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Modeling and Simulating more efficient and reliable fuel cell vehicles through enhancing the integration of the 6kW PEM fuel cell stack with DC/DC boost converter and BLDC motor 33

Modeling and Simulating more efficient and reliable fuel cel...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Mtumka, Olona Krishnamurthy, Senthil Cape Peninsula University of Technology Bellville Center for Intelligent Systems and Emerging Technologies Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

This study focuses on enhancing the integration of the PEM fuel cell, DC/DC boost converter, and BLDC motor, which help to develop more efficient and dependable fuel cell vehicles. The development of renewable energy sources for power generation has received much attention due to the depletion of fossil fuel supplies worldwide and economic difficulties. Fuel cells are one of the most efficient and cleanest power generation methods. Polymer electrolyte membrane (PEM) fuel cell is the preferred type of fuel cell for vehicles because of its high energy density, fast start-up, and lack of pollution. This paper aims to model and simulate a PEM fuel cell stack to power a Brushless DC (BLDC) motor for a fuel cell vehicle to reduce emissions in the transportation sectors. A boost converter is implemented to boost the output voltage of the PEM fuel cell stack to 100 V. The paper utilizes the trapezoidal commutation of the BLDC motor to drive the inverter. The trapezoidal commutation uses the six-switching technique to control the inverter's phase currents. The variation of fuel flow rate and drive cycle are included to observe the effect on the operation of the fuel cell stack. The paper presents the modeling and simulation of the PEM Fuel cell stack to improve power conversion, energy management, and vehicle control, helping to make fuel cell vehicles more viable for widespread adoption in the future. © 2025 IEEE.

关键词： Brushless DC motors

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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intelligent Dynamic Inversion Controller Design for Ball and Beam System

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computers, Materials & Continua 2022年第8期72卷 2341-2355页

作者： Ibrahim M.Mehedi Abdulah Jeza Aljohani Md Mottahir Alam Mohamed Mahmoud Mohammed J.Abdulaal Muhammad Bilal Waleed Alasmary Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Electrical and Computer EngineeringTN38505United States Computer and Information Systems Umm Al-Qura UniversityMakkahSaudi Arabia

The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable *** an effective ball and beam system controller is a real challenge for researchers and *** this paper,the control design technique is investigated by using intelligent Dynamic Inversion(IDI)method for this nonlinear and unstable *** proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in *** Moore-PenroseGeneralized Inverse(MPGI)is used to invert the prescribed constraint dynamics to realize the baseline control law.A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties,nonlinearities,and external *** semi-global asymptotic stability of IDI control is guaranteed in the sense of *** simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the *** addition to that,comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.

关键词： Ball and beam system(BBS) unstable system internal model PD controllers IDI control robust control

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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IEEE Transactions on Automation Science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Real-time Architecture for Audio-Visual Active Speaker Detection

Real-time Architecture for Audio-Visual Active Speaker Detec...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Huang, Min Wang, Wen Lin, Zheyuan Tesema, Fiseha B. Ji, Shanshan Gu, Jason Wan, Minhong Song, Wei Li, Te Zhu, Shiqiang The Intelligent Robotics Research Center Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University Canada

ISBN: (纸本)9781665481090

Continuously measuring the speaking state of users with robot in a human-robot Interaction(HRI) system improves metrics of interaction quality. Meanwhile, mainstream active speaker detection (ASD) algorithms emphasize achieving high AUCs at frame level in the AVA-Active Speaker dataset and pay less attention to get real-time performance in robotic systems. In this paper, we propose a model named FSDNet to keep a high AUC score in the AVA-Active Speaker dataset while reducing time cost, our model increase AUC score by 0.1% compared with the State-Of-The-Art and need only 75% running time. Furthermore, we put forward an architecture with a time-related prediction function to make our algorithm more effective and generative in interactive robotic systems. The code is released at https://***/huangmin9966/FSDNet-RealTimeArch. © 2022 IEEE.

关键词： Human robot interaction

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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

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Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee Woon‑Hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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