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conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.

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ieee CCECE2002. canadian conference on electrical and computer engineering. conference Proceedings (Cat. No.02CH37373)

IEEE CCECE2002. Canadian Conference on Electrical and Comput...

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canadian conference on electrical and computer engineering (CCECE)

The following topics are dealt with: power system modelling, planning and operation; power electronics and machines; electromagnetics and optics; antenna theory, design and applications; remote sensing; SAR ; acoustic ranging; microelectronics and VLSI; nanotechnology and micromachining; instrumentation and sensors; circuits and systems; robotics and mechatronics; reliability engineering; computers and digital hardware; real-time systems; software and information technology; computational intelligence; neural networks; genetic algorithms and fuzzy logic; pattern recognition; image processing; video processing; signal processing and filter design; biomedical engineering; health-care systems; communications systems; computer networks; wireless networks; telecommunication traffic analysis; QoS; industrial applications.

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canadian conference on electrical and computer engineering 2004 (ieee Cat. No.04CH37513)

Canadian Conference on Electrical and Computer Engineering 2...

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canadian conference on electrical and computer engineering (CCECE)

The following topics are dealt with: OFDM; mobile networks; optical networks; VLSI design; image and video processing; power systems; security applications; signal processing algorithms; software engineering; MIMO systems; fading channels and equalizers; quality of service; high speed circuits; circuits and applications; multimedia signal processing; power quality; optical wireless access networks; modulation and coding; CDMA; wireless LAN; switching, routing and networking; abstraction, modelling and simulation; watermarking and retrieval; speech and audio processing; transmission and distribution systems; computer architecture; ad-hoc networks; biomedical signal and image analysis; power converters and motor drives; embedded systems; fuzzy systems and neural networks; FPGA and system on chip; radio resource management in next generation wireless networks; space time coding; internetworking and protocols; RF circuits; information and intelligent systems; power systems stability and renewable energy systems; control systems; electric machines; communications; robotics; agent-based systems.

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ieee canadian Journal of electrical and computer engineering

IEEE Canadian Journal of Electrical and Computer Engineering

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ieee canadian Journal of electrical and computer engineering

作者： Dr. Mohamed S. Shehata

出版社：IEEE CANADA

The ieee canadian Journal of electrical and computer engineering, issued quarterly, has been publishing high-quality refereed scientific papers in all areas of electrical and computer engineering since 1976. Sponsored by ieee Canada (The Institute of electrical and Electronics Engineers, Inc., Canada) as a part of its role to provide scientific and professional activity for its members in Canada, the CJECE complements international journals and will be of particular interest to anyone involved in research and development activities in the field of electrical and computer engineering.

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Young Professional Events at the 2024 International Microwave Biomedical conference [Young Professionals]

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ieee Microwave Magazine 2025年第1期26卷 92-93页

作者： Meo, Simona Di University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy

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Guest Editorial: Special Issue of Selected Extended Papers From the ieee International conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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ieee Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Performance Analysis of Intelligent Classifiers for High Impedance Fault Detection in a PV-Integrated ieee-13 Bus System

IEEE Canadian Journal of Electrical and Computer Engineering

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ieee canadian Journal of electrical and computer engineering 2025年第2期48卷 98-108页

作者： Mahzan, Najwa Nasuha Othman, Mohammad Lutfi Wahab, Noor Izzri Abdul Veerasamy, Veerapandiyan Salim, Nur Ashida Abidin, Aidil Azwin Zainul Islam, Syed Zahurul Group Department of Electrical and Electronics Engineering Faculty of Engineering Serdang43400 Malaysia School of Electrical and Electronic Engineering Singapore639798 Singapore School of Electrical Engineering College of Engineering Selangor Shah Alam40450 Malaysia Department of Electrical and Electronic Engineering Putrajaya Campus Selangor Kajang43000 Malaysia Faculty of Electrical and Electronic Engineering Department of Electrical Engineering Parit Raja Johor86400 Malaysia

High impedance faults (HIFs) present significant challenges in power systems, particularly when an electrical wire contacts a high-resistance material, leading to low currents that are difficult for traditional relays to detect. With the increasing integration of photovoltaic (PV) systems, these challenges are exacerbated due to the complex behavior of PV-generated signals. This study aims to enhance the detection of HIFs in PV-integrated systems using advanced machine learning techniques. The approach employs various classifiers, including artificial neural networks, support vector machines (SVMs), decision trees, and random forest (RF) to improve fault identification accuracy. A MATLAB/SIMULINK simulation was conducted on an ieee 13-bus system with a 300-kW solar PV plant. The discrete wavelet transform (DWT) with the db4 wavelet was used for feature extraction, focusing on phase energy values. The classifiers were evaluated under different scenarios, such as normal operation, load switching (LS), capacitor switching (CS), HIF, and line-to-ground (LG) faults. The RF classifier outperformed others, achieving a fault detection accuracy of 99.4083%, demonstrating its robustness in adapting to various fault conditions. The Naive Bayes (NB), multilayer perceptron (MLP), and logistic regression (LR) classifiers achieved lower accuracies of 78.6982%, 76.9231%, and 80.4734%, respectively. These results indicate a significant improvement in fault detection capability, enhancing the stability, reliability, and resilience of electrical grids integrated with PV systems. The findings suggest that the RF classifier is highly effective for HIF detection, which is crucial for the protection and efficient operation of modern power grids with high renewable energy penetration. © 2025 ieee.

关键词： Decision trees

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Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks

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ieee Transactions on Automatic Control 2025年第5期70卷 2843-2858页

作者： Wang, Xuan Cortés, Jorge George Mason University Department of Electrical and Computer Engineering FairfaxVA22030 United States University of California San Diego Department of Mechanical and Aerospace Engineering La Jolla CA92093 United States

This article studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly nonconvex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, nonconvex objective function. We prove that the minimizer of the objective function is unique and establish that the solution of the optimization problem leads to the identification of all the desired system parameters. These results are the basis to introduce an algorithm to find the optimizer by searching the different regions corresponding to the domain of definition of the objective function. To deal with measurement noise in sampled data, we propose a modification of the original algorithm whose identification error is linearly bounded by the magnitude of the measurement noise. We demonstrate the effectiveness of the proposed algorithms through simulations on synthetic and experimental data. © 1963-2012 ieee.

关键词： Velocity measurement

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