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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Kameli, Sayed Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Guzinski, Jaroslaw Texas AandM University at Qatar Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdansk University of Technology Electrical and Control Engineering Faculty ul. Gabriela Narutowicza 11/12 Gdansk80-233 Poland

ISBN: (纸本)9798350300048

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity. © 2023 IEEE.

关键词： Power transformers

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Optimizing Energy Management and Load Balancing Through AI-Driven Quantum Approximate Optimization 4

Optimizing Energy Management and Load Balancing Through AI-D...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Manasa, M. Anitha, B.S. Mahadevi Tlajiya, Falguni Chavan, Sangramjit Department of Cyber Security Karkala India Brindavan College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Department of Electrical Engineering Gujarat India Pune India

ISBN: (纸本)9798350352931

The integration of Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. This study explores the application of these quantum computing techniques, coupled with Artificial Intelligence (AI), to optimize energy distribution across smart grids. The proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. The results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. The quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. These findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management. © 2024 IEEE.

关键词： Quantum computers

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Evaluating the Robustness of Neural Networks Against Adversarial Perturbations 2

Evaluating the Robustness of Neural Networks Against Adversa...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Bhat, C. Rohith Nandhini, J. Narayanasamy, S. Karthik, B. Lakineni, Prasanna Kumar Taqui, Syed Noeman Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai India Jai Shriram Engineering College Department of Ece Tirupur India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

N eural networks have impressive powers in the field of deep learning and have been used in many different applications. Nevertheless, the issue of robustness against deliberate harmful inputs, commonly referred to as adversarial perturbations, continues to be a prominent topic. The presence of these perturbations, which are frequently undetectable by human observers, has the potential to cause erroneous predictions in models, hence presenting potential hazards to crucial domains such as autonomous vehicles or medical diagnostics. The primary objective of this study is to assess the vulnerability of neural networks to adversarial perturbations, ascertain the underlying factors contributing to this susceptibility, and investigate viable solutions for mitigating such vulnerabilities. Various adversarial attack strategies were utilized on diverse neural network topologies, and their performances were assessed. The results of our study suggest that whereas deeper models exhibit enhanced classification abilities, they may not necessarily possess more resilience against hostile inputs. Additionally, it has been found that certain conventional regularization techniques might further amplify susceptibility. In order to address these problems, innovative training techniques and adjustments to the network structures are implemented, resulting in enhanced resilience against adversarial attacks. This study highlights the significance of developing neural networks that possess both accuracy and robustness, hence advancing the frontier of safe deep learning applications. © 2023 IEEE.

关键词： Adversarial Perturbations Deep Learning Neural Networks Robustness

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Smart Last-Mile Delivery with IoT-Connected Robot Vehicles 10

Smart Last-Mile Delivery with IoT-Connected Robot Vehicles

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sujatha, M. Kovarasan, Rajesh Kambattan Sudha, K. Mishra, Nilamadhab Thiyagarajan, G. Srinivasan, C. Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Business Administration Tamil Nadu Mannargudi India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350363104

This research discusses using IoT technologies with robot vehicles to improve last-mile deliveries. This novel method optimizes the last stage of the distribution chain, which sometimes presents logistical issues. The system examines robot vehicles' last-mile delivery monitoring, data collecting, and communication capabilities via an Internet of Things connection. These trucks collect and communicate traffic, packaging, and environmental data using sensors and communication equipment. Analyzing and processing this data improves route planning, delivery timing, and performance. Through the IoT, autonomous delivery trucks, a centralized control center, and other components of the delivery ecosystem could operate in combination. This collaborative network helps respond to changing situations such as highway congestion and package urgency by rerouting or raising delivery priority. The potential for delivery trucks to improve productivity, intelligence, and focus on the customer in the last mile of a package's journey. The IoT-driven solutions may be highlighted as major results, advantages, and implications for the logistics sector, addressing urbanization, consumer demands, and environmental concerns in modern delivery systems. © 2024 IEEE.

关键词： Trucks

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Micromagnetic Simulations of Concentration Detection Capability of Large-Area and Low-Aspect-Ratio GMR Sensors in Biomedical Diagnostics

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IEEE Transactions on Magnetics 2025年

作者： Mendonsa, Riyan Liang, Shuang Tonini, Denis Wang, Jian-Ping University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States University of Minnesota Department of Chemical Engineering and Materials Science MinneapolisMN55455 United States

Since the discovery of Giant Magnetoresistance(GMR), it has found many applications in sensors. One of these key areas is for biomedical sensing. GMR biosensors with large-area and low-aspect-ratio were demonstrated experimentally with ultra high sensitivity, followed by a relatively simplified theoretical explanation. This study presents a systematic micromagnetic simulation analysis of large-area and low-aspect-ratio GMR sensors, with a particular focus on two key regimes: the linear portion before saturation by the magnetic nanoparticle(MNP) interaction with the sensor and the flattening out due to sensor saturation. This study presents a simulation-based analysis of large-area and low-aspect-ratio GMR sensors using micromagnetic simulations for biomedical diagnostic applications. We investigate various MNP types and sizes. Through systematic simulations, we compare sensitivity to concentration and highlight the influence of MNP characteristics and concentration on the large-area GMR sensor's performance. This study not only furthers the understanding of large-area and low-aspect-ratio GMR sensor behavior, but also paves the way for future targeted experimental research and development in this promising field. © 1965-2012 IEEE.

关键词： Aspect ratio

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Analysis of a Transformerless High Step-Down DC-DC Converter

Analysis of a Transformerless High Step-Down DC-DC Converter

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Truong, Phan Nhat Anh Dung, Nguyen Liu, Yu-Chen Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering Blacksburg United States National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350316445

The analysis and performance of a proposed high step-down DC-DC converter without employing transformers are discussed in this article. The operating principle of the control signals in this proposed converter is similar to the traditional two-phase interleaved buck converter, resulting in a simple controller. Moreover, the proposed converter has lower switches' voltage stress than the traditional buck converter. Therefore, the low voltage rating switches can be considered to achieve low losses, high efficiency, and low cost. Besides, the comparisons between the proposed converter and the previous high step-down topologies are presented to show the benefits of the proposed topology such as lower passive component count and higher effective duty cycle range. The experimental results of the 48W prototype have been provided to validate the theoretical analysis. © 2023 IEEE.

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Wine Quality Prediction Based on Machine Learning Techniques 2nd

Wine Quality Prediction Based on Machine Learning Techniques

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2nd Conference on Flexible Electronics for Electric Vehicles, FlexEV 2021

作者： Gupta, Yogesh Saraswat, Amit Department of Computer Science BML Munjal University Gurugram India Department of Electrical Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789811905872

Nowadays, it is extremely difficult to choose wines as there are numerous wine manufacturers. In response to the increase in customer base of wine, wine companies need to improve their quality and sales. There have been many attempts to develop a methodological approach for assessment of wine quality. In this paper, machine learning methods such as decision tree, random forest and support vector are used to check the quality of two types of wine: red and white. This work takes into account various ingredients of wine to predict its quality. The experiments show the superiority of random forest over decision tree and support vector classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Real-time programmable metasurface for terahertz multifunctional wave front engineering

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Light(science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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Dual-frequency piezoelectric micromachined ultrasound transducer based on polarization switching in ferroelectric thin films

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Microsystems & Nanoengineering 2023年第5期9卷 233-243页

作者： Jin Soo Park Soo Young Jung Dong Hun Kim Jung Ho Park Ho Won Jang Tae Geun Kim Seung-Hyub Baek Byung Chul Lee Bionics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical Engineering Korea UniversitySeoul 02841Republic of Korea Center for Electronic Materials Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Material Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Division of Bio-Medical Science and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul 02792Republic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoul 02447Republic of Korea

Due to its additional frequency response,dual-frequency ultrasound has advantages over conventional ultrasound,which operates at a specific frequency ***,a tunable frequency from a single transducer enables sonographers to achieve ultrasound images with a large detection area and high *** facilitates the availability of more advanced techniques that simultaneously require low-and high-frequency ultrasounds,such as harmonic imaging and image-guided *** this study,we present a novel method for dual-frequency ultrasound generation from a ferroelectric piezoelectric micromachined ultrasound transducer(PMUT).Uniformly designed transducer arrays can be used for both deep low-resolution imaging and shallow high-resolution *** switch the ultrasound frequency,the only requirement is to tune a DC bias to control the polarization state of the ferroelectric *** vibration of the PMUT membrane strongly depends on the polarization state,producing low-and high-frequency ultrasounds from a single excitation *** strategy for dual-frequency ultrasounds meets the requirement for either multielectrode configurations or heterodesigned elements,which are integrated into an ***,this technique significantly reduces the design complexity of transducer arrays and their associated driving circuits.

关键词： piezoelectric ferroelectric enable

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A 2.2-3.6 GHz CMOS Reconfigurable Fourth-Order Bandpass Filter With Compact Size and High Selectivity Using Transformer-Type Resonators

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IEEE Transactions on Microwave Theory and Techniques 2023年第1期71卷 218-229页

作者： Li, Kun Liu, Bin Chi, Pei-Ling Wang, Yong Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu Sichuan610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Nanhu Laboratory Research Center of Advanced Rf Chips and Systems Jiaxing Zhejiang314000 China

A compact reconfigurable CMOS bandpass filter (BPF) with high selectivity is proposed using transformer-type resonators in this article. The transformer-type resonator is constructed by a transformer loaded with parallel varactors for frequency tuning, and two varactors are then added across primary wind and secondary wind for coupling tuning. With the transformer-type structure, one resonator is formed inside another resonator, thus fully utilizing the chip area and resulting in a miniaturized structure. Magnetic and electrical couplings are formed within the transformer-type resonator and facilitate the bandwidth (BW) tuning. The quality factor (Q) of each resonator is improved by cross-coupled negative resistance structure. Meanwhile, two pairs of transformer-type resonators are placed side-by-side and naturally form a quadruplet structure;as a result, two transmission zeros (TZs) are introduced at each side of the passband thus significantly improving the selectivity and stopband rejection of the BPF. The proposed tunable on-chip BPF is manufactured by a commercial 55 nm bulk CMOS technology with a core area of 1.48 × 0.54 mm2. The filter center frequency (CF) can be tuned from 2.2 to 3.6 GHz, while the 3 dB fractional BW (FBW) can be tuned from 14% to 33%. The shape factor of the proposed filter is 1.4-1.9. © 1963-2012 IEEE.

关键词： Varactors

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