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Improved Numerical Method for Skin Effect Computing of Conductors Having Rectangular Cross Sections 19

Improved Numerical Method for Skin Effect Computing of Condu...

19th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and electronic engineering, ISEF 2019

作者： Raicevic, Nebojsa Butrylo, Boguslaw Aleksic, Slavoljub Barukcic, Marinko University of Nis Faculty of Electronic Engineering Nis Serbia Bialystok University of Technology Faculty of Electrical Engineering Bialystok Poland University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9781728115603

A procedure for numerical solution of skin effect integral equations is presented in this paper. The function of current density, in a conductor having rectangular cross-section, can be approximated by means of the finite functional series with basic functions which automatically satisfy wave equation. The unknown complex coefficients are determined by using least square method and iterative method. Differential evolution optimization algorithm is applied for minimization of error functional. This approach leads to a very accurate solution, even in the case when only two terms in current approximation are adopted, giving a considerable advantage over the existing results. Being that the electromagnetic field components depend on the current density derivation, the proposed method gives very accurate solution not only for current density distribution and resistance, but also for field components and for internal inductivity coefficients. Hybrid Boundary Elements Method is used to determine the current distribution in the cross sections of a group of conductors. © 2019 IEEE.

关键词： Iterative methods

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Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction 2

Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incr...

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2nd International Conference on Electrical, Communication and computer engineering, ICECCE 2020

作者： Rehman, Atiqur Khan, Aurangzeb Ali, Muhammad Akhtar Khan, Muhammad Umair Khan, Shafqat Ullah Ali, Liaqat University of Science and Technology Bannu Department of Computer Science Bannu Pakistan Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne United Kingdom University of Science and Technology Bannu Department of Electrical Engineering Bannu Pakistan University of Buner Department of Electronics Buner Pakistan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering University of Science and Technology Bannu Bannu Pakistan

ISBN: (纸本)9781728171166

Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature extraction algorithms namely Principle Component Analysis (PCA), Sparse PCA, Kernel PCA and Incremental PCA. These algorithms are integrated with machine learning models to improve HF prediction. The performance of all these integrated models are evaluated by analyzing Cleveland heart failure database. Experimental results pointed out that Kernel PCA algorithm integrated with linear discriminant analysis model and Sparse PCA integrated with Gaussian Naive Bayes (GNB) model offers 91.11% of HF classification accuracy. Hence, based on the experimental results it is shown that Kernel PCA and Sparse PCA are suitable feature extraction methods for HF data. © 2020 IEEE.

关键词： Feature extraction

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A Recommendation Algorithm Trading off Performance and Privacy Protection

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Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University 2021年第7期55卷 117-123页

作者： Ma, Dailusi Zhu, Haiping Tian, Feng Feng, Pei Chen, Yan Ji, Xiangting Li, Yujie Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University Xi'an710049 China Co. Ltd. Beijing100085 China School of Computer Science and Engineering Nanjing University of Technology Nanjing210094 China

Aiming at the problem of privacy protection in recommender systems, a balanced optimization model between privacy protection parameters and recommendation accuracy is proposed. Taking online learning resource recommender system as an example, this paper builds a matrix factorization model and studies the relationship between privacy protection parameters and recommendation accuracy after introducing differential privacy noise into data input module and model training module. According to the implicit feedback characteristics of online learning resource recommender system data, a negative sampling algorithm by resource-popularity is proposed. The experiment is based on the original and balanced data from the Network College of Xi'an Jiaotong University using Baidu PaddlePaddle platform, and the root mean square error is used as an evaluation index to measure the recommendation accuracy. The results show that negative sampling makes higher prediction accuracy than the original. And the recommendation prediction accuracy is directly proportional to the reciprocal of differential privacy protection parameter. When RMSEs are less than 2.0 and 1.3 and the privacy protection parameter is 7 and 3, respectively, both the input-based algorithm and the model-based algorithm achieve best balance. Moreover, when the privacy protection parameter is no more than 5, the model-based algorithm has a higher recommendation accuracy than the input-based algorithm. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

关键词： Recommender systems

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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

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Informatics in Medicine Unlocked 2022年 28卷 100840页

作者： Hasan, Md. Imran Rahman, Md Habibur Islam, M. Babul Islam, Md Zahidul Hossain, Md Arju Moni, Mohammad Ali Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Department of Electrical and Electronic Engineering University of Rajshahi 6205 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Tangail 1092 Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Artificial Intelligence & Digital Health Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease's initiation and progression as well as developing drug therapeutics. © 2022 The Authors

关键词： Biomarker signatures Blood common gene COVID-19 Differentially expressed genes Gene ontology PBMCs cell

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An analysis of voltage source inverter switches fault classification using short time Fourier transform

arXiv

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arXiv 2021年

作者： Manap, Mustafa Nikolovski, Srete Skamyin, Aleksandr Karim, Rony Sutikno, Tole Jopri, Mohd Hatta Faculty of Electrical & Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Malacca Malaysia Power Engineering Department Faculty of Electrical Engineering Computer Science and Information Technology University of Osijek Osijek31000 Croatia Department of Electric Power and Electromechanics Saint Petersburg Mining University Saint Petersburg Russia GmbH Germany Deparment of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Yogyakarta Indonesia

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. Time-frequency representation (TFR) represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit faults with varying characteristics for each switch in VSI. The classification results demonstrate the proposed technique is good to be implemented for VSI switches faults classification, with an accuracy classification rate of 98.3%. © 2021, CC BY.

关键词： Signal analysis

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Introduction of the Study Results of the Moon Electrical Power Systems 22

Introduction of the Study Results of the Moon Electrical Pow...

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22nd IAA Symposium on Building Blocks for Future Space Exploration and Development at the 75th International Astronautical Congress, IAC 2024

作者： Oka, Noriaki Yamauchi, Kenji Ijichi, Koichi Yanagawa, Hiroki Kashimura, Osamu Uchida, Atsushi Tanaka, Koji Mitani, Tomohiko Honjo, Kazuhiko Ishikawa, Ryo Ishimura, Kosei Fujino, Yoshiyuki Fujita, Kazuhisa Itoh, Kenji Kawakami, Yoshihiro Ishikawa, Yoji Yamaguchi, Hiroshi Ogawa, Akihiko Japan Space Systems 3-5-8 Shibakoen Minato-ku Tokyo105-0011 Japan Emerging Technologies and Security Division Mitsubishi Research Institute Inc. 10-3 Nagatacho 2-chome Chiyoda-ku Tokyo100-8141 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan Kyoto University Gokasho Kyoto Uji611-0011 Japan Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Dept. of Modern Mechanical Engineering School of Creative Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Faculty of Science and Engineering Toyo University 2100 Kujirai Saitama-ken Kawagoe350-8585 Japan 1955-1 Kurematsu-cho chuo-ku Shizuoka Hamamatsu431-1202 Japan Department of Electrical and Electronic Engineering College of Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Obayashi Future Lab. Technology Division Obayashi Corporation 2-15-2 Konan Minato-ku Tokyo108-8502 Japan 492 Minosho-machi Nara-ken YamatoKoriyama639-1186 Japan Electronics Technologies Office Technologies Development Department IHI Aerospace Co. Ltd. 900 Fujiki Gunma-ken Tomioka-shi370-2398 Japan

ISBN: (纸本)9798331312251

Since the year of 2021, Ministry of Economy, Trade and Industry (METI) started work on the energy issues on the Moon including the feasibility study of the electrical power system with Mitsubishi Research Institute, Japan Space Systems, Institute of Space and Astronautical Science of Japan Aerospace Exploration Agency (JAXA), the universities and the industries. The architecture of the Moon electrical power infrastructure is found to be more like those of the power infrastructure of the earth than current satellite power system, and consisted of the power generation, power distribution grid and the power storage, and supplying electrical power to the consumer. The differences are that the environment of the Moon such as the vacuum and temperature condition, the influence of the regolith, etc. should have to be considered, and also the transportation cost of the construction is the key factor for the necessary technology to be selected. Of course, the technologies already established by the International Space Station (ISS) should be applied where necessary such as in the human habitable area. And more feature of the Moon is that the power generation on the Moon orbit and be sent to the surface or the inside the Permanent Shadow Region (PSR) other than on the surface of the Moon. The main consumer of the power system until the year 2050th is the water extraction and then the electrolysis of the water for, such as, the propellant for the transportation means and the electricity for the human habitant. The results of the system feasibility study of the Moon power architecture and also the electric power system team activities including the discussion with the hydrogen production team are going to be introduced. Copyright © 2024 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Space power generation

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Angular embedding: A new angular robust principal component analysis

arXiv

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arXiv 2020年

作者： Liu, Shenglan Yu, Yang School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China

As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensitive to outliers, which has been improved by numerous Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density, which is improved for large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers. Extensive experiments on both synthetic and real-world datasets with vector-level or pixel-level outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art RPCA based methods. © 2020, CC-BY.

关键词： Embeddings

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Molecular Communications Loss Budget for tsRNA Detection in the Brain

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年

作者： Khalil, Aiman Pumares, Kurt J. A. Skogberg, Anne Kallio, Pasi Kilbane, Deirdre Martins, Daniel P. South East Technological University Walton Institute for Information and Communication Systems Science X91 WaterfordP20H Ireland Tampere University BioMediTech Institute Faculty of Medicine and Health Technology Korkeakoulunkatu 3 Tampere33720 Finland South East Technological University FutureNeuro SFI Research Centre Walton Institute for Information and Communication Systems Science X91 WaterfordP20H Ireland University of Essex School of Computer Science and Electronic Engineering Wivenhoe ColchesterCO4 3SQ United Kingdom

Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g. collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions–brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures. © 2015 IEEE.

关键词： Budget control

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Enhancing tremor classification: Transformer-based analysis of biomechanics patterns for Parkinson's and essential tremor

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Clinical Biomechanics 2025年 127卷

作者： Muhammad Izzuddin Mahali Jenq-Shiou Leu Cries Avian Jeremie Theddy Darmawan Muhamad Faisal Nur Achmad Sulistyo Putro Setya Widyawan Prakosa Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Malang Indonesia Department of Biochemistry National University of Singapore Singapore Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

Background Differentiating Essential Tremor and Parkinson's Disease is challenging due to overlapping tremor characteristics, including similar frequency ranges (4–8 Hz) and kinetic manifestations that defy conventional clinical differentiation. This study aimed to develop a multiclass differentiation system for Essential Tremor, Parkinson's Disease, and Healthy Controls by employing deep learning to decode distinct biomechanical patterns from multi-sensor movement data during dynamic motor tasks. Methods Tremor severity was assessed using accelerometers positioned on the thumb, index finger, metacarpal, and wrist during four protocols: two static (rest, postural) and two dynamic (free motion, motion with object). We employed a Transformer-based model with multi-head attention to capture spatiotemporal movement patterns. Two analytical approaches were compared: (1) feature extraction followed by Transformer processing, and (2) direct Transformer processing of raw signals. Findings The feature-based approach achieved perfect classification accuracy (100 %) for postural holding (utilizing integrated absolute value and other derived features) and free motion (employing mean power and additional features). The raw signal approach similarly attained 100 % accuracy in classifying free motion (200-sample window) and motion with object (200- and 300-sample windows). Integration of multi-protocol dynamic tasks (free motion and motion with object) yielded 99.32 % overall accuracy. Crucially, dynamic protocols demonstrated consistent superiority over static protocols in diagnostic performance. Interpretation The Transformer model with multi-head attention effectively identified disease-specific biomechanical patterns. Its high accuracy in distinguishing Essential Tremor, Parkinson's Disease, and Healthy Control participants, particularly during dynamic tasks, positions it as a promising tool for enhancing clinical decision-making and artificial intelligence-assisted monitoring

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Exergy-based fault detection on the Tennessee Eastman process

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IFAC-PapersOnLine 2020年第2期53卷 13713-13720页

作者： J. Vosloo K.R. Uren G. van Schoor L. Auret H. Marais School of Electrical Electronic and Computer Engineering Faculty of Engineering North-West University South Africa Unit for Energy and Technology Systems Faculty of EngineeringNorth-West University South Africa School of Process Engineering Stellenbosch University South Africa

The exergy-based fault detection method has not yet been applied to a complex industrial system that adequately represents a dynamically changing process. One such system, the Tennessee Eastman process, is commonly used as a benchmark for fault detection methods. In this paper, an exergy-based fault detection approach is applied to the Tennessee Eastman process. This is done to investigate the feasibility of using this approach when confronted with noisy sensor data and control loops masking faulty behaviour. An exergy characterisation was performed on stream data obtained from the Tennessee Eastman process. The exergy characterisation included a new approach to calculate the standard chemical exergy of unknown components. For fault detection, threshold limits were determined for the exergy characterisation when normal operating conditions are assumed. The threshold limits were calculated following the upper and lower control limit determination of the Shewhart control chart. The results showed that this method could quantify both the physical state as well as the chemical features of the process and that 17 out of the 20 considered faults could be detected. This shows that the exergy-based method could be adequately applied to the Tennessee Eastman process.

关键词： Fault detection Exergy Tennessee Eastman process

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