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iEnergy 2023年第1期2卷 63-70页

作者： Fei Feng Yi-Fan Zhou Peng Zhang Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA

Quantum power flow(QPF)offers an inspiring direction for overcoming the computation challenge of power flow through quantum ***,the practical implementation of existing QPF algorithms in today’s noisy-intermediate-scale quantum(NISQ)era remains limited because of their sensitivity to *** paper establishes an NISQ-QPF algorithm that enables power flow computation on noisy quantum *** main contributions include:(1)a variational quantum circuit(VQC)-based alternating current(AC)power flow formulation,which enables QPF using short-depth quantum circuits;(2)NISQ-compatible QPF solvers based on the variational quantum linear solver(VQLS)and modified fast decoupled power flow;and(3)an error-resilient QPF scheme to relieve the QPF iteration deviations caused by noise;(3)a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum *** simulation tests validate the accuracy and generality of NISQ-QPF for solving practical power flow on IBM’s real,noisy quantum computers.

关键词： Quantum AC power flow quantum computing variational quantum linear solver fast decoupled load flow noisy-intermediate-scale quantum device

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A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 727-743页

作者： Dajun Du Minggao Zhu Xue Li Minrui Fei Siqi Bu Lei Wu Kang Li Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Electronic and Electrical Engineering University of LeedsLeeds LS29JTUK IEEE

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and *** timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense ***,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are *** malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are ***,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are ***,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively ***,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

关键词： Cyber-physical power systems security threat attack detection attack defense state estimation machine learning

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of Electronics and Information Engineers.

关键词： Cavity resonators

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Mathematical Modeling and Analysis of Permanent Magnet Synchronous Motor in Quaternion Space 12

Mathematical Modeling and Analysis of Permanent Magnet Synch...

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12th International electrical engineering Congress, iEECON 2024

作者： Jiratthivarutkul, Kreetha Pongyart, Witthawas Faculty Of Engineering Department Of Electrical And Computer Engineering 1518 Pracharaj 1 Bangsue Bangkok Thailand

ISBN: (纸本)9798350383591

This paper presents an innovative method for the mathematical modeling of Permanent Magnet Synchronous Motors (PMSMs) using quaternion space. Traditional modeling methods typically assume a balanced three-phase voltage source, enabling the representation of the motor's rotation in a two-dimensional dq-space vector, with the third component set to zero. However, these conventional approaches prove inadequate when confronted with an unbalanced source, as the third component becomes non-zero, necessitating a three-dimensional model for precision. To address this challenge, quaternion space is utilized to formulate a mathematical model of the PMSM in three-dimensional space. In this study, all signals such as voltage, current, and flux are expressed as pure quaternions, and rotations in three-dimensional space are executed through quaternion multiplication. As a result, a mathematical model of the Permanent Magnet Synchronous Motor (PMSM) is formulated within the realm of quaternion space. The construction of this quaternion-based model is intricately explained, providing a comprehensive representation of crucial aspects, including stator voltage, stator-rotor interactions, and critical parameters such as torque and power consumption. Simulation results demonstrate that the PMSM model based on quaternions exhibits behavior consistent with traditional models when subjected to a balanced three-phase voltage source. Notably, the developed model demonstrates adaptability in accurately representing the motor's response even in the presence of unbalanced stator voltage sources. © 2024 IEEE.

关键词： Vector spaces

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Comparative Analysis of Diverse Architectures for Accurate Blood Cancer Cell Classification

Comparative Analysis of Diverse Architectures for Accurate B...

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2024 International Conference on computer, electrical and Communication engineering, ICCECE 2024

作者： Md. Muttakin, G.K. Yesmin, Farhana Alam, Syeda Sadia Ashraf, Md Sadi Akuthota, Vishwanath Quader, Montaser Abdul Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering India Metropolitan University Department of Computer Science and Engineering United Kingdom Brac University Department of Computer Science and Engineering Bangladesh Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350386479

Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific type and subtype of blood cancer as well as efficient treatment planning. It provides specific information, helping healthcare professionals predict the prognosis, survival rates, and the potential for disease recurrence. On this basis, deep learning models have demonstrated remarkable performance. In this paper, we have introduced a comprehensive research into blood cancer cell classification, employing a diverse dataset encompassing various blood cell types. We explore the effectiveness of VGG19 with batch normalization, Vision Transformer (ViT), Ensemble Adversarial Inception-ResNetV2, DenseNet201, and ResNeXt50 architectures in this challenging task. In order to enhance the model performance, we integrate essential data preprocessing techniques, including resizing, cropping, and normalization. Additionally, novel data augmentation strategies, such as random cropping, and flipping are introduced to augment the training dataset and improve model generalization. Remarkably, VGG19 with batch normalization has shown tremendous success by achieving 99.86% accuracy. Moreover, the DenseNet201 model has performed brilliantly by achieving an accuracy of 98.55%. The ResNeXt50 model shows excellent performance with an accuracy of 98.31%. The Vision Transformer (ViT) achieves a solid accuracy of 98.91%. Lastly, Ensemble Adversarial Inception-ResNet V2 also performed no-tably achieving 96.38%. In this context, VGG19 with batch normalization is able to show more excellent performance than other models by achieving the highest accuracy. © 2024 IEEE.

关键词： Cancer cells

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Single-Phase Direct PWM Bipolar Buck–Boost AC–AC Converter Without Commutation Problem

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第6期11卷 5940-5953页

作者： Ahmed, Hafiz Furqan Ali Khan, Ashraf Abdoli, Iman Zaabi, Omar Al the Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan the Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X7 Canada the Department of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

— The direct pulsewidth modulation (PWM) ac–ac converters are seeing rapid development due to their single-stage power conversion with reduced footprints, due to the elimination of intermediate dc-link capacitor. However, they only provide unipolar voltage gain and fixed frequency operation and use the bidirectional voltage-blocking devices without natural inductor current free-wheeling path, restricting their practical applications. This article proposes a direct PWM bipolar and step-variable frequency buck–boost ac–ac converter. The proposed converter is fabricated by connecting input–output LC filters and a small value film capacitor to six unidirectional voltage-blocking devices, which are arranged as a three-leg inverter module. It can provide noninverting and inverting buck–boost voltage operations with a symmetric bipolar voltage gain of ±d/(1 − d) and can also change the frequency of output voltage in discrete steps, same as that of single-phase matrix converters (MCs). It has no open-circuit risk of inductor due to the presence of natural current free-wheeling path during PWM dead time. Therefore, it is unaffected by the commutation problem of conventional direct ac–ac converters. The proposed converter can realize high-quality input and output due to the presence of second-order input–output filters with continuous input–output currents. A detailed analysis is provided based on proposed switch modulation strategies and confirmed through extensive experimental tests on a laboratory-fabricated hardware. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Matrix converters

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： Syed Kamran Haider Abbas Ahmed Noman Mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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Triboelectric Nanogenerators (TENGs) Based on Various Flexible Polymeric Materials Along With Printed and Non-Printed Electrodes

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第2期2卷 92-100页

作者： Ali, Sam Palaniappan, Valliammai Maddipatla, Dinesh Panahi, Masoud Bazuin, Bradley J. Atashbar, Massood Z. Western Michigan University Department of Electrical and Computer Engineering KalamazooMI49008 United States

Four designs (D1-D4) of flexible triboelectric nanogenerators (TENGs) were successfully fabricated in three different configurations for energy harvesting applications. Typically, TENG consists of anode layer, cathode layer, and electrodes. Combination of materials were investigated in this study using polyethylene terephthalate (PET), thermoplastic polyurethane (TPU) and a flexible fabric as top triboelectric (cathode) layers;Kapton and polydimethylsiloxane (PDMS) as bottom triboelectric (anode) layers. Copper (Cu) tape and screen-printed silver (Ag) were used as electrodes. Each of these designs (D1-D4) has three configurations C1-C3 in which the top substrate (cathode layer) was different from one configuration to other. In order to determine the best material combination that would provide better TENG performance, different configurations were considered. The capability of the TENGs in terms of open circuit voltage (Voc) and short circuit current (Isc) were demonstrated by subjecting them to ∼ 5 -6 N force at 10 Hz operating frequency. A maximum Voc of 21.61 ± 2.27, 35.32 ± 0.89, 31.21 ± 1.43, and 45.75 ± 5.09 Vpp resulted in a power density of 0.29, 1.12, 0.47, and 0.91μW/cm2 from the D1-D4 designs, respectively. The TENG fabricated using screen printed Ag flake on PDMS and fabric (D4) exhibited the highest open circuit output and a good repeatability after six months of re-testing with a maximum difference of 7.39% change (Δ V/V) in the output signal. In addition, a cyclic test was performed on D4, the results obtained demonstrate a good durability of the TENG sensor over 1000 cycles. The fabricated TENG device has a simple design, compact size, and demonstrated a better performance in comparison with some of the other works in terms of output voltage. © 2022 IEEE.

关键词： Substrates

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Real-Time Lie-Speech Determination Using Voice-Stress Technology

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第2期5卷 81-93页

作者： Al-Dhaher, Fadi K. Mohammed, Duraid Y. Khalaf, Mohammed Al-Karawi, Khamis A. Sarfraz, Mohammad Al Maathidi, Muhammad Mazin Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq General Directorate of Education Anbar Ramadi31001 Iraq Computer Science Department Al-Maarif University College Ramadi31001 Iraq University of Diyala Iraq Electrical Engineering Department Aligarh Muslim University Aligarh India Computer Science Department University of Bahrain Bahrain

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena

关键词： National security

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