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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Guha, Ritam Deb, Kalyanmoy Michigan State University Computer Science and Engineering Department East Lansing United States Michigan State University Electrical and Computer Engineering Department East Lansing United States

ISBN: (纸本)9798350308365

The goal of a generic evolutionary multi- or many-objective algorithm is to explore a search space and find the trade-off optimal solutions for two or more conflicting objectives. In platform-based practical design optimization problems, it is not sufficient to just find a set of trade-off optimal solutions, certain regularity properties are expected in the entire fleet of trade-off solutions. For this purpose, we propose a scalable regularity-based optimization framework - RegEM(a)O - which automatically extracts polynomial regularity principles from the resulting Pareto-optimal front of multi- or many-objective problems. Thereafter, it attempts to search for a regular front of trade-off solutions following a similar form of polynomial regularity principles. Despite being slightly worse than the true Pareto-optimal solutions, regular solutions possess simple properties among them, making them easily interpretable, their inventory easily maintainable, and easily scalable. In this paper, we apply RegEM(a)O to a number of small and large-scale real-world engineering design problems to demonstrate its practical advantage. © 2024 IEEE.

关键词： Optimization algorithms

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V2G-Auth: Lightweight Authentication and Key Agreement Protocol for V2G Environment Leveraging Physically Unclonable Functions

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 66-78页

作者： Reddy, Alavalapati Goutham Babu, Ponnuru Raveendra Odelu, Vanga Wang, Li Kumar, Sathish A.P. Department of Mathematics and Computer Science Fontbonne University St. LouisMO63105 United States Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh 534101 India Department of Electrical Engineering and Computer Science Cleveland Sate University ClevelandOH44115 United States Department of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City517646 India

Electric vehicle (EV) is a fast-growing technology that could help reduce greenhouse gas emissions in the energy and transportation sectors. The characteristics of EVs allow for rapid smart city expansion and serve as a key weapon in the emergence of vehicle-to-grid (V2G) technology. The entities, such as EVs and charging stations, connect with one another in order to gather data about the status of the EV and the environment in which it is traveling. The exchange of information among entities in V2G over a public channel may be exposed to security threats. The user’s devices are also vulnerable to physical attacks since they are left unprotected at their end. Hence, we suggest a lightweight protocol for key agreement and mutual authentication between entities operating in a V2G environment. Additionally, we assess the proposed scheme using the Scyther tool to conduct formal and informal security analyses in order to demonstrate how effectively it can counteract known security attacks. Finally, the performance study reveals that the suggested protocol provides a communication and computational benefit when compared to relevant protocols. © 2023 Institute of electrical and Electronics Engineers. All rights reserved.

关键词： Vehicle-to-grid

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms 6

Optimizing QoE in IoT-Based Video Streaming through Deep Lea...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350344349

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-Term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-Time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments. © 2024 IEEE.

关键词： Video streaming

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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A Deep Neural Network Approach for Fault Detection in Industrial Motors: Enhancing Reliability and Efficiency in Industry 4.0 21

A Deep Neural Network Approach for Fault Detection in Indust...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ali, Hassan Ahyen, Mostafa Yaqub, Raziq Badawi, Ahmed Shifa, Noora Khandakar, Amith University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Alabama A and M University Department of Electrical Engineering and Computer Science Huntsville United States Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350378078

In the era of Industry 4.0, the integration of sophisticated monitoring and control technologies is essential for the reliability and efficiency of industrial equipment. This paper presents a deep neural network (DNN)-based approach for fault detection and classification in industrial motors. The approach identifies common motor faults, including bent shaft, loose vibration, loosely mounted components, electrical faults, and bearing vibration. The proposed DNN model demonstrated robust performance, achieving an overall training accuracy of 83.49% ± 0.43% and a test accuracy of 81.91 % ± 0.82%. The model also achieved a mean precision of 82.05% ± 0.77%, mean recall of 81.91 % ± 0.82%, and mean F1-Score of81.86%± 0.78%. The 95% confidence intervals for accuracy, precision, recall, and F1-Score were (80.77%, 83.04%), (80.98%, 83.11 %), (80.77%, 83.04%), and (80.78%,82.95%), respectively. These results demonstrate the DNN model's ability to precisely classify various motor faults with minimal variability, as evidenced by its narrow confidence intervals and low standard deviations. The model's reliability and efficacy for real-world applications in proactive maintenance and fault detection are confirmed by the consistent performance that is observed across cross-validation folds. © 2024 IEEE.

关键词： Predictive maintenance

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Farmbot: An IoT-Based Wireless Agricultural Robot for Smart Cultivation 26

Farmbot: An IoT-Based Wireless Agricultural Robot for Smart ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Kar, Sudipto Mohammad, Ragib Zaman, Meem Tasfia Reza, Sayed Maher Talukder, Ovi Hasan, Mehadi North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

Smart technologies have significantly transformed various sectors, and agriculture is no exception. This paper aims to introduce a robot called "Farmbot"to revolutionize traditional farming techniques and enhance crop production. The construction of a radio frequency remote-controlled robot using Arduino microcontrollers and ultrasonic sensors is programmed to navigate by the farmer on the ground. The Farmbot aims to aid farmers by delivering valuable data through a smartphone app on moisture levels, soil's potential of hydrogen, temperature, humidity, and the presence of flammable gasses across distinct farm zones, all through a single set of sensors. It provides comprehensive insights into the overall land condition, enabling informed decisions regarding irrigation, fertilization, and the crucial steps required for optimal growth. To further enhance its utility and application, features such as fertilizer and insecticide dispersion and optional plowing functionality are incorporated into the robot to enhance agricultural efficiency, ensuring optimal crop health and pest control and saving labor. The experimental results will demonstrate the effectiveness of the Farmbot in assisting farmers with irrigation management, fertilization management, and automated farming tasks. © 2023 IEEE.

关键词： agriculture air quality humidity micro-controller moisture radio frequency robot sensors temperature

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation Systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Nanowatt Integrated Circuits and Systems

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IEEE NANOTECHNOLOGY MAGAZINE 2022年第3期16卷 2-3页

作者： Sheu, Bing Kao, Shao-Ku Electronics Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace Greece Computer Engineering Laboratory Electrical Engineering Mathematics and Computer Science Faculty Delft University of Technology Delft The Netherlands

The scaling of CMOS technology continues to follow Moore’s law through clever designs at the transistor, circuit, and system levels, from both manufacturing and packaging. The integration of 50 billion-plus transistors into a single silicon chip has become quite common. Power consumption is now one of the key design factors in advancing nanoelectronics. It is our great pleasure to introduce Prof. Kiat Seng Yeo and Prof. Chao-Sung Lai as the guest editors of this special issue of IEEE Nanotechnology Magazine .

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A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM Coding Method for IoT Applications 6

A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM C...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Iman, Khalid Ismail Boby, R.I. Mollik, Md. Sazib Chowdhury, Imran Rahman, Sahidur Ankur, Wobeydul Haq Abdullah, Khaizuran Stamford University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350385779

Business, education, and research have formed a strong connection with the Internet of Things (IoT), an open-access collective communication service offered by Internet-connected devices. Though it seems to be a good deal in terms of cost and functionality, it is still the most dangerous communication channel due to high hacking rates and the ease of high data loss. Even though contemporary software is becoming more sophisticated for data and security, it still needs greater protection in a real-time setting. Utilizing extensive security measures may increase safety, but doing so will jeopardize the real-time environment. This study introduces a highly efficient encryption framework that combines chaotic maps with the Adaptive Differential Pulse Code Modulation (ADPCM) coding technique. The proposed algorithm ensures real-time secure communication for Internet of Things (IoT) applications while complying with ITU-T Recommendation Point G.726 and ANSI T1.303-1989 standards. It supports ADPCM transcoding algorithms at connection speeds of 16, 24, 32, and 40 kbps. The chaotic map technique was selected for its robust encryption capabilities using random variables and ADPCM for its high energy economy in transcoding and is characterized using two parallel encode functions per frame, which may be executed by small memory. The proposed encryption algorithm will perform three main operations. Firstly, it will be able to generate chaotic values from distinct initial conditions using two chaotic logistic maps. Secondly, it will transform these values into binary tables using a random encoding condition. Finally, it will execute the basic proposed ADPCM process to enhance security further. © 2024 IEEE.

关键词： Cryptography

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