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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Suyata, Thong-In Mungporn, Pongsiri Yodwong, Burin Phattanasak, Matheepot Thounthong, Phatiphat Jamshidpour, Ehsan Pierfederici, Serge Nahid-Mobarakeh, Babak Bizon, Nicu Inteeworn, Ridtee Gonmanee, Apinun Takorabet, Noureddine Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand Thai French Innovation Institute King Mongkut's University of Technology North Bangkok Bangkok Thailand Renewable Energy Research Centre King Mongkut's University of Technology North Bangkok Bangkok Thailand ENSEM-GREEN Université de Lorraine Nancy54052 France Laboratoire d'Energetique et de Mecanique Théorique et Appliquée Umr 7563 Cnrs University of Lorraine Vandoeuvre-lès-Nancy54500 France Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada The National University of Science and Technology Politehnica Bucharest Piteşti University Centre Pitesti110040 Romania Provincial Electricity Authority Nakhonnayok 138 M.5 T.Banyai A.Muang Nakhonnayok Thailand Department of Electronics Technology Khon Kaen Technical College Institute of Vocational Education Northeastern Region 3 Khon Kaen40000 Thailand

ISBN: (纸本)9798350314274

This paper presents an original control algorithm using Hamiltonian energy control for a permanent magnet synchronous motor (PMSM) drive. The algorithm addresses the challenge of constant power load (CPL), which can cause instability of the input DC bus. The Hamiltonian energy control incorporates a model-based estimation to handle the CPL. An exponentially stable state-observer calculates load torque and stator resistance, improving control efficiency. Moreover, the difference flatness approach is used to generate a current reference to compensate for speed error in the nonlinear drive system. Experimental validation is conducted on a 10kW PMSM using a dSPACE MicroLabBox controller card. The Experimental results demonstrate the proposed algorithm's effectiveness, exhibiting excellent performance even with parameter variations. © 2023 IEEE.

关键词： Permanent magnets

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

arXiv

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arXiv 2025年

作者： Yin, Haoran Tang, Yanqun Bemani, Ali Kountouris, Marios Zhou, Yu Zhang, Xingyao Liu, Yuqing Chen, Gaojie Yang, Kai Liu, Fan Masouros, Christos Li, Shuangyang Caire, Giuseppe Xiao, Pei School of Electronics and Communication Engineering Sun Yat-sen University China Mathematical and Algorithmic Sciences Laboratory Huawei France Research and Development France Communication Systems Department EU-RECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China National Mobile Communications Research Laboratory Southeast University Bangladesh Department of Electrical and Electronic Engineering University College London United Kingdom Department of Electrical Engineering and Computer Science Technical University of Berlin Germany 5G/6G Innovation Centre University of Surrey United Kingdom

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

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SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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Disentanglement of intrinsic and extrinsic side-jump scattering induced spin Hall effect in N-implanted Pt

引用

Physical Review B 2023年第6期107卷 064402-064402页

作者： Utkarsh Shashank Yoji Nakamura Yu Kusaba Takafumi Tomoda Razia Nongjai Asokan Kandasami Rohit Medwal Rajdeep Singh Rawat Hironori Asada Surbhi Gupta Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Materials Science Division Inter University Accelerator Centre Aruna Asaf Ali Marg New Delhi 110067 India Department of Physics and Centre for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand 248007 India Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

The rapidly evolving utilization of spin Hall effect (SHE) arising from spin-orbit coupling in 5d transition metals and alloys has made giant strides in the development of designing low-power, robust, and nonvolatile magnetic memory. Recent studies, on incorporating nonmetallic lighter elements such as oxygen, nitrogen, and sulfur into 5d transition metals, have shown an enhancement in dampinglike torque efficiency θDL due to the modified SHE, but the mechanism behind this enhancement is not clear. In this paper, we study θDL at different temperatures (100–293 K) to disentangle the intrinsic and extrinsic side-jump scattering induced SHE in N-implanted Pt. We observe a crossover of intrinsic to extrinsic side-jump mechanism as the implantation dose increases from 2×1016 to 1×1017ions/cm2. A sudden decrease in the intrinsic spin Hall conductivity is counterbalanced by the increase in the extrinsic side-jump induced SHE efficiency. These results conclude that studying θDL as a function of implantation dose, and as a function of temperature, is important to understand the physical mechanism contributing to SHE.

关键词： Spin Hall effect Spin-orbit coupling Spin-orbit torque Spintronics Thin films Ferromagnetic resonance Ion implantation

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A predictive analytics approach for stroke prediction using machine learning and neural networks

arXiv

引用

arXiv 2022年

作者： Dev, Soumyabrata Wang, Hewei Nwosu, Chidozie Shamrock Jain, Nishtha Veeravalli, Bharadwaj John, Deepu ADAPT SFI Research Centre Dublin Ireland School of Computer Science University College Dublin Ireland Beijing University of Technology Beijing China Beijing-Dublin International College Beijing China National College of Ireland Dublin Ireland Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Electrical and Electronic Engineering University College Dublin Ireland

The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical diagnosis, caregivers create opportunities for better patient management by systematically mining and archiving the patients' medical records. Therefore, it is vital to study the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Using various statistical techniques and principal component analysis, we identify the most important factors for stroke prediction. We conclude that age, heart disease, average glucose level, and hypertension are the most important factors for detecting stroke in patients. Furthermore, a perceptron neural network using these four attributes provides the highest accuracy rate and lowest miss rate compared to using all available input features and other benchmarking algorithms. As the dataset is highly imbalanced concerning the occurrence of stroke, we report our results on a balanced dataset created via sub-sampling techniques. Copyright © 2022, The Authors. All rights reserved.

关键词： Records management

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Catalytic degradation of tetracycline using marigold flower-like structure erbium molybdate decorated on sulphur-doped g-C3N4 nanocomposite: Kinetics, thermodynamics, DFT calculations, and toxicity studies

引用

Separation and Purification technology 2024年 330卷

作者： Ballur Prasanna, Sanjay Sakthivel, Rajalakshmi Arehalli Shivamurthy, Santhosh Lin, Yu-Chien Liu, Xinke Chen, Jung-Chih Liu, Ting-Yu Chung, Ren-Jei Taipei Taiwan NMKRV College for Women's Karnataka India Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Catholic Mercy Hospital Catholic Mercy Medical Foundation Hsinchu County Taiwan Medical Device Innovation & Translation Centre National Yang Ming Chiao Tung University Taipei Taiwan Department of Materials Engineering Ming Chi University of Technology New Taipei City Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan City Taiwan Taipei10608 Taiwan College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

In this study, a novel nanocomposite has been developed comprising erbium molybdate decorated on sulphur-doped graphitic carbon nitride (EMO/SGCN) acts as an effective catalyst for the degradation of tetracycline (TC) by NaBH4. The as-prepared catalyst exhibited remarkable catalytic behavior towards the removal of TC, with degradation efficiency and rate constants (k) of 89.15% and 0.0576 min−1, respectively, during the first 90 min. The TC degradation kinetics followed a pseudo-first order response based on the correlation coefficient (R2) values and adsorption capabilities. Catalytic degradation routes were spotted by GC-MS (Gas chromatography-mass spectrometry) analysis. The degradation mechanism of TC by the catalyst was suggested grounded on the experimental investigation and density functional theory calculations. Toxicity prediction was used to assess the toxicity of intermediates. Therefore, the use of EMO/SGCN in the presence of NaBH4 for the catalytic degradation of antibiotics in wastewater treatment can provide new insights into metal composite-based catalysts. © 2023 Elsevier B.V.

关键词： Kinetics

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A graph-based convolutional neural network stock price prediction with leading indicators

A graph-based convolutional neural network stock price predi...

引用

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

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Beyond Lambertian light trapping for large-area silicon solar cells:fabrication methods

引用

Opto-Electronic Advances 2022年第9期5卷 14-25页

作者： Jovan Maksimovic Jingwen Hu Soon Hock Ng Tomas Katkus Gediminas Seniutinas Tatiana Pinedo Rivera Michael Stuiber Yoshiaki Nishijima Sajeev John Saulius Juodkazis Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials(SEAM) School of ScienceSwinburne University of TechnologyHawthorn Vic 3122Australia Melbourne Centre for Nanofabrication ANFF Victoria151 Wellington Rd.Clayton Vic 3168 Australia Department of Electrical and Computer Engineering Graduate School of EngineeringYokohama National University79-5 TokiwadaiHodogaya-kuYokohama 240-8501Japan Institute of Advanced Sciences Yokohama National University79-5 TokiwadaiHodogaya-kuYokohama 240-8501Japan Department of Physics University of Toronto60 St.George StreetTorontoONM5S 1A7Canada World Research Hub Initiative(WRHI) School of Materials and Chemical TechnologyTokyo Institute of Technology2-12-1OokayamaMeguro-kuTokyo 152-8550Japan

Light trapping photonic crystal(PhC)patterns on the surface of Si solar cells provides a novel opportunity to approach the theoretical efficiency limit of 32.3%,for light-to-electrical power conversion with a single junction *** is beyond the efficiency limit implied by the Lambertian limit of ray trapping~29%.The interference and slow light effects are harnessed for collecting light even at the long wavelengths near the Si *** compare two different methods for surface patterning,that can be extended to large area surface patterning:1)laser direct write and 2)step-&-repeat 5×reduction projection *** area throughput limitations of these methods are compared with the established elec-tron beam lithography(EBL)route,which is conventionally utilised but much slower than the presented ***-tral characterisation of the PhC light trapping is compared for samples fabricated by different *** of Si etched via laser patterned mask was~7%at visible wavelengths and was comparable with Si patterned via EBL made *** later pattern showed a stronger absorbance than the Lambertian limit6.

关键词： silicon solar cells laser ablation light trapping Lambertian limit

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Automatic alignment and location of multiple fiducial marks

引用

International Journal of Mechatronics and Manufacturing Systems 2020年第2期13卷 125-152页

作者： Lin, Chuen-Horng Wen, Chih-Chin Chen, Jr-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. Taichung Taiwan Mechatronics Section Metal Industries Research and Development Centre 1001 Kaohsiung Highway Kaohsiung811 Taiwan

This paper proposes a method for the automatic alignment and locating the position. This paper proposes methods for the alignment and location on two types of fiducial marks (FM) images. FM images of the first type include light-emitting diode (LED) wafer images, liquid crystal display (LCD) images and hand-held electronic device (HED) images. FM images of the second type include LCD cross images and LED square cross images. This paper proposes the artificial measurement method and the mechanical measurement method in order to make mechanical positioning-related decisions. To validate the properties and the robustness of the proposed methods. The results achieved by the proposed matching, alignment methods are compared with those achieved by manual image matching in order to evaluate the accuracy and performance of the alignment methods. computer image processing skills, appropriate charge coupled device (CCD) equipment, quantity, image capture technology and glazing mode are used to develop high speed, precision and stability automatic positioning technology in this paper. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Location

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