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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Zhihao He Duleepa J. Thrimawithana Martin Neuburger Grant A. Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

This paper proposes a three-phase asymmetrical amplitude digitized modulation (TP-AADM) scheme for a three-phase integrated boost multi-level converter (TP-IBMC) utilized on an inductive power transfer (IPT) system with a three-to-three coupler and the double-sided LCL tuning network. It can control these output voltages driving the coupler independently in order to generate balanced currents in the secondary coils or the rectifier with the misalignment situation while the conventional solutions require three separated H-bridge converters. A simulation model in accordance with the SAEJ2954 WPT3/Z3 indicates the performance in the misaligned IPT system.

关键词： Multilevel converters Modulation Rectifiers Couplers Wireless power transfer Rendering (computer graphics) Regulation Voltage control Tuning Standards

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A High-Band Compact Microstrip Patch Antenna for 5G Wireless Technologies 6

A High-Band Compact Microstrip Patch Antenna for 5G Wireless...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Santhakumar, G. Muthukumar, R. Asa Jyothi, G. Balakumar, S. Maranan, Ramya Bonde, Roshan Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Erode Sengunthar Engineering College Department of EEE Tamil Nadu Erode India Malla Reddy Engineering College ECE Department Maisamma Guda Telangana Hyderabad India Arba Minch University Faculty of Electrical & Computer Engineering Arba MinchPin: 21 Ethiopia Saveetha School of Engineering Department of Research and Innovation SIMATS Tamil Nadu Chennai India Dr. Babasaheb Ambedkar Technological University Lonere Raigad India

ISBN: (纸本)9798350304480

This examination presents a High Increase, Overhauled Bandwidth Patch antenna for 5G Undertakings. Using an inset-took care of taking care of method for the microstrip patch antenna wave band, the double band is achieved. Two rectangular apertures are embedded on the patch's transmitting component to provide it a high increase. The anticipated antenna Consolidating three stages at the edge of the rectangular patch increases bandwidth. Fifth-generation (5G) networks clearly outperform 4G because to reduced idleness, more transmission speed, greater bandwidth, and a higher possibility of integrating with more notable various devices. For impending 5G association technologies, this paper studies and presents a microstrip patch antenna working at 28 GHz. The outcome of this reenactment is superior to the previous one. It typically acts as a dependable choice for 5G wireless innovation as a result. The performance of the suggested antenna is superior to that of earlier antennas spread in logical diaries. © 2023 IEEE.

关键词： 5g High-Band Microstrip Patch Antenna Wireless Technologies

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Exploring the use of deep learning in task-flexible ILC

arXiv

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arXiv 2023年

作者： Vinjarapu, Anantha Sai Hariharan Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary Asml Veldhoven Netherlands

Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward *** Codes 37M15 © 2023, CC BY.

关键词： Feedforward control

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Enhancing Machine Learning Performance through Intelligent Data Quality Assessment: An Unsupervised Data-centric Framework

arXiv

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arXiv 2025年

作者： Rahal, Manal Ahmed, Bestoun S. Szabados, Gergely Fornstedt, Torgny Samuelsson, Jörgen Department of Mathematics and Computer Science Karlstad University Universitetsgatan 2 Karlstad65188 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague16627 Czech Republic Department of Engineering and Chemical Sciences Universitetsgatan 2 Karlstad65188 Sweden

Poor data quality limits the advantageous power of Machine Learning (ML) and weakens high-performing ML software systems. Nowadays, data are more prone to the risk of poor quality due to their increasing volume and complexity. Therefore, tedious and time-consuming work goes into data preparation and improvement before moving further in the ML pipeline. To address this challenge, we propose an intelligent data-centric evaluation framework that can identify high-quality data and improve the performance of an ML system. The proposed framework combines the curation of quality measurements and unsupervised learning to distinguish high- and low-quality data. The framework is designed to integrate flexible and general-purpose methods so that it is deployed in various domains and applications. To validate the outcomes of the designed framework, we implemented it in a real-world use case from the field of analytical chemistry, where it is tested on three datasets of anti-sense oligonucleotides. A domain expert is consulted to identify the relevant quality measurements and evaluate the outcomes of the framework. The results show that the quality-centric data evaluation framework identifies the characteristics of high-quality data that guide the conduct of efficient laboratory experiments and consequently improve the performance of the ML system. Copyright © 2025, The Authors. All rights reserved.

关键词： Data quality

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Exploring the Impact of RIS on Cooperative NOMA URLLC Systems: A Theoretical Perspective

arXiv

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arXiv 2024年

作者： Zheng, Jianchao Wu, Tuo Yao, Junteng Yuen, Chau Ding, Zhiguo Adachi, Fumiyuki School of Computer Science and Engineering Huizhou University Huizhou516000 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Tohoku University Sendai Japan

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user (CEU), respectively, and an RIS is deployed to enhance signal quality. Specifically, compared to CEU, CU lies nearer from BS and enjoys the higher channel gains. Closed-form expressions for the CU's average block error rate (BLER) are derived. Furthermore, we evaluate the CEU's BLER performance utilizing selective combining (SC) and derive a tight lower bound under maximum ratio combining (MRC). Simulation results are provided to our analyses and demonstrate that the RIS-assisted system significantly outperforms its counterpart without RIS in terms of BLER. Notably, MRC achieves a squared multiple of the diversity gain of the SC, leading to more reliable performance, especially for the CEU. Furthermore, by dividing the RIS into two zones, each dedicated to a specific user, the average BLER can be further reduced, particularly for the CEU. © 2024, CC BY.

关键词： Cooperative communication

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controlling the Movement Path of Leg Exoskeletons Through Fractional Order Sliding Mode control

Controlling the Movement Path of Leg Exoskeletons Through Fr...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Aicha Znidi Fatma Abdelhedi Ahmed Said Nouri Nabil Derbel Lab: Numerical Control of Industrial Processes National Engineering School of Gabes University of Gabes Gabes Tunisia Department of Electrical and Computer Engineering college of Engineering King Abdulaziz: University Jeddah Saudi Arabia National Engineering School of of Sfax University of Sfax

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

This research introduces a new method, Fractional Order Sliding Mode control (FOSMC), to manage leg exoskele-tons during gait rehabilitation. This innovative algorithm utilizes fractional calculus principles to precisely regulate the joints of the exoskeleton. By integrating fractional order dynamics, the controller effectively handles the complexities and uncertainties inherent in human walking patterns. This leads to more precise tracking, lower energy usage, and increased comfort for patients during rehabilitation. Additionally, the application of Lyapunov theory ensures the stability of the system. The effectiveness of this control method is assessed through simulation examples in the study.

关键词： Legged locomotion Uncertainty Tracking Heuristic algorithms Exoskeletons Medical services Stability analysis

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Unsafe at any Bandwidth: Towards Understanding Risk Factors for Ransomware in Higher Education

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Procedia computer Science 2024年 238卷 815-820页

作者： Logan Suarez Dakhilallah Alshubrumi Tj O’Connor Sneha Sudhakaran Department of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA Faculty L3Harris Institute for Assured Information Florida Institute of Technology Melbourne FL USA

United States higher education institutions host an assortment of services that are accessible via public IP addresses. The wide variety of network services and the important personal and institutional data stored on such services make higher education institutions particularly desirable targets for attackers. This study analyses the vulnerabilities found through Shodan scans on these networks, in conjunction with institutional characteristics data taken from the National Center for Education Statistics (NCES), to examine correlations between an institution’s characteristics and the vulnerabilities found on its networks. By exploring this data, the study aims to bring awareness to the current state of higher education institution network security and determine vulnerability trends between certain institutions. Our analysis reveals that most institutions have many medium impacts but highly exploitable vulnerabilities, with most being on Apache HTTP servers. We also present that the most significant indicators of an institution’s vulnerability are its enrolment and yearly total expenses. We investigate how smaller institutions have lower numbers of vulnerabilities, but their vulnerabilities have the potential for higher impact. We conclude that there is a significant chance of ransomware risk in US higher educational institutions.

关键词： Shodan Vulnerability Assessment Higher Education

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A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

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2025 International Conference on Energy, Power, Environment, control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of engineering & Technology 2025.

关键词： Deep learning

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Efficient voltage control strategy: observability design for multistage DC–DC buck converter

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Frontiers in Energy Research 2024年 12卷

作者： Albariqi, Khalid A. Alhussainy, Abdullah Ali Bawayan, Haneen Alturki, Yusuf A. Alghamdi, Sultan Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering College of Engineering University of Prince Mugrin Madinah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential. Copyright © 2024 Albariqi, Alhussainy, Bawayan, Alturki and Alghamdi.

关键词： Buck converter

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