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A new electric field mill array with each of the mill’s rotor controlled precisely by a GPS module:Equipment and initial results

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Earth and Planetary Physics 2024年第2期8卷 423-435页

作者： Kozo Yamashita Hironobu Fujisaka DaoHong Wang Hiroyuki Iwasaki Kazuo Yamamoto Koichiro Michimoto Masashi Hayakawa Department of Production Systems Engineering and Sciences Komatsu UniversityNu 1-3 Shicho-machiKomatsuIshikawa 923-8511Japan Fujisaka Technology Office 1058-5 Yoshizawa-choOhta-shiGunma 373-0019Japan Department of Electrical Electronic and Computer EngineeringGifu University1-1 YanagidoGifu CityGifu 501-1193Japan Cooperative Faculty of Education Gunma University4-2-Aramaki-machiMaebashiGunma 371-8510Japan Department of Electrical and Electronic Engineering Chubu University1200 Matsumoto-choKasugaiAichi 487-8501Japan Independent Researcher Kosenba Town 2-44-11-406Kawagoe CitySaitama350-0036Japan Advanced Wireless&Communications Research Center The University of Electro-Communications1-5-1 ChofugaokaChofuTokyo 182-8585Japan

We have newly designed an electrostatic sensor,called an electric field mill(EFM),to simplify the estimation of the charge position and charge amount transferred by lightning *** is necessary for this remote estimation of the transferred charge to measure electric field changes caused by charge loss at the time of a lightning strike at multiple *** multiple-station measurement of electric field changes,not only speed but also phase for exposure and shielding of the sensing plates inside each EFM of the array should be synchronized to maintain the sensitivities of the deployed ***,there is no such EFM with specified speed and phase control performance of the rotary ***,we developed a new EFM in which the rotary mechanism was controlled consistently to within 3%error by a GPS *** EFMs had been distributed in the Hokuriku area of Japan during the winter season of 2022-2023 for a test *** we describe the design and a simple calibration method for our new EFM *** analysis method based on the assumption of a simple monopole charge structure is also *** validation,locations of assumed point charges were compared with three-dimensional lightning mapping data estimated by radio observations in the MF-HF *** results indicated the validity to estimate transferred charge amounts and positions of winter cloud-to-ground lightning discharges with our new EFM array.

关键词： lightning electrostatic field electric field mill electric field change

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kiana Pilevar Abrisham Khalil Alipour Bahram Tarvirdizadeh Mohammad Ghamari Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) – the gold standard in this field – is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R 2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics.

关键词： Accuracy Noise Estimation Predictive models Feature extraction Brachytherapy Data models Arteries Spectrogram Monitoring

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Nanophotonic-structured front contact for high-performance perovskite solar cells

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Science China Materials 2022年第7期65卷 1727-1740页

作者： Md.Akhtaruzzaman Mohammad Ismail Hossain Mohammad Aminul Islam Md.Shahiduzzaman Ghulam Muhammad A.K.Mahmud Hasan Yuen Hong Tsang Kamaruzzaman Sopian Solar Energy Research Institute(SERI) Universiti Kebangsaan Malaysia43600 BangiSelangorMalaysia Department of Applied Physics Hong Kong Polytechnic University(PolyU)KowloonHong KongChina Centre for Integrated Systems Engineering and Advanced Technologies(Integra) Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia(UKM)43600 BangiSelangorMalaysia Graduate School of Pure and Applied Sciences University of TsukubaTsukubaIbaraki 305-8573Japan Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaJalan Universiti50603 Kuala LumpurSelangorMalaysia Nanomaterials Research Institute(NanoMaRi) Kanazawa UniversityKakumaKanazawa 920-1192Japan Department of Computer Engineering College of Computer and Information SciencesKing Saud University11543 RiyadhSaudi Arabia Department of Electrical and Computer Engineering University of CaliforniaDavisCA 95616USA Institute of Sustainable Energy Universiti Tenaga Nasional(@The National Energy University)Jalan IKRAM-UNITEN43000 KajangSelangorMalaysia

We report the design of a nanophotonic metaloxide front contact aimed at perovskite solar cells(PSCs)to enhance optoelectronic properties and device stability in the presence of ultraviolet(UV)***-quality Cr-doped ZnO film was prepared by industrially feasible magnetron sputter deposition for the electron transport layer of *** a means,the influence of the Cr content on the film and device was systematically ***-depth device optics and electrical effects were studied using advanced three-dimensional opto-electrical multiphysics rigorous simulations,optimizing the front contact for realizing high *** numerical simulation was validated by fabricating PSCs optimized to reach high performance,energy conversion efficiency(ECE)=17.3%,open-circuit voltage(V_(OC))=1.08 V,short-circuit current density(J_(SC))=21.1 mA cm^(-2),and fillfactor(FF)=76%.Finally,a realistic front contact of nanophotonic architecture was proposed while improving broadband light absorption of the solar spectrum and light harvesting,resulting in enhanced quantum efficiency(QE).The nanophotonic PSC enables J_(SC)improvement by~17%while reducing the reflection by 12%,resulting in an estimated conversion efficiency over 23%.It is further demonstrated how the PSCs’UV-stability can be improved without considerably sacrificing optoelectronic *** of nanophotonic designed ZnO:Cr front contact,PSCs device,and fabrication process are described.

关键词： ZnO:Cr front contact magnetron sputtering perovskite solar cells UV stability optics and electrical effects

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Soft Switched Hybrid Energy Storage Module for Power and Energy Management of a DC Bus

Soft Switched Hybrid Energy Storage Module for Power and Ene...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Ilya Zeltser Michael Evzelman Daniel Beniaminson Mor M. Peretz Electronics Department Rafael Advanced Defense Systems Ltd. Haifa Israel Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

A hybrid energy storage module based around two strings consistent of battery cells as an energy optimized string, and super-capacitors as a power surge optimized string, is presented. The energy transfer between the two strings is carried out using a series resonant link, that connects one battery to two super capacitors. The rest of the cells are equalized by series resonant balancers that shuttle the energy along each of the strings. The serial resonant link between the strings results in faster energy transfer, operates more efficiently than previous options, and reduces component count compared to the latest available solutions. Hybrid energy storage connection to a common DC bus options are outlined, and three setups that provide added value for dual string architecture are shown. Dual voltage source management methods are summarized, along with their advantages and down sides. The system is analyzed analytically, evaluated in simulation, and verified by an experimental prototype. The simulation and experimental results are in good agreement with theoretical predictions.

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Link-based Anomaly Detection with Sysmon and Graph Neural Networks

Link-based Anomaly Detection with Sysmon and Graph Neural Ne...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Charlie Grimshaw Brian Lachine Taylor Perkins Emilie Coote Department of National Defence Naval Platform Systems Ottawa Canada Electrical and Computer Engineering Royal Military College Kingston Canada Cyber & Strategic Risk Deloitte Canada Toronto Canada

Anomaly detection is a challenge well-suited to machine learning and in the context of information security, the benefits of unsupervised solutions show significant promise. Recent attention to Graph Neural Networks (GNNs) has provided an innovative approach to learn from attributed graphs. Using a GNN encoder-decoder architecture, anomalous edges between nodes can be detected during the reconstruction phase. The aim of this research is to determine whether an unsupervised GNN model can detect anomalous network connections in a static, attributed network. Network logs were collected from four corporate networks and one artificial network using endpoint monitoring tools. A GNN-based anomaly detection system was designed and employed to score and rank anomalous connections between hosts. The model was validated against four realistic experimental scenarios against the four large corporate networks and the smaller artificial network environment. Although quantitative metrics were affected by factors including the scale of the network, qualitative assessments indicated that anomalies from all scenarios were detected. The false positives across each scenario indicate that this model in its current form is useful as an initial triage, though would require further improvement to become a performant detector. This research serves as a promising step for advancing this methodology in detecting anomalous network connections. Future work to improve results includes narrowing the scope of detection to specific threat types and a further focus on feature engineering and selection.

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China Automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Enhancing Power Quality Event Classification with AI Transformer Models

Enhancing Power Quality Event Classification with AI Transfo...

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IEEE General Meeting Power& Energy Society

作者： Ahmad M. Saber Amr Youssef Davor Svetinovic Hatem Zeineldin Deepa Kundur Ehab El-Saadany EECS Department Advanced Power and Energy Center Khalifa University Abu Dhabi UAE Department of Electrical and Computer Engineering University of Toronto Toronto Canada Concordia Institute for Information Systems Engineering Montreal Canada

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal’s amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%–91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

关键词： Voltage measurement Noise Power quality Transformers Harmonic analysis Frequency measurement Numerical models Noise measurement Transient analysis Signal to noise ratio

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Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

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computers, Materials & Continua 2021年第12期69卷 2863-2882页

作者： Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi KIIT University BhubaneswarOdishaIndia Department of Electrical Engineering CETBhubaneswarOdishaIndia Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Computer Science&Engineering Pandit Deendayal Petroleum UniversityGandhinagarIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in *** non-linearity are added to make the fossil fuel-based power systems more *** order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic *** this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum *** conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in *** adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

关键词： Economic load dispatch valve point loading industry 4.0 prohibited operating zones ramp rate limit oscillatory particle swarm optimization

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