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9th International Electrical engineering Congress, iEECON 2021

作者： Worasutr, Aman Worasawate, Denchai Pongthavornkamol, Tiwat Fukawa, Kazuhiko Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronic and Computer Technology Center Location and Automatic Identification System Research Team Pathum Thani Thailand Tokyo Institute of Technology Department of Communications and Computer Engineering Tokyo Japan

ISBN: (纸本)9781728195841

This paper proposes the improved indoor human detection algorithm from data provided by W-Band FMCW radar by using k-mean technique. The data provided by the radar contain both humans and nun-human objects. To identify non-human objects, the obtained data are filtered by the velocity and RCS of the detected objects. The K-means algorithm is applied to the filtered dataset to estimate the positions of humans. The estimated positions of humans are much closed to the actual ones. © 2021 IEEE.

关键词： K-means clustering

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Synthesis of Dynamic Virtual-Inertia for Microgrid system Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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Intelligent control, Computing and communications (IC3), International Conference on

作者： Jackson J. Justo Galina Demidova Dmitry Lukichev Nikolai Poliakov Peter Makolo Ramesh C. Bansal Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Department of Electrical Engineering University of Dar es Salaam Dar es Salaam Tanzania Depatment of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied.

关键词： Time-frequency analysis Renewable energy sources Power system dynamics Microgrids Power system stability Inverters Power system reliability Reliability Frequency control Power generation

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Detection of S100 Calcium Binding Protein A6 by Silicon Nitride Photonic Sensor

Detection of S100 Calcium Binding Protein A6 by Silicon Nitr...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Udomsom, Suruk Rattakorn, Punramon Mankong, Ukrit Umezawa, Toshimasa Matsumoto, Atsushi Kanno, Atsushi Yamamoto, Naokatsu Mendez-Astudillo, Manuel Atthi, Nithi Yamwong, Wittawat Intuyod, Kitti Pinlaor, Somchai Kawanishi, Tetsuya Imai, Kazuo Chiang Mai University Biomedical Engineering Institute Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand National Institute of Information and Communications Network System Research Institute Tokyo Japan Ansys Inc. Tokyo Japan Thai Microelectronics Center National Electronics and Computer Technology Center Chachoengsao Thailand Khon Kaen University Faculty of Medicine Department of Pathology Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Parasitology Khon Kaen Thailand Waseda University Department of Electronic and Physical Systems Tokyo Japan Imai Engineering Tokyo Japan

ISBN: (纸本)9781665402064

The detection of S100 Calcium Binding Protein A6, a useful cholangiocarcinoma marker, has been demonstrated for the first time using silicon nitride photonic resonator sensor. The sensor was designed and fabricated by low-cost 500 nm technology. Label-free sensing results based on the reactions between anti-S100A6 antibody and S100A6 antigen, showed good sensitivity and specificity. This sensing technique has potential in low-cost and low-level detection of the biomarker, deserving further study. © 2022 IEEE.

关键词： Silicon nitride

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. El-Samie, Fathi E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

关键词： Emotion Recognition

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A Model Predictive Current control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Maryam Sarebanzadeh Mohammad Ali Hosseinzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Academica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment.

关键词： Current control Reactive power Total harmonic distortion Simulation Switches Cost function Transformers Matrix converters Voltage control Predictive control

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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