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Federated Recognition Architecture Based on voting and Feedback Mechanisms for Accuracy Object Classification in Distributed Edge Intelligence Environment

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IEEE SENSORS JOURNAL 2023年第22期23卷 27478-27489页

作者： Tuan, Nguyen Anh Xu, Rongxu Moe, Sa Jim Soe Kim, Dohyeun Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea Jeju Natl Univ Big Data Res Ctr Jeju 63243 South Korea

Deep learning has recently become a crucial tool to solve many complex problems and has the potential to revolutionize industries. With the widespread adoption of the Internet of Things, there are now many devices with limited computational resources that are capable of running deep learning models. It has opened up new opportunities to implement deep learning in the edge environment so that decisions can be made locally without sending data to a cloud server for processing. However, because of limited resources, the model's performance and communication overhead are challenges when deploying learning models to edge devices. The focus of this article is to investigate and propose a federated recognition architecture for object classification in a distributed edge intelligence environment. Specifically, we build an edge server that includes a voting module and a feedback module to improve the overall accuracy of object classification. The voting module aggregates predictions of multiple edge devices, whereas the feedback module sends the voting results to edge devices to adjust the local deep learning model. We build edge devices based on the EdgeX platform which makes it easy to manage data and optimize communication overheads. Because the edge server and edge nodes only exchange prediction results, our proposed architecture ensures security with sensitive data as well as deep learning model architecture. By testing on the image dataset, we evaluate the proposed architecture's performance and show that it outperforms individual local models in terms of accuracy. Furthermore, our experiments demonstrate that, with the feedback mechanism, the deep learning model is constantly updated with new data to maintain accuracy and avoid being outdated. Besides, we prove the real-time processing speed by collecting the delay time of the proposed model. The results show that our proposed architecture has the potential to be deployed in practical applications such as smart cities

关键词： Convolutional neural network (CNN) deep learning distributed edge environment EdgeX platform federated recognition Internet of Things online learning voting algorithms

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Thyroid prediction using ensemble data mining techniques

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International Journal of Information Technology (Singapore) 2022年第3期14卷 1273-1283页

作者： Yadav, Dhyan Chandra Pal, Saurabh VBS Purvanchal University (VBSPU) UP Jaunpur India Department of MCA VBS Purvanchal University (VBSPU) Jaunpur India

Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bagging Boosting Data mining meta classifier algorithms Stacking voting algorithms

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Velocity Sensor Fault-Tolerant Controller for Induction Machine Using Intelligent voting Algorithm

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ENERGIES 2022年第9期15卷 3084-3084页

作者： Alyoussef, Fadi Akrad, Ahmad Sehab, Rabia Morel, Cristina Kaya, Ibrahim Dicle Univ Inst Nat & Appl Sci Elect & Elect Engn Dept TR-21280 Diyarbakir Turkey Ecole Super Tech Aeronaut & Construct Automobile ESTACA Lab F-53000 Laval France Dicle Univ Fac Engn Elect & Elect Engn Dept TR-21280 Diyarbakir Turkey

Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC.

关键词： induction machine mechanical sensor failure fault-tolerant control sensorless control back-stepping controller sliding mode observer extended kalman filter neural networks fuzzy logic voting algorithms reliability performance

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Simulation environment for the choice of the decision making algorithm in multi-version real-time system

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INFORMATION AND SOFTWARE TECHNOLOGY 2020年第0期120卷 106245-000页

作者： Kovalev, Igor, V Saramud, Mikhail, V Losev, Vasiliy V. Siberian Fed Univ 79 Svobodny Ave Krasnoyarsk 660041 Russia Reshetnev Siberian State Univ Sci & Technol Krasnoyarsky Rabochy Av 31 Krasnoyarsk 660037 Russia

Context: Nowadays the most effective way to improve the reliability of software is an approach with the introduction of software redundancy - multi-version programming. The reliability of a multi-version system is determined not only by the reliability of the versions that make it up, but to a greater degree by the decision making algorithm. Objective: Our objective is evaluation and selection of the most reliable voting algorithms in multi-version environments. In order to get this objective there is a need to check all the algorithms in the execution environment, simulating characteristic of the developed system. Thus, we obtain the characteristics of the quality of the algorithm operation in precisely those conditions in which it will work in the system that is developed. Method: The article suggests weighted voting algorithms with a forgetting element, as well as modifications of existing voting algorithms. To be able to check the quality of their work, the simulation environment has been implemented that simulates the operation of the software multi-version execution environment. Results: The article substantiates the use of the most reliable decision making algorithms in the decision block of the real-time operating system. A comparative analysis of decision making algorithms for the operation of the decision making block of the multi-version real-time execution environment has been carried out. Conclusions: The software implementation of the simulation environment that implements the simulations of versions with given characteristics is considered, not only classical decision making algorithms, but also the author's modifications are investigated. The environment allows to obtain the quality characteristics of all implemented decision making algorithms with given system characteristics. The modeling results are considered, the dependence of the system reliability indicators on its input parameters is shown, a comparative analysis of various decision making al

关键词： Multi-version Fault tolerance Quality assurance Reliability voting algorithms Simulation modeling

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Speed Sensor Fault-Tolerant Controller for Induction Motor Using New Minimum Probability Voter Based on Signal Strength 3

Speed Sensor Fault-Tolerant Controller for Induction Motor U...

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3rd International Conference on Applied Automation and Industrial Diagnostics (ICAAID)

作者： Alyoussef, Fadi Akrad, Ahmad Dicle Univ Inst Nat & Appl Sci Elect & Elect Engn Dept Diyarbakir Turkey ENSEA Quartz Lab F-95000 Cergy France

ISBN: (纸本)9781728128740

A speed sensor fault-tolerant back-stepping controller with a new minimum probability voter based on signal strength (MPVSS) has been introduced in this paper. The major goal of MPVSS is to detect and to reconfigure the induction motor control in case of speed sensor failure (intermittent disconnection of an incremental encoder) using sliding mode observer for low speed estimation and extended Kalman filter for medium and high speed estimation. Here, the proposed MPVSS enjoys the feature of insensitivity to rotor resistance variations and easiness to set its threshold. In fact, signal strength plays an essential role in releasing the designer from severe constraints imposed by selecting a correct voter threshold. Simulation results show the efficiency of the suggested method. Besides, the superiority of the presented approach has been confirmed by comparing its performance with a maximum-likelihood voting algorithm.

关键词： Fault Tolerant Control Back-Stepping Controller voting algorithms Sliding Mode Observer Extended Kalman Filter

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Threshold behaviour of discordant voting on the complete graph

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JOURNAL OF DISCRETE algorithms 2018年 50卷 10-22页

作者： Cooper, Colin Rivera, Nicolas Kings Coll London Dept Informat London England

Given a connected graph G whose vertices are coloured in some way, a discordant voting process on G is as follows. At each step a pair of adjacent vertices with different colours interact, and one of the vertices changes its colour to match the other one. If eventually all vertices have the same colour, we say a consensus has been reached. A vertex is discordant if it has a discordant edge, i.e. a neighbour of a different colour. In the general discordant voting process, at each step a discordant vertex u is chosen uniformly at random, and then a discordant edge (u, v) is chosen from among the discordant edges of u, also uniformly at random. With probability beta vertex u adopts the colour of vertex v and with probability 1 - beta vertex v adopts the colour of vertex u. Let T be the number of steps needed to reach consensus. For the complete graph K-n with an initial colouring where half the vertices are red and half blue, then when beta = 0, ET = circle dot(n logn), whereas when beta = 1 then ET = circle dot(2(n)). The case beta = 1/2 corresponds to a simple random walk on a path with vertex set {0, 1,...,n} and has ET = n(2) / 4. We study the effect of varying beta from zero to one, thus revealing the detailed transition from ET = circle dot(n logn) to ET = circle dot(2(n)). In terms of beta, the transition from circle dot(n logn) to circle dot(n(2)) occurs in a scaling window of width O(1/n) around beta = 1/2. For any a > 1, there is an explicit value of beta = 1/2 + O(logn/n) for which ET = circle dot(n(a)). When beta > 1/2 constant, there is an explicit value a(beta) such that ET = circle dot(a(n)). (C) 2018 Elsevier B.V. All rights reserved.

关键词： Random processes voting algorithms Distributed computing

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voting Algorithm Approach for Autonomous Vehicle Safe Driving 19

Voting Algorithm Approach for Autonomous Vehicle Safe Drivin...

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19th IEEE International Conference on Industrial Technologies (ICIT)

作者： Boukhari, Mohamed Ryad Chaibet, Ahmed Boukhnifer, Moussa Glaser, Sebastien Inst VEDECOM Versailles France ESTACA Montigny Le Bretonneux France

ISBN: (纸本)9781509059492

The aim of this paper is to assess three voting algorithms ensuring fault tolerance for an autonomous vehicle. Thus, by using a vehicle longitudinal dynamic model and a SHERPA/VEDECOM sensors model, the emphasis is to ensure a reliable inter-distance measurement to the controller in both faultless and faulty scenarios. The simulation results show the effectiveness of the designed algorithms to provide fault tolerance;nonetheless, some disparities are noticed between the voting algorithms in terms of performance.

关键词： voting algorithms Fault Tolerant Control Vehicle Perception Sensors

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On the adaptability of ensemble methods for distributed classification systems: A comparative analysis

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第7期15卷 1-19页

作者： Villaverde, Monica Aledo, David Perez, David Moreno, Felix Univ Politecn Madrid Ctr Elect Ind Escuela Tecn Ingenieros Ind Jose Gutierrez Abascal 2 E-28006 Madrid Spain

In this work, a two-stage architecture is used to analyze the information collected from several sensors. The first stage makes classifications from partial information of the entire target (i.e. from different points of view or from different kind of measures) using a simple artificial neural network as a classifier. In addition, the second stage aggregates all the estimations given by the ensemble in order to obtain the final classification. Four different ensembles methods are compared in the second stage: artificial neural network, plurality majority, basic weighted majority, and stochastic weighted majority. However, not only reliability is an important factor but also adaptation is critical when the ensemble is working in changing environments. Therefore, the artificial neural network and the plurality majority algorithm are compared against our two proposed adaptive algorithms. Unlike artificial neural network, majority methods do not require previous training. The effects of improving the first stage and how the system behaves when different perturbations are presented have been measured. Results have been obtained from two applications: a realistic one and another simpler one, with more training examples for a more accurate comparison. These results show that artificial neural network is the most accurate proposal, whereas the most innovative proposed stochastic weighted voting is the most adaptive one.

关键词： Ensemble methods wireless sensor networks artificial neural networks voting algorithms classifier ensemble

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Speed Sensor Fault-Tolerant Controller for Induction Motor Using New Minimum Probability Voter Based on Signal Strength

Speed Sensor Fault-Tolerant Controller for Induction Motor U...

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International Conference on Applied Automation and Industrial Diagnostics (ICAAID)

作者： Fadi Alyoussef Ahmad Akrad Electrical and Electronics Eng. Dept. Dicle University Diyarbakır Turkey Quartz Laboratory ENSEA Cergy France

ISBN: (纸本)9781728128757

关键词： Fault tolerant control Back-Stepping controller voting algorithms Sliding mode observer Extended kalman filter sliding mode observer fault-tolerant control Signal Intensity speed sensor induction motors Extended Kalman filters incremental encoder Speed Estimation control of movement Fault tolerance rotor resistance

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Comparative tests of decision making algorithms for a multiversion execution environment of the fault tolerance software

Comparative tests of decision making algorithms for a multiv...

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European Conference on Electrical Engineering and Computer Science (EECS)

作者： Kovalev, Igor Voroshilova, Anna Losev, Vasiliy Saramud, Mikhail Chuvashova, Maria Medvedev, Aleksandr Reshetnev Siberian State Univ Sci & Technol Krasnoyarsk Russia

ISBN: (纸本)9781538620854

many areas are currently experiencing rapid growth, requiring reliable and fault-tolerant software in general and, in particular, management systems. The most effective today is multiversion programming, one of the approaches to improve the reliability of software by introducing redundancy. With this approach, there arises the problem of choosing the right variant from the collection by the answer of multiple versions, which is solved by various decision algorithms, most often based on voting. The reliability of this solution greatly affects the reliability of the redundant system as a whole. The article considers existing decision-making algorithms in multiversion execution environments and suggests own modifications of existing voting algorithms and t / (n-1) algorithm. A software implementation of the simulation environment that implements simulations of versions with specified characteristics and proposed modified algorithms is considered. The results of the simulation are considered, the dependence of the reliability indicators of the system on its input parameters is shown, a comparative analysis of various decision algorithms is made on the basis of simulation results. The obtained results show the effectiveness of the proposed modifications of the voting algorithms by an agreed majority and the specificity of the t / (n-1) decision algorithm, which makes sense to apply not in all systems. Most importantly, the simulation results show the possibility of creating a reliable system from not reliable software components, which is very important, especially for areas where there are currently no highly reliable algorithms or software components.

关键词： NVP NVS fault tolerance classification reliability voting algorithms simulation modeling NVX

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