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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Bhadula, Shuchi Alabdeli, Haider Badhoutiya, Arti Deepak, A. Bhardwaj, Nitin Anitha, G. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research investigates the use of reinforcement learning (RL) algorithms for optimal control systems in electrical engineering. The article discusses four popular RL algorithms - Q-learning, Deep Q network (DQN), as well as Policy Gradient Methods (PGM) and Proximal Policy Optimization (PPO) density. A simulation in an electric control system was performed to assess these strategies. The proposed research was designed and made with the intention of improving adaptivity, delays in responding to time actions on adopting circumstances such as UAV placement navigation paradigm, wave energy conversions, there is an aspect of HVAC systems around water circulation networks control. The results demonstrated the robustness of DQN and PPO, with system efficiency values of 0.92 and 0.91, response times of 0.08s and 0.09s, respectively, and in that case they were specific circumstances showcasing stable behavior system compared to training on an experience replay memory basis Comparative analysis with relative work highlighted the competitive performance of RL algorithms as compared to other methods, which further validated their practical application. Sensitivity analysis demonstrated the DQN and PPO adaptation to a change in influential factors, proving that they are capable of functioning under changing conditions. The computational efficiency analysis showed that DQN and PPO had faster convergence speeds compared to Q-learning and Policy Gradient Methods. According to this research, RL offers vital bits of knowledge into the transformative potential that these algorithms can bring to electrical engineering and in this way make them successful instruments for versatile networks. © 2024 IEEE.

关键词： Reinforcement learning

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Advancing Electric Vehicle Motor Design Optimization - Overview of a Novel Approach for Fault Characterization 4

Advancing Electric Vehicle Motor Design Optimization - Overv...

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4th International Conference on electrical Machines and Drives, ICEMD 2024

作者： Faiz, Jawad Nassiri, Mohammad Mahdi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331507510

Nowadays, the worldwide automotive industry has placed great emphasis on the expansion of electric vehicles (EVs), leaving no room for doubt. Data from numerous esteemed research institutions and statistical centers unequivocally demonstrate the environmental benefits of these vehicles. The distinguishing factors of EVs are their electric power units and propulsion systems, which set them apart from combustion engine-based vehicles. Currently, comprehensive studies are being conducted on various aspects of EVs, including design and development, materials, production, processing, operation, and recycling. Research areas include charging technologies, the development of reliable and high-capacity rechargeable cells, the design of lightweight and powerful electric motors, and the use of improved devices in power electronics circuits. This paper aims to investigate an approach for design optimization of electric motors employed in EVs. These proposed solutions aim to strengthen the mechanical, electromagnetic, and thermal structure of these motors, enabling them to withstand expected stresses and minimizing the occurrence of faults. © 2024 IEEE.

关键词： Traction motors

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Sharing AES Engine for RISC-V Custom Instructions Performing Encryption and Decryption

Sharing AES Engine for RISC-V Custom Instructions Performing...

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East-West Design & Test Symposium (EWDTS)

作者： Zahra Hojati Zahra Jahanpeima Maryam Rajabalipanah Hossein Ta'ati Atefe Rabiei Zainalabedin Navabi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

In light of the critical role of cryptography in safeguarding data and the advantages of AES for hardware implementation, this paper presents a hardware implementation of the 128-bit Advanced Encryption Standard (AES) algorithm. We designed an AES unit that utilizes the same hardware for both encryption and decryption, which we integrated into a RISC-V-based processor, by developing four custom instructions to facilitate this integration. Our methodology leverages the inherent parallelism of the AES algorithm to optimize speed, enhance security features, and ensure low power consumption along with efficient resource utilization. A comprehensive performance analysis showed a nearly 19x speedup compared to a software implementation, demonstrating significant performance and efficiency benefits.

关键词： Power demand Software algorithms Parallel processing Hardware Software Encryption Performance analysis Resource management Logic Standards

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Two-dimensional bilayer phosphorenes: a tunable platform for nanoscale heat energy transfer

Two-dimensional bilayer phosphorenes: a tunable platform for...

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Technology and Energy Management (ICTEM), International Conference on

作者： Zahra Valiollahi Mahyar Dehdast Mohamad Neshat School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350329797

ISBN: (纸本)9798350329803

Active control of heat energy transfer as the key factor in thermal management at nanoscale, has recently attracted increasing attention. Relying on intrinsic in-plane anisotropy and tunable electronic and optical properties, the monolayer of black phosphorus called phosphorene, has exhibited great potential in near-field radiative heat transfer (NFRHT). The results show that the imaginary part of optical conductivity is enhanced one-order of magnitude as electron density increases from $n=5 \times 10^{12} / \mathrm{cm}^{2}$ to $n=20 \times 10^{12} / \mathrm{cm}^{2}$. Herein, the dynamic control of NFRHT is analyzed in the bilayer phosphorene system. We studied the tunable NFRHT between two phosphorene layers under different doping concentrations, realizing NFRHT is dominant in lower doping levels. For the energy of 0.05 eV, when the doping concentration decays a quarter, the phosphorene sheet would be extended toward a high-k wavevector over 2 times. Moreover, it is found that decreasing the vacuum gap between the bilayer can result in the dramatic increase of the NFRHT rate. This work can open up a new way to utilize anisotropic two-dimentional materials for dynamic management of heat energy transfer at the nanoscale.

关键词： Thermal management of electronics Energy exchange Doping Plasmons Optical imaging Geometrical optics Electron optics

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Condition Monitoring of Induction Motors using Fiber Bragg Grating Sensors 4

Condition Monitoring of Induction Motors using Fiber Bragg G...

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4th International Conference on electrical Machines and Drives, ICEMD 2024

作者： Dashti, Mahdi Faiz, Jawad Rezazadeh, Ghasem Samimi, Mohammad Hamed College of Engineering University of Tehran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331507510

This paper presents a method for condition monitoring and operating conditions of induction motors using Fiber Bragg Grating (FBG) strain sensors. The approach leverages a detailed simulation workflow using ANSYS Multi-physics and COMSOL Multi-physics to accurately capture and assess motor performance under normal operating conditions. Initially, a magnetic field intensity is generated within the induction motor using ANSYS Electronics to stimulate a magneto-strictive material, which is subjected to mechanical forces. Subsequently, ANSYS mechanical simulation calculates the resulting strain and deformation within the magneto-strictive material. This strain data is then applied to an FBG sensor modeled in COMSOL Multi-physics optically to simulate and analyze the Bragg wavelength shift over time, providing a reliable indicator of motor health. The combined use of these simulation platforms enables a precise, non-invasive, and continuous monitoring system that leverages the sensitivity of FBG sensors to detect potential deviations from healthy operation, ensuring timely detection of abnormalities and facilitating preventive maintenance in industrial applications. © 2024 IEEE.

关键词： Fiber Bragg gratings

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EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

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Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon Al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

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Beyond Linear Binning: Logarithmic Insights for Calibrated Machine Learning in Wireless Systems 100

Beyond Linear Binning: Logarithmic Insights for Calibrated M...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Raina, Rashika Simmons, Nidhi Simmons, David E. Yacoub, Michel Daoud Queen's University Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Dhali Holdings Ltd. BelfastBT5 7HW United Kingdom University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

ISBN: (纸本)9798331517786

In this paper, we explore the calibration of a machine learning (ML)-based outage predictor aimed at optimizing resource allocation to minimize outages in communication systems. We model the wireless channel using an auto-correlated time series with samples distributed in accordance with a Rayleigh fading process. Our novel contribution concerns proposing histogram-based reliability plots that employ logarithmic binning to assess its impact on the model calibration compared to the traditional linear binning technique. We train our ML model using an outage loss function (OLF) tailored to this system and the well-known binary cross entropy (BCE). Additionally, we analyze the effect of different model parameters on the calibration performance of this outage predictor. Our findings demonstrate that logarithmic binning reveals nuanced calibration traits ignored by linear binning, particularly at lower confidence levels. This finding is crucial for wireless systems for which understanding behavior at very low probabilities is essential. Additionally, we observe that our ML model trained with OLF becomes more overconfident as the classification threshold increases and in scenarios characterized with rare events, namely outages. This observation serves as a tool for improving the calibration properties of OLF. © 2024 IEEE.

关键词： Resource allocation

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A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

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Research 2023年第3期2023卷 105-115页

作者： Zi Hao Guo ZiXuan Zhang Kang An Tianyiyi He Zhongda Sun Xiong Pu Chengkuo Lee Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400People's Republic of China School of Nanoscience and Technology University of Chinese Academy of SciencesBejing 100049People's Republic of China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore School of Mechanical and Materials Engineering North China University of TechnologyBeijing 100144People's Republic of China

Regular exercise paves the way to a healthy ***,conventional sports events are susceptible to weather *** motion sensors for home-based sports are mainly limited by operation power consumption,single-direction sensitivity,or inferior data ***,by leveraging the 3-dimensional printing technique and triboelectric effect,a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement *** integrating with a belt,this sensor could be used to identify some low degree of freedom motions,e.g.,waist or gait motion,with a high accuracy of 93.8%.Furthermore,when wearing the sensor at the ankle position,signals generated from shank motions that contain more abundant information could also be effectively *** means of a deep learning algorithm,the kicking direction and force could be precisely differentiated with an accuracy of 97.5%.Toward practical application,a virtual reality-enabled fitness game and a shooting game were successfully *** work is believed to open up new insights for the development of future household sports or rehabilitation.

关键词： integrating printing Regular

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

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Verification of Digital Twins using Classical & Statistical Model Checking 1

Verification of Digital Twins using Classical & Statistical ...

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1st International Workshop on Autonomous Systems Quality Assurance and Prediction with Digital Twins, ASQAP 2025

作者： Gunasekaran, Raghavendran Haverkort, Boudewijn Tilburg University Tilburg School of Humanities and Digital Sciences Tilburg Netherlands University of Twente Faculty for Electrical Engineering Mathematics and Computer Science Enschede Netherlands

With the increasing adoption of digital techniques, the concept of digital twin (DT) has received a widespread attention in both industry and academia. While several definitions exist for a DT, most definitions focus on the existence of a virtual entity (VE) of a real-world object or process, often comprising interconnected models which interact with each other, undergoing changes continuously owing to the synchronization with the real-world object. These interactions might lead to inconsistencies at execution time, due to their highly stochastic and/or time-critical nature, which may lead to undesirable behavior. In addition, the continuously varying nature of VE owing to its synchronization with the real-world object further contributes to the complexity arising from these interactions and corresponding model execution times, which could possibly affect its overall functioning at runtime. This creates a need to perform (continuous) verification of the VE, to ensure that it behaves consistently at runtime by adhering to desired properties such as deadlock freeness, functional correctness, liveness and timeliness. Some critical properties such as deadlock freeness can only be verified using classical model checking;on the other hand, statistical model checking provides the possibility to model actual stochastic temporal behavior. We therefore propose to use both these techniques to verify the correctness and the fulfillment of desirable properties of VE. We present our observations and findings from applying these techniques on the DT of an autonomously driving truck. Results from these verification techniques suggest that this DT adheres to properties of deadlock freeness and functional correctness, but not adhering to timeliness properties. © *** & ***.

关键词： Model checking

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