems using extended Petri nets, and proposes a methodology of model based design and implementation of hierarchical and distributed control. Based on the hierarchical approach, the Petri net is translated into the detailed Petri net by stepwise refinements from the highest conceptual level to the lowest machine control level. The coordinator is introduced to coordinate the distributed controllers so that the decomposed transitions fire at the same time. System coordination algorithm through communication between the coordinator and the controllers, is implemented using multithread programming. By the proposed method, modeling, simulation and control of large and complex robotic systems can be performed consistently using Petri nets.
FORMAL MODELING AND VERIFICATION OF MULTI-AGENTS SYSTEM USING WELLFORMED NETScsandit
Multi-agent systems are asynchronous and distributed computer systems. These characteristics make them also a discrete-event dynamic system. It is, therefore, important to analyze the behavior of such systems to ensure that they terminate correctly and satisfy other important properties. This paper presents a formal modeling and analysis of MAS, based on Well-formed Nets, in order to ensure the absence of any undesired or unexpected behavior. To validate our ontribution, we consider the timetable problem, which is a multi-agent resource allocation problem.
1) The document proposes implementing an efficient K-means clustering algorithm to enhance connectivity and lifetime in wireless sensor networks.
2) It compares the proposed K-means algorithm to an existing Jumper Firefly algorithm based on energy consumption, network lifetime, and end-to-end delay.
3) Simulation results show the proposed K-means algorithm improves performance by reducing energy consumption from 16 to 12 Joules, increasing network lifetime by 96% compared to 83% for the existing algorithm, and lowering end-to-end delay from 3.7 to 2.7 seconds.
This document discusses modeling and identifying spacecraft systems using adaptive neuro fuzzy inference systems (ANFIS). It presents ANFIS as a framework for controlling nonlinear multi-input multi-output systems with uncertainties. The document analyzes four cases of identifying a spacecraft system: deterministic models without and with noise, and ANFIS models without and with noise. It describes using ANFIS to represent a multi-input multi-output system as coupled input-output models. Experimental results demonstrate ANFIS's effectiveness in system identification.
Comparison of Neural Network Training Functions for Hematoma Classification i...IOSR Journals
Classification is one of the most important task in application areas of artificial neural networks
(ANN).Training neural networks is a complex task in the supervised learning field of research. The main
difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training
function for the classification task. We compared the performances of three types of training algorithms in feed
forward neural network for brain hematoma classification. In this work we have selected Gradient Descent
based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under
conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with
Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The
last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are
selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of
convergence and correctness of the classification. Our conclusion about the training functions is based on the
simulation results
Neural Network Implementation Control Mobile RobotIRJET Journal
This document describes the design and implementation of a neural network controlled mobile robot. The robot is equipped with IR sensors to detect obstacles and a microcontroller runs a neural network program. The neural network is trained offline using a backpropagation algorithm and sensor input patterns to navigate around obstacles. Experimental results showed the robot could successfully react to new obstacle configurations not in its training set. Potential applications of neural networks discussed include industrial process control, sales forecasting, and target marketing. The design could be improved by adding GPS and speed control to allow the robot to navigate to a target destination avoiding obstacles.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Analysis of intelligent system design by neuro adaptive control no restrictioniaemedu
This document discusses using neuro-adaptive control to analyze the design of intelligent systems. It begins by introducing the topic and noting that conventional adaptive control techniques assume explicit system models or dynamic structures based on linear models, which may not be valid for complex nonlinear systems. Neural networks and other intelligent control approaches that do not require explicit mathematical modeling are presented as alternatives. The paper then focuses on using time-delay neural networks for system identification and control of nonlinear dynamic systems. Various neural network architectures and learning algorithms for system modeling and control are described.
Analysis of intelligent system design by neuro adaptive controliaemedu
This document summarizes the analysis of intelligent system design using neuro-adaptive control methods. It discusses using neural networks for system identification through series-parallel and parallel models. It also discusses supervised control using a neural network trained by an expert operator, inverse control using a neural network trained on the inverse system model, and neuro-adaptive control using two neural networks - one for system identification and one for control. Neuro-adaptive control allows handling nonlinear system behavior without linear approximations.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
This paper presents a new methodology for designing a neuro-fuzzy control for complex physical systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The advantage of this technique is that, produce a simple and well-performing system because it selects the fuzzy sets and the numerical numbers and process both numerical and linguistic information. This approach is able to process and learn numerical information as well as linguistic information. The proposed control scheme is applied to a multi-area power system with hydraulic and thermal turbines.
1. Self-organizing maps (SOM) are an unsupervised learning algorithm that transform high-dimensional data into lower dimensions for visualization while preserving topological properties.
2. The SOM network has an input layer fully connected to an output layer arranged in a grid, with each node containing a weight vector of the same dimension as inputs.
3. During training, the best matching unit (BMU) and its neighbors on the grid have their weight vectors adjusted to better match the input based on their distance from the BMU, with learning rates decreasing over time.
Development of a virtual linearizer for correcting transducer static nonlinea...ISA Interchange
This document describes the development of a virtual linearizer to correct for static nonlinearity in transducers. It involves developing two integrated software modules: 1) a data acquisition and management software using test-point software to acquire transducer input-output data and display results, and 2) a soft compensator using a multilayer feedforward backpropagation neural network trained with the Levenberg-Marquardt algorithm to model the inverse response of the transducer and provide compensation for nonlinearity. The virtual linearizer is able to acquire transducer data, train the neural network model, validate the model's performance, and provide a digital readout of the measured value compensated for nonlinearity.
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
This document analyzes node localization in wireless sensor networks. It compares three range-based localization algorithms (TOA, AOA, RSSI) based on their standard deviation of localization error under varying network parameters. Through simulations, it finds that the TOA algorithm generally provides the lowest error compared to the other two algorithms. Specifically, it finds that standard deviation decreases with increasing network density and anchor node density, but first decreases and then increases with network size. It concludes that the TOA algorithm provides the best accuracy for localization based on its analysis of parameter effects.
This document summarizes research on improving image classification results using neural networks. It compares common image classification methods like support vector machines (SVM) and K-nearest neighbors (KNN). It then evaluates the performance of multilayer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on image classification. The document tests various configurations of MLP and RBF networks on a dataset containing 2310 images across 7 classes. It finds that a MLP network with two hidden layers of 10 neurons each achieves the best results, with an average accuracy of 98.84%. This is significantly higher than the 84.47% average accuracy of RBF networks and outperforms KNN classification as well. The research concludes that neural
Integrating Fuzzy Mde- AT Framework For Urban Traffic SimulationWaqas Tariq
This document summarizes a research paper that proposes integrating fuzzy modeling concepts with Model Driven Engineering (MDE) and Activity Theory (AT) to develop a framework for simulating urban traffic systems. The framework uses AT concepts like activity, subject, object, tools etc. to model the traffic system. It then applies fuzzy set theory to quantify uncertainty in the modeling. MDE is used to successively refine models from analysis to design. The framework was applied to develop a platform independent model of an urban traffic control system using UML. Fuzzy relationships were defined between model elements to represent uncertainty in message passing between system entities. The framework allows modeling both behavioral and structural aspects of the traffic system using fuzzy concepts integrated with MDE and
Mobility models for delay tolerant network a surveyijwmn
Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the
environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route
data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and
application protocols are highly dependent on the underlying mobility of nodes and its characteristics.
Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate DTN
protocols across many scenarios. In DTN mobility modelling literature, a number of mobility models have
been developed based on synthetic theory and real world mobility traces. Furthermore, many researchers
have developed specific application oriented mobility models. All these models do not provide accurate
evaluation in the all scenarios. Therefore, model selection is an important issue in DTN protocol
simulation. In this study, we have summarized various widely used mobility models and made a comparison
of their performances. Finally, we have concluded with future research directions in mobility modelling for
DTN simulation.
Develop a mobility model for MANETs networks based on fuzzy Logiciosrjce
The study and research in the field of networks MANETs depends alleged understand the protocols
well of the simulation process before they are applied in the real world, so that we create an environment
similar to these networks. The problem of a set of nodes connected with each other wirelessly, this requires the
development of a comprehensive model and full and real emulator for the movement of the contract on behalf of
stochastic models. Many models came to address the problems of random models that restricted the movement
of decade barriers as well as the signals exchanged between them, but these models were not receiving a lot of
light on the movement of the contract, such as direction, speed and path that is going by the node. The main
goal is to get a comprehensive model and simulator for all parts of the environment of the barriers and
obstacles to the movement of the nodes and the mobile signal between them as well as to focus on the movement
transactions for the node of the direction, speed, and best way. . This research aims to provide a realistic
mobility model for MANET networks. It also addresses the problem of imprecision in social relationships and
the location where we apply Fuzzy logic.
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
Model Based Hierarchical and Distributed Control of Discrete Event Robotic Sy...Waqas Tariq
This paper deals with the modeling and control of discrete event robotic systems using extended Petri nets, and proposes a methodology of model based design and implementation of hierarchical and distributed control. Based on the hierarchical approach, the Petri net is translated into the detailed Petri net by stepwise refinements from the highest conceptual level to the lowest machine control level. The coordinator is introduced to coordinate the distributed controllers so that the decomposed transitions fire at the same time. System coordination algorithm through communication between the coordinator and the controllers, is implemented using multithread programming. By the proposed method, modeling, simulation and control of large and complex robotic systems can be performed consistently using Petri nets.
FORMAL MODELING AND VERIFICATION OF MULTI-AGENTS SYSTEM USING WELLFORMED NETScsandit
Multi-agent systems are asynchronous and distributed computer systems. These characteristics make them also a discrete-event dynamic system. It is, therefore, important to analyze the behavior of such systems to ensure that they terminate correctly and satisfy other important properties. This paper presents a formal modeling and analysis of MAS, based on Well-formed Nets, in order to ensure the absence of any undesired or unexpected behavior. To validate our ontribution, we consider the timetable problem, which is a multi-agent resource allocation problem.
1) The document proposes implementing an efficient K-means clustering algorithm to enhance connectivity and lifetime in wireless sensor networks.
2) It compares the proposed K-means algorithm to an existing Jumper Firefly algorithm based on energy consumption, network lifetime, and end-to-end delay.
3) Simulation results show the proposed K-means algorithm improves performance by reducing energy consumption from 16 to 12 Joules, increasing network lifetime by 96% compared to 83% for the existing algorithm, and lowering end-to-end delay from 3.7 to 2.7 seconds.
This document proposes a modified ant colony optimization (ACO) routing protocol for mobile ad hoc networks (MANETs). The key points are:
1) The protocol is based on swarm intelligence principles and uses mobile software agents like ants to intelligently route packets from node to node.
2) It modifies the standard ACO algorithm to make it power-balanced and achieve faster packet delivery rates by making the pheromone decay dependent on nodes' battery levels.
3) The routing process involves forward and backward ants establishing and maintaining routes between source and destination via probabilistic path selection based on accumulated pheromone levels.
A deep reinforcement learning strategy for autonomous robot flockingIJECEIAES
Social behaviors in animals such as bees, ants, and birds have shown high levels of intelligence from a multi-agent system perspective. They present viable solutions to real-world problems, particularly in navigating constrained environments with simple robotic platforms. Among these behaviors is swarm flocking, which has been extensively studied for this purpose. Flocking algorithms have been developed from basic behavioral rules, which often require parameter tuning for specific applications. However, the lack of a general formulation for tuning has made these strategies difficult to implement in various real conditions, and even to replicate laboratory behaviors. In this paper, we propose a flocking scheme for small autonomous robots that can self-learn in dynamic environments, derived from a deep reinforcement learning process. Our approach achieves flocking independently of population size and environmental characteristics, with minimal external intervention. Our multi-agent system model considers each agent’s action as a linear function dynamically adjusting the motion according to interactions with other agents and the environment. Our strategy is an important contribution toward real-world flocking implementation. We demonstrate that our approach allows for autonomous flocking in the system without requiring specific parameter tuning, making it ideal for applications where there is a need for simple robotic platforms to navigate in dynamic environments.
DYNAMIC AND REALTIME MODELLING OF UBIQUITOUS INTERACTIONcscpconf
This document discusses modeling real-time interaction between a user and a ubiquitous system using dynamic Petri net models. It proposes using Petri nets to model a user's activity as a set of elementary actions. Elementary actions are modeled as Petri net structures that are then composed together through techniques like sequence, parallelism, etc. to form an overall model of user-system interaction. The models can be dynamically adapted based on changes to the user's context. OWL-S ontology is used to describe the dynamic aspects of the Petri net models, especially real-time composition of models. Simulation results validate the approach of dynamically modeling user-system interaction through mutation of Petri net models.
This document proposes an algorithm called Agent NetReconf that uses autonomous agents to dynamically reconfigure networks in the presence of multiple node and link failures. Agent NetReconf is a distributed algorithm that does not require global network topology knowledge. It uses mobile agents deployed at each router to detect failures and construct a restoration spanning tree to reconnect disconnected nodes and update routing tables asynchronously. The algorithm proceeds in four phases - leader selection, restoration tree construction, reconfiguration synchronization, and routing table updates. It is analyzed and proven to ensure termination, liveness, and safety.
This chapter discusses how mobility can positively impact mobile ad hoc networks (MANETs) if properly managed. It begins by introducing common mobility models used to simulate node movement. It then discusses how mobility exists at the node, information, and user levels in MANETs. The chapter argues that mobility can improve routing capability by enabling temporary connections between nodes. It outlines two main approaches - uncontrolled schemes that rely on inherent random node movement, like epidemic routing, and controlled schemes that plan node movement to ferry messages, like message ferrying. Finally, it discusses additional ways mobility may increase network capacity, security, and information dissemination if accounted for in protocol design from the start.
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
Minimum Process Coordinated Checkpointing Scheme For Ad Hoc Networks pijans
The wireless mobile ad hoc network (MANET) architecture is one consisting of a set of mobile hosts
capable of communicating with each other without the assistance of base stations. This has made possible
creating a mobile distributed computing environment and has also brought several new challenges in
distributed protocol design. In this paper, we study a very fundamental problem, the fault tolerance
problem, in a MANET environment and propose a minimum process coordinated checkpointing scheme.
Since potential problems of this new environment are insufficient power and limited storage capacity, the
proposed scheme tries to reduce the amount of information saved for recovery. The MANET structure used
in our algorithm is hierarchical based. The scheme is based for Cluster Based Routing Protocol (CBRP)
which belongs to a class of Hierarchical Reactive routing protocols. The protocol proposed by us is nonblocking coordinated checkpointing algorithm suitable for ad hoc environments. It produces a consistent
set of checkpoints; the algorithm makes sure that only minimum number of nodes in the cluster are
required to take checkpoints; it uses very few control messages. Performance analysis shows that our
algorithm outperforms the existing related works and is a novel idea in the field. Firstly, we describe an
organization of the cluster. Then we propose a minimum process coordinated checkpointing scheme for
cluster based ad hoc routing protocols.
Unknown input observer for Takagi-Sugeno implicit models with unmeasurable pr...IJECEIAES
Recent years have seen a great deal of interest in implicit nonlinear systems, which are used in many different engineering applications.This study is dedicated to presenting a new method of fuzzy unknown inputs observer design to estimate simultaneously both non-measurable states and unknown inputs of continuous-time nonlinear implicit systems defined by Takagi-Sugeno (T-S) models with unmeasurable premise variables. The suggested observer is based on the singular value decomposition approach and rewritten the continuous-time T-S implicit models into an augmented fuzzy system, which gathers the unknown inputs and the state vector. The exponential convergence condition of the observer is established by using the Lyapunov theory and linear matrix inequalities are solved to determine the gains of the observer. Finally,the effectiveness of the suggested method is then assessed using a numerical application. It demonstrates that the estimated variables and the unknown input converge to the real variables accurately and quickly (less than 0.5 s).
Link Disconnection Entropy Disorder in Mobile Adhoc Networkspaperpublications3
Abstract: In Mobile Ad-hoc Networks, nodes move freely causing an interruption in communications. This communication interruption can be accounted in a time lapse to an entropy to connection or disconnection; the combined entropy disorder of a node’s links describe how suitable a node is to communicate to neighbors. This entropy disorder is tightly coupled to mobility and communications factors such as node’s speed or data traffic saturation. In this paper, we analyze the relationship between speed and traffic saturation into a disorder in link entropy with a focus on disconnection, namely Link Disconnection Entropy Disorder (LDED). The findings indicate a high LDED value to nodes with high speed.
Bilateral control of master slave manipulators with constant time delayISA Interchange
This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006) [14] to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved performance. Moreover, by using Fourier transform and Parseval’s identity in the frequency domain, we demonstrate that this new PID controller preserves the passivity of the system. Simulation and semi-experimental results show that the PID controller tracking performance is superior to that of the PD controller tracking performance in slave/environmental contacts.
Power system and communication network co simulation for smart grid applicationsIndra S Wahyudi
This paper proposes a power system and communication network co-simulation framework that integrates a power system dynamics simulator (PSLF) and a network simulator (NS2). The framework uses a global scheduler to run the simulation in a discrete event-driven manner, eliminating errors from separate synchronization of the simulators. As a case study, an agent-based remote backup relay system is simulated using this co-simulation framework to demonstrate its effectiveness in evaluating smart grid applications involving interactions between power and communication systems.
Congestion control based on sliding mode control and scheduling with prioriti...eSAT Publishing House
This document presents a method for joint congestion control and scheduling in wireless networks using sliding mode control. It formulates the problem using network utility maximization to maximize total utility while satisfying capacity constraints. Dual decomposition is used to separate the problem into congestion control and scheduling subproblems. A sliding mode controller is designed for congestion control based on queue length feedback. The scheduling problem depends on Lagrangian prices from the congestion control problem. Simulation results show improved packet delivery ratio, throughput, and performance under varying signal-to-noise ratios. The method jointly optimizes congestion control and scheduling using a sliding mode approach.
An Efficient Mechanism of Handling MANET Routing Attacks using Risk Aware Mit...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
CoCoWa is a collaborative approach to detecting selfish nodes in mobile ad-hoc networks (MANETs) and delay tolerant networks (DTNs) that improves upon local watchdog approaches. It combines local watchdog detections with the dissemination of information about detected selfish nodes between nodes during contacts. This reduces the time and increases the precision of detecting selfish nodes by reducing the effects of false positives and negatives generated by local watchdogs. The paper presents an analytical model and experimental evaluation using mobility traces showing CoCoWa provides significantly faster and more accurate detection of selfish nodes with reduced overhead compared to traditional watchdog approaches.
This document proposes developing a mobility model for mobile ad hoc networks (MANETs) based on fuzzy logic. It discusses existing mobility models and their limitations in capturing realistic node movement. The proposed model aims to provide a more realistic mobility model for MANETs by incorporating fuzzy logic to address imprecision in social relationships and node locations. It defines mathematical formulas to model social relationships between nodes and calculate the probability of nodes visiting locations based on these relationships and associated weights that vary over time. The model aims to take a more comprehensive approach to mobility modeling in MANETs by considering social, geographical, and temporal factors.
Similar to DECENTRALIZED SUPERVISION OF MOBILE SENSOR NETWORKS USING PETRI NET (20)
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
DECENTRALIZED SUPERVISION OF MOBILE SENSOR NETWORKS USING PETRI NET
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
DOI : 10.5121/ijcseit.2015.5402 11
DECENTRALIZED SUPERVISION OF MOBILE
SENSOR NETWORKS USING PETRI NET
Fatemeh Jafarinejad1
and Ali A. Pouyan2
1
Department of Computer & IT Engineering, Shahrood University of Technology,
Shahrood, Iran
2
Department of Computer & IT Engineering, Shahrood University of Technology,
Shahrood, Iran
ABSTRACT
In semiautonomous mobile sensor networks, since human operators may be involved in the control loop,
particular improper actions may cause accidents and result in catastrophes. For such systems, this paper
proposes a decentralized supervisory control system to accept or reject the human-issued commands so
that undesirable executions never be performed. In the present approach, Petri nets are used to model the
operated behaviors and to synthesize the decentralized supervisory system. The presented technique could
be applied to large-scale and complicated wireless mobile sensor networks.
KEYWORDS
District Event System, Petri net, Sensor Network, Supervisory Control.
1. INTRODUCTION
Nowadays, sensor networks (SNs) have been used in different areas such as networking,
embedded systems, pervasive computing, and multi agent systems due to its wide array of real-
world applications [1]. In particular, wireless sensor networks (WSNs) with the ability of sensing,
storing and processing data can detect and monitor any different physical conditions such as
temperature, pressure, sound, etc. Moreover, WSNs can be deployed in extremely harsh
environments and hostile regions (ocean floor, active volcanoes, mines, forests) [2,3]
.Furthermore, they are used in wide variety of fields such as control systems, health monitoring,
bio-medical applications, detect pollution, detect smoke to fire alarm, military (battlefield
surveillance and troop movements), burglary and so on [4].
Because of time consumption and hardship of configuration of WSNs, mobile sensor networks
(MSNs) are used to support self-configuration, adaptability, scalability, and optimal performance.
These features, achieved by changing network topology, can react to the events of environment or
change the mission planning [5].
In some of MSN systems human operators use semiautonomous robots for charging the static
sensors, repairing replacing or removing the static sensors, maintaining network coverage for
both sensing and communication, and investigating condition of launching an alert by several
static sensors [6] (Fig. 1). In such cases, human errors in sending a command to robots have a
significant influence on system. Therefore, the use of a controller to control and filter the
commands received from the human is a good idea to manage these “human-in-the-loop” errors
and it improves the overall reliability of the system. This kind of controller is called supervisor
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
12
and in such environment with some robots, designing a Supervisor is necessary to restrict action
of robots to preserve the mutual exclusions of the multi-agent system.
In this paper a Petri net decentralized supervisor to control the agents of an MSN system is
proposed. In section 2, we explain a brief summary of the related work. Section 3 brings an
introduction to Petri nets and its usage in supervisory control. In section 4 the proposed method is
described by an example. Finally the conclusion is mentioned in section 5.
Figure1. Human –involved MSNEs
2. RELATED WORK
Autonomous robotic sensor agents are used in complex environments for active investigation. In
[7], the networks of such sensor agents for these circumstances have been studied.
An important issue in these applications is control of robots. Some papers use the Supervisory
control theory to provide a suitable framework for controlling the states in a MSN system.
Lee and Hsu in [8] propose (for the first time using Petri nets) a technique to design supervisory
agents to prevent abnormal human operations from being carried out. This supervisory approach
was also applied to human-computer interactive systems [6].
In [9] Lee and Chung proposed a PN-based localization scheme on a discrete event control
framework for indoor service robots.
In 2008, Lee [6] also proposes a command filtering framework to accept or reject the human-
issued commands such that undesirable executions are never performed. He uses Petri nets to
model the operated behaviors and to synthesize the command filters for supervision.
In [5], Low describes a distributed layered architecture for resource-constrained multi robot
cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a
dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across
regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in
proportion to that of the moving targets to be tracked in a non-stationary environment. As a result,
the adverse effects of task interference between robots are minimized and network coverage is
improved. In the lower task execution layer, the robots use self-organizing neural networks to
coordinate their target tracking within a region. Both layers employ self-organization techniques,
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
13
which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and
self-protecting.
In 2010, Moldoveanu [1], continued Low work and proposed a command filtering framework to
accept or reject the human-issued commands so that undesirable executions are never performed.
He also used Petri nets to model the operated behaviors and to synthesize the command
filters for supervision.
3. PETRI NET AND IDEA OF SUPERVISORY CONTROL
We can think of a human-in-the-loop system as a discrete-event system (DES) because the
progress in the system and state changes in it are driven by occurrences of individual events of
human action. Supervisory control theory (SCT) provides a suitable framework for synthesizing
DES [10]. Primarily, SCT was studied by automaton based models but nowadays an increasing
interest is given to Petri net based models [11]. Petri net is a graphical and mathematical
modeling tool which can be used as a visual communication aid. Basically, Petri net is a bipartite
graph consisting of two types of nodes, places and transitions, connected by arcs.
A Petri net is a 5-tuple [12], Petri net = (P, T, F, W, M0) where:
P = {p1, p2, …, pm} is a finite set of places,
T = {t1, t2, …, tn} is a finite set of transitions,
F ⊆ (P × T) U (T × P) is a set of arcs (flow relation),
W: F → {1, 2, 3, …} is a weight function,
M0: P → {1, 2, 3, … } is the initial marking,
P ∩ T = Ø and P U T ≠ Ø. (1)
Places and transitions are called nodes and denote states and events in the DES. In Petri net with
m places and n transitions, the incidence matrix A is n×m matrix whose elements are:
Aij = w (tj, pi) – w (ti, pj) (2)
Where w (t, p) is the weight of the arc between p and t. If all arcs in Petri net have weights equal
to 1, it should be noted that:
A = O – I (3)
The matrices I (input matrix) and O (output matrix) provide a complete description of the
structure of PN. If there are no self loops, the structure may be described only by A. The
incidence matrix allows an algebraic description of the evolution of the marking of PN. The
marking of Petri net changes from marking mk to marking mk+1:
mk+1 = mk + WT
.v (4)
Where v is a transition vector composed of non-negative integers that correspond to the number
of times a particular transition has been fired between markings mk and mk+1 . [13]
The behavior and state changes of a DES system can be modeled by a Petri net model. In some
cases we want to control the qualitative behavior of a system. Given an uncontrolled system, there
exist supervisors which restrict the behavior of the system to the desired behavior by dynamically
disallowing some of controllable events [14].
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
14
In Petri net model of the system, any marking of Petri net represent a state of system and
transitions represent the state change or behavior of system. Undesired behaviors of system can
be expressed as states in Petri net that are unwanted to occur, called forbidden states. If these
states are not related to each other, called arbitrary forbidden state, they can be controlled by
reachability analysis of Petri net (e. g. work of Dideban and Alla in [15]).
But if the constraints are related to each other and sets of forbidden states can be described in a
linear equation, the problem of restricting the behavior, called generalized mutual exclusion
problem, can be solved by Place-invariant analysis of Petri net model. This analysis will impose
some extra places, called supervisor places, in net. Finally by solving an LP equation, the initial
marking and incidence matrix of supervisor places are:
M0, s = b - LM0 (5)
Ds = -LD (6)
In which the b and L are elements of constraint equation: LM < b. L is the weight of each place in
this constraint, and b is the upper bound of linear composition of marking of places.
In large scale systems, because of reasons such as security and cost of sending commands, a
control architecture without central coordination and with communication between local
supervisors is desired, the idea which is called “thinking globally and acting locally” by Rudie
and Wonham in [16].
A decentralized supervisor S consists of a set of supervisors S1, S2, …, Sn operating in parallel,
such that a given specification is satisfied. This idea has been proposed for various applications
such as manufacturing, failure detection, and communication protocol [16].
In [17] an algorithm to achieve the decentralized supervisor, given the centralized one is
addressed. The idea is suitable in conditions where controllability and observability constraints of
transitions to the specified supervisor are satisfied and therefore the admissibility and
deadmissibility of constraints are satisfied. The Algorithm is proposed as follow in [17]:
Algorithm 1: Supervisory Design of a Deadmissible Constraint:
1) Let M0 be the initial marking of the system N, and S be control places of the centralized
supervisor, enforcing L.M<b. The constraints are admissible for disjoint sites of network,
called de-admissible constraints.
2) For all i∈S, let xi∈N be a state variable of Si :
Define Si for all i∈S, by the fallowing rules:
- Initialize xi = b-LM0
- If t∈Tc, i, t∈C• and xi < Ws(C, t), then Si disables t.
- If t fires and t∈To, i, t∈•C, then xi = xi + Ws(C, t).
- If t fires and t∈To, i, t∈C•, then xi = xi - Ws(C, t).
In this algorithm, C• is set of transitions belonging to output transitions of a control place. •C is
the set of input transitions of a control place. Tc, i is set of transitions that are controllable from
the supervisor Si. To, i is set of transitions that are observable from the supervisor Si. Ws(C, t) is
the weight of the arc between place C and transition t in supervisor S.
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
15
4. DECENTRALIZED SUPERVISION OF ROBOTS IN MSN
One idea to overcome the errors in human-in the-loop condition of a semiautonomous mobile
sensor is to construct a system to filter the commands received by human. Set of admissible states
of a command filtering systems in MSN are does that preserve the collision avoiding movements
of robots and deadlock-free operation of them.
Supervisor system of MSNs can have different architecture to receive and send information of
robots to each other. In [1], a different idea for designing architecture of supervision of agents is
discussed. It addresses works of the other papers to design a command filtering framework and
proposes a P2P communication framework as an architecture for MSNs (Fig. 2).
Figure 1. Command filtering framework for MSNs
We also use P2P architecture, But we use a decentralized command filter system where each
robot knows just useful information of the others and according to these information decide if the
command received from human is erroneous or not. Thus, the Petri net model of our system
should be in a way that it can be divided into models in robots or agents of the system.
PN model of [1] is a centralized model so that the state of all robots and state of environment are
controlled locally. It can be difficult to use this model in cases where the environment is more
complex and where we have different constraint for each robot.
It is preferred to construct the Petri net model of each robot just according to the environment and
finally, the overall system model is the composition of Petri net model of individual robots. One
Advantage of this model is that if the environment is more complex the overall Petri net model is
not very hard to be drived.
4.1 Example
To illustrate and explain the concept of decentralized supervision of MSN, we use the example
that is presented in [1] (Fig. 3). Therefore, the proposed Petri net model is easily comparable with
the Petri net model presented in [1].
The example consists of 8 rooms. Sensors and 5 robots which repair them are scattered in the
rooms. Rooms can be reached from any other rooms. The goal is avoiding collision of robots in
rooms. Initially all robots are in room 8. There is two constraints:
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
16
• The capacity of rooms1, 2, 5 and 6 is just one robot
• The capacity of rooms 3 and 4 is only two robots.
Figure3. Mobile wireless surveillance system with 5 robots
In [1], the Petri net model of system is based on the environment and agents. But because we
want to model the overall system and then divide it in Petri net model of each robot, we develop
another model of the system.
We make a Petri net model for each robot and the combination of Petri net model of all robots
constructs the overall system model. In this example, because we don’t have any constraint on an
especial robot, the Petri net models of all robots are the same and we just show the model of one
of them (Fig. 4)
State of each robot is as its existence in one room and absence of it from other rooms. Behavior or
state change of robots is related to their exchanging room, so the state change of robot is related
to the environment and the way between rooms. If two rooms have a way to each other, the robot
can change state of itself to be in the adjacent room from the other. Hence, the Petri net model is
derived very simply. Figure 4 shows the Petri net model of robot 1. In this figure, the places are
named as RiPj which means that robot i is in palace j, and transitions are named as iPjPk which
means that robot i moves from room j to room j.
Figure 4. Petri net Model of Robot 1.
The constraints of such system are:
a1p1+a2p1+a3p1+a4p1+a5p1+a6p1+a7p1+a8p1 <=1
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
17
a1p2+a2p2+a3p2+a4p2+a5p2+a6p2+a7p2+a8p2 <=1
a1p5+a2p5+a3p5+a4p5+a5p5+a6p5+a7p5+a8p5 <=1
a1p6+a2p6+a3p6+a4p6+a5p6+a6p6+a7p6+a8p6 <=1
a1p3+a2p3+a3p3+a4p3+a5p3+a6p3+a7p3+a8p3 <=2
a1p4+a2p4+a3p4+a4p4+a5p4+a6p4+a7p4+a8p4 <=2
By solving the above constraint equations for system, the net model with control places will be
achieved. The system with its supervisor can be seen in figure 5. In this figure the Petri net model
of all the robots other than robot 1 are presented as robot i just for simplicity of the model shape.
Appling the decentralized approach of [17] we can disjoint the supervisor of each robot agent and
construct a decentralized supervision for this MSN system. The key concept in this work is to
construct the overall Petri net system in a way which can be decomposed into some disjoint
supervisors. In the case of MSN systems it is important to construct the Petri net model in view
point of robot agents to be able to make a decentralized supervisory system. The supervisor of
robot 1 is presented in figure 6.
Figure 5. Centralized Supervisor of the MSN Example.
Figure 6. Decentralized Supervisor of Robot 1.
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.3/4, August 2015
18
5. CONCLUSIONS AND FUTURE WORK
In this paper, we suggest use of a decentralized supervision to control the robots in an MSN
system. In this multi agent system, each robot has only access to the state of itself and only knows
necessary information about others. In this system the security and information encapsulation are
as important as controlling procedure. So, the overall system is finally decomposed and divided in
supervisors of each of the robots and the overall system is modeled in robots point of view.
Another advantage of dividing Petri net model of the system into Petri net model of individual
robots is that it is easier to acquire the Petri net model of the entire system which is less sensitive
to environmental changes.
For the future work there are two main directions of research. The basic idea of this paper can be
applied to different supervisory applications to see their results and compare them to existing
approaches. Moreover, acquiring a general architecture for this category of problems will be a
required work.
REFERENCES
[1] F. Moldoveanu, D. Floroian and D. Puiu, (2010) “Petri Nets and Agents to Supervisory Control of
Complex Environment”, Bulletin of the Transilvania University of Brasov, Vol. 3, No. 52, pp 267-
276.
[2] F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, (2002) “A survey on sensor networks,”
IEEE communication Magazine, pp 102-114.
[3] G. Werner-Allen, (2006 ) “Deploying a Wireless Sensor Network on an Active Volcano,” IEEE
Trans. Internet Computing, vol. 10, no. 2, pp 18–25.
[4] A. A. Pouyan, S. M. M. Salehi and F. Jafarinejad, (2013) “A Markov-Based Model for Fault Tolerant
and Reliable Wireless Sensor Networks,” proceeding of 11th Iranian Conference on Intelligent
Systems.
[5] Low, K.H., Leow, W.K., et al., (2006) “Autonomic Mobile Sensor Network with Self-coordinated
Task Allocation,” IEEE Trans. Syst., Man, Cybern. C, No. 3, p. 315-327.
[6] J. S. Lee, (2008) “A Petri Net Design of Command Filters for Semiautonomous Mobile Sensor
Networks,” IEEE Trans. Ind. Electron., No. 4, p. 1835-1841.
[7] E. Petriu and T. Whalen, (2004) “Robotic Sensor Agents: A New Generation of Intelligent Agents for
Complex Environment Monitoring,” IEEE Instrumentation & Measurement Magazine, vol. 7, No. 3,
p. 46-51.
[8] J. S. Lee, P. L. Hsu, (2003) “Remote Supervisory Control of the Human-in-the-loop System by Using
Petri Nets and Java,” IEEE Trans. Ind. Electron. Vol. 50, No. 3, p. 431-439.
[9] D. Lee, W. Chung, (2006) “Discrete Status Based Localization for Indoor Service Robots,” IEEE
Trans. on Industrial Electronics. Vol. 53, No. 5, p. 1737-1746.
[10] R. J. Ramadge and W. M. Wonham, (1987) “Supervisory control of a class of discrete event
processes,” SIAM Journal on Control and Optimization, vol. 25, No. 1, pp 206-230.
[11] A. Ghaffari, N. Rezg and X. L. Xie, (2003) "Design of Live and Maximally Permissive Petri Net
Controller Using Theory of Regions", IEEE Trans. On Robotics and Automation, vol. 19, no. 1.
[12] T. Murata, (1989) “Petri nets: Properties, analysis and applications,” Proceedings of the IEEE, vol.
77, No. 4, pp 541-580.
[13] A. Gudelj, D. Kezić and S. Vidačić, (2012) “Marine Traffic Optimization Using Petri Net and Genetic
Algorithm,” Scientific Journal on Traffic and Transportation Research, Vol. 24, No. 6.
[14] R. Kumar and L. E. Holloway, (1996) “Supervisory Control of Deterministic Petri Nets with Regular
Specification Languages,” IEEE Trans. on Automatic Control, Vol. 41, No. 2, pp 245- 249.
[15] A. Dideban and H. Alla, (2009) “Controller Synthesis By Petri Nets Modeling,” Third International
Workshop on Verification and Evaluation of Computer and Communication Systems (VECoS 2009).
[16] K. Rudie and W. M. Wonham, (2002) “think globally, act locally: decentralized supervisory control,”
IEEE Transaction on Automatic Control, Vol. 37, No. 11, pp 1692 – 1708.M. V. Iordache and P. J.
Antsaklis, (2006) “decentralized supervision of Petri Net,” IEEE Trans. on Automatic Control, Vol.
51, No. 2, pp 376-381.