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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Raghu Dhumpati Nirmala Reddy Sankati Ramakrishna Posimsetti Naveen Kumar Sheo Kumar Homera Durani Department of Computer Science and Engineering Bahrain Polytechnic Bahrain Department of Computer Science and Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering-Data Science KKR & KSR Institute of Technology and Sciences Guntur India VLSI System Design Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of CSE School of Engineering RK University Rajkot India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

With the introduction of IIoT systems, industry breakthroughs and linked smart objects revolutionize the world. The enemies are finding a lot of room to launch a number of devastating cyber assaults thanks to all this connection and dependability. However, implementing IDS systems may be tough due to the absence of dataset for training as well as testing. This makes it difficult to detect likely cyberattacks with the required accuracy. The method of detection might not be able to learn perfectly and detect cyberattacks significantly higher precision if there aren't enough records related to every threat category in the dataset. In addition, the detection of breaches procedure is becoming more complicated due to unbalanced datasets. In this light, our research suggests a more effective GAN-based IDS model for creating simulated data samples that can fool and evade security systems before directing the real-life network assault. To be more specific, it creates fictitious datasets and trains IDS on these in conjunction with the actual data in order to boost the system's efficiency. The specifics of the Improved Generative Adversarial Network-based Intrusion Detection system (IGAN-IDS) model that is suggested in this study are described in this section. With an accuracy of 99.6 percent, the suggested IGAN-IDS model surpasses all of its competitors, including BSA-IDS, GAN, MLP-GAN, Synthetic Minority Over-sampling Technique (SMOTE), and ADASYN. This proves that it outperforms all other approaches when it comes to classification.

关键词： Training Accuracy Intrusion detection Generative adversarial networks Rendering (computer graphics) Data models Security Computer crime Industrial Internet of Things Testing

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Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks 11

Oil Palm Leaf Disease Detection on Natural Background Using ...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Septiarini, Anindita Hamdani, Hamdani Junirianto, Eko Thayf, Mohammad Sofyan S. Triyono, Gandung Henderi Mulawarman University Faculty of Engineering Department of Informatics Samarinda Indonesia Polytechnic of Agriculture Department of Information Technology Samarinda Indonesia Stmik Kharisma Makassar Department of Information System Makassar Indonesia Universitas Budi Luhur Faculty of Information Technology Jakarta Indonesia University of Raharja Informatics Engineering Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9781665460309

Oil palm plant diseases typically manifest themselves on the leaves, resulting in reduced crop quality. It is necessary to solve this issue as the need for premium-quality palm oil keeps growing. Despite the fact that various automatic detection models for oil palm leaf disease have been developed, their performance was frequently inadequate due to the similarity of class characteristics. This work proposes a method that automatically detects the oil palm leaf disease on a natural background to distinguish between infected and healthy leaf classes. The method was developed using deep learning based on Convolution Neural Network (CNN) model. The private dataset consists of 600 oil palm leaf images (300 healthy and 300 infected) on a natural background. In order to decrease the computation time, pre-processing was carried out, which consists of resizing and normalizing the image, followed by augmentation. Augmentation was applied by rotation, flip, shear, and zooming techniques. Furthermore, the CNN model was employed to detect oil palm leaf disease using Tensorflow 2.5.0 framework with 224 × 224 input data. The proposed method successfully achieved the highest performance, revealed by the accuracy value of 1. © 2022 IEEE.

关键词： Neural network models

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Adaptive Dead-Time Algorithm to Reduce Current Harmonics in Three-Phase Motor Inverters Used for Electric Vehicles 27

Adaptive Dead-Time Algorithm to Reduce Current Harmonics in ...

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27th International Conference on Electrical Machines and systems, ICEMS 2024

作者： Thao, Nguyen Gia Minh Ngo, Duc-Kien Khan, Md Yakub Ali Doss, Arockia Selvakumar Arockia Ota, Motoki Fujisaki, Keisuke Shimane University Interdisciplinary Faculty of Science and Engineering Department of Mechanical Electrical and Electronic Engineering Matsue690-8504 Japan University of Danang-University of Technology and Education Faculty of Electrical and Electronic Engineering Viet Nam School of Mechanical Engineering Vellore Institute of Technology Chennai600127 India Shimane University Next Generation Tatara Co-Creation Centre Matsue690-8504 Japan Toyota Technological Institute Electromagnetic Energy System Laboratory Nagoya468-8511 Japan

ISBN: (纸本)9784886864406

This study proposes an adaptive dead-time algorithm in discrete time domain to decrease current harmonics in the interior permanent magnet synchronous motor (IPMSM) drive inverters, which may remarkably reduce motor losses under load condition. In detail, the efficient algorithm without large computational burden is designed for automatically seeking the optimal dead-time value of the pulse width modulation (PWM)-based inverter to minimize the fluctuations in the d-axis current of the considered motor as possible. Moreover, the total harmonic distortion (THD) of the IPMSM phase current in a detailed simulation model is provided and analyzed to assess the performance of the proposed dead-time algorithm carefully. Simulation results in MATLAB/Simulink with different rotational speeds are provided to illustrate the efficacy of the introduced algorithm. Furthermore, this model-free dead-time algorithm can be appropriately applied for other three-phase power inverters utilized in electric vehicles and renewable energy systems without large modifications. © 2024 The Institute of Electrical Engineers of Japan.

关键词： MATLAB

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer science, engineering, and Decision sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring

Wearable Biosensors: Transformative Innovations and Future P...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Trupti Thute Amit Gudadhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare.

关键词： Integrated optics Technological innovation system integration Biosensors Optical sensors Energy harvesting Biomedical monitoring Monitoring Nanotechnology Diseases

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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Proton storage chemistry in aqueous zinc-organic batteries:A review

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InfoMat 2023年第2期5卷 45-65页

作者： Xianming Deng James Kumankuma Sarpong Guobin Zhang Jing Hao Xu Zhao Linyuan Li Hongfei Li Cuiping Han Baohua Li Shenzhen Key Laboratory of Power Battery Safety and Shenzhen Geim Graphene Center Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenPeople's Republic of China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhenGuangdongPeople's Republic of China Faculty of Materials Science and Energy Engineering Institute of Technology for Carbon NeutralityShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenPeople's Republic of China Shenzhen Key Laboratory of Energy Materials for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenPeople's Republic of China

Benefiting from the advantageous features of structural diversity and resource renewability,organic electroactive compounds are considered as attractive cathode materials for aqueous Zn-ion batteries(ZIBs).In this review,we discuss the recent developments of organic electrode materials for aqueous *** the proton(H^(+))storage chemistry in aqueous Zn-organic batteries has triggered an overwhelming literature surge in recent years,this topic remains ***,our review focuses on this significant issue and summarizes the reported electrochemical mechanisms,including pure Zn^(2+)intercalation,pure H^(+)storage,and H^(+)/Zn^(2+)***,the impact of H^(+)storage on the electrochemical performance of aqueous ZIBs is discussed *** the significance of H^(+)storage,we also highlight the relevant characterization methods ***,perspectives and directions on further understanding the charge storage mechanisms of organic materials are *** hope that this review will stimulate more attention on the H^(+)storage chemistry of organic electrode materials to advance our understanding and further its application.

关键词： aqueous zinc-ion batteries H^(+)/Zn^(2+)co-storage organic electrode materials proton storage chemistry

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Phase binarization in mutually synchronized bias field free spin-Hall nano-oscillators for reservoir computing

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Physical Review B 2024年第21期109卷 214425-214425页

作者： Sourabh Manna Rohit Medwal John Rex Mohan Yasuhiro Fukuma Rajdeep Singh Rawat Natural Sciences and Science Education Department of Physics Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka 820-8502 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

Mutually coupled spin-Hall nano-oscillators (SHNO) can exhibit a binarized phase state, offering pathways to realize Ising machines and efficient neuromorphic hardware. Conventionally, phase binarization is achieved in coupled identical SHNOs via injecting an external microwave at twice the oscillator frequency in the presence of a strong biasing magnetic field. However, this technology poses potential challenges of higher energy consumption and complex circuit design. Moreover, differences in the individual characteristic frequencies of SHNOs resulting from fabrication-induced mismatch in SHNO dimensions may hinder their mutual synchronization. Addressing these challenges, we demonstrate purely dc current-driven mutual synchronization and phase binarization of two nonidentical nanoconstriction SHNOs without biasing magnetic field and microwave injection. We thoroughly investigate these phenomena and underlying mechanisms using micromagnetic simulation. We show how the localized fundamental mode of the spin wave emerging from the magnetization auto-oscillation reinforces the mutual synchronization, while the second-harmonic spin wave induces the phase binarization in the coupled SHNO pair. We further demonstrate the bias field free synchronized SHNO pair efficiently performing a reservoir computing benchmark learning task: sin- and square-wave classification, with 100% accuracy, utilizing the current-tunable phase binarization phenomenon. Our results showcase promising magnetization dynamics of coupled bias field free SHNOs for future computing applications.

关键词： Magnetic anisotropy Magnetism Magnetization dynamics Micromagnetism Spintronics Landau-Lifschitz-Gilbert equation Micromagnetic modeling

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Mathematical Modelling of Nonlinear Heat Conduction with Relaxing Boundary Conditions 1

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10th International Conference on Numerical Methods and Applications, NMA 2022

作者： Filipov, Stefan M. Faragó, István Avdzhieva, Ana Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy Sofia Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics & MTA-ELTE Research Group Budapest Hungary Faculty of Mathematics and Informatics "St. Kl. Ohridski" University of Sofia Sofia Bulgaria

ISBN: (数字)9783031324123

ISBN: (纸本)9783031324116

This paper considers one dimensional unsteady heat condition in a media with temperature dependent thermal conductivity. When the thermal conductivity depends on the temperature, the corresponding heat equation is nonlinear. At one or both boundaries, a relaxing boundary condition is applied. It is a time dependent condition that approaches continuously, as time increases, a certain time independent condition. Such behavior at the boundaries arises naturally in some physical systems. As an example, we propose a simple model system that can give rise to either Dirichlet or convective relaxing boundary condition. Due to the dependence of the thermal conductivity on the temperature, the convective condition is nonlinear. For the solution of the problem, we propose a new numerical approach that first discretizes the heat equation in time, whereby a sequence of two-point boundary value problems (TPBVPs) is obtained. We use implicit time discretization, which provides for unconditional stability of the method. If the initial condition is given, we can solve consecutively the TPBVPs and get approximations of the temperature at the different time levels. For the solution of the TPBVPs, we apply the finite difference method. The arising nonlinear systems are solved by Newton’s method. A number of example problems are solved demonstrating the efficiency of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary conditions

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