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GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

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Protection and Control of Modern Power Systems 2024年第6期9卷 108-121页

作者： Shiwei Xia Chenhui Zhang Yahan Li Gengyin Li Linlin Ma Ning Zhou Ziqing Zhu Huan Ma the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China the State Grid Shandong Dispatch and Control Center Jinan 250003China the State Grid Shandong Electric Power Research Institute Jinan 250001China the Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China

Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series ***,a data-driven method is proposed in this study,combining graph convolution network and long short-term memory network(GCN-LSTM)to analyze the transient power angle sta-bility by exploring the spatiotemporal disturbance char-acteristics of future power systems with high penetration of renewable energy sources(wind and solar energy)and power *** key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score,whereas the network adjacency matrix is constructed according to the system network *** normalized feature quan-tities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features,reflecting changes in the network ***,the spa-tial features are inputted into the LSTM network to ob-tain the temporal features,reflecting dynamic changes in the transient power angle of the ***,the spatiotemporal features are fused through a fully con-nected network to analyze the transient power angle stability of future power systems,and the softmax activa-tion cross-entropy loss functions are used to predict the stability of the *** proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system,and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power sys-tems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algo-rithms.

关键词： Future power system spatiotemporal disturbance characteristics transient power angle stabil-ity graph convolutional network long short-term memory network.

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Time-multiplexing Method Using Dual Ferroelectric Liquid Crystal Shutters for Light Field 3D Display

Time-multiplexing Method Using Dual Ferroelectric Liquid Cry...

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International Conference on Display Technology, 2024

作者： Sun, Zhi-Bo Yuan, Zheng-Nan Huo, Yi-Peng Dong, Shou-Cheng Cheung, Alex Vashchenko, Valerii Vashchenko, Olena Srivastava, Abhishek Kwok, Hoi-Sing State Key Laboratory of Advanced Displays and Optoelectronics Technologies Center for Display research Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

In light field displays, the resolution loss is a serious problem, leading to insufficient image depth, display size, and resolution. Here, we achieve a high refresh time multiplexing method that can effectively increase the display resolution by up to 32 times. Ferroelectric Liquid Crystal (FLC) with a fast response at 2 kHz and an optimized charge compensation method is proposed in this *** is very promising for light field 3D displays in the future. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Time division multiplexing

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Design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

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Progress In Electromagnetics research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani Das, Sudipta Devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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A Comparative Study of Validation Methods for Sensor-Based Human Activity Recognition Using Deep Learning Models 10

A Comparative Study of Validation Methods for Sensor-Based H...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Online Multicast Traffic engineering for Multi-View Videos With View Synthesis in SDN

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IEEE/ACM Transactions on Networking 2024年第4期32卷 2778-2793页

作者： Chiang, Sheng-Hao Wang, Chih-Hang Yang, De-Nian Liao, Wanjiun Chen, Wen-Tsuen Institute of Information Science Academia Sinica Taipei City115201 Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei City106319 Taiwan Institute of Information Science and the Research Center for Information Technology Innovation Academia Sinica Taipei City115201 Taiwan National Taiwan University Department of Electrical Engineering Taipei City106319 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Multi-view videos (MVV) have emerged to provide users with immersively interactive experiences with 3D multimedia content. Compared with traditional 2D videos, MVV offers multiple view angles to avoid generating occluded regions from a single viewpoint and allows users to receive different view angles, which consume much higher bandwidth. In this paper, we leverage multicast and view synthesis to effectively reduce the number of transmitted views and total bandwidth consumption in software-defined content delivery networks (SD-CDN). By exploiting the SDN architecture, SD-CDN can optimize traffic engineering and the selection of multi-view sources to serve users. We formulate a new optimization problem, Online Multicast with View Synthesis (OMVS), prove the NP-hardness, and design an online algorithm with the ideas of View Popularity Cost Ratio, View Watching Possibility, and synthesis tree, to achieve the tightest competitive ratio. The experiment on real networks and implementation in an experimental SDN manifest that the proposed algorithm outperforms state-of-the-art algorithms regarding the total cost, bandwidth consumption, synthesis quality, and link and node utilization. © 2024 IEEE.

关键词： Bandwidth

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Face Detection and Recognition in Near Infra-Red Image 6

Face Detection and Recognition in Near Infra-Red Image

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6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Shet, Athreya V Bs, Chinmay Shetty, Anvithkumar A Shankar, T. Hemavathy, R. Ramakanth, P. R v College of Engineering Computer Science and Engineering Department Bengaluru India R v College of Engineering Research and Projects Center for Cctv Research Bengaluru India

ISBN: (纸本)9781665456982

Face detection and recognition is an extensively researched topic in Artificial Intelligence. The use of AI in detection and mapping of faces or any objects can reduce the time spent in video auditing. A face recognition system maps facial traits from a picture or video using biometrics. To identify a match, it compares the data with a database of recognized faces. In any situation, facial recognition technology can intelligently assist in confirming a person's identification. Infra-Red camera or night vision in security cameras uses infrared light to capture near-infrared images in the dark and also through fog, dust and smoke, such that camera works in all conditions. Cameras operating in visual spectrum will work well only when the image capturing works well, but IR camera images can capture quality images at all times. The paper presents our work in creating a facial recognition system in near-infrared images from a surveillance camera, in which database was created in live Close Circuit Television footage for different individuals and the same was tested with different face detection techniques, classify images using the facial embeddings from VGG-face model to determine the faces. © 2022 IEEE.

关键词： Face recognition

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Abnormal Situation Simulation and Dynamic Causality Discovery in Urban Traffic Networks 15

Abnormal Situation Simulation and Dynamic Causality Discover...

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15th International Conference on Spatial Information Theory, COSIT 2022

作者： Wang, Yadi Pan, Yicheng Ma, Meng Wang, Ping School of Software and Microelectronics Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9783959772570

Various participants in urban traffic systems intertwine a highly complicated coupling network. An interpretable analysis of underlying correlations is one of the keys to understanding traffic anomalies. Unfortunately, abnormal situation analysis in real scenarios faces severe limitations in negative sample deficiency, data integrity, and verifiability. In view of this, we developed a simulation tool - the Traffic Anomaly Situation Simulator (TASS). Through configurable scripts, TASS simulates real traffic networks by road editing, data collection, and fault injection. Given the generated cases, we designed a dynamic causal discovery algorithm, Dycause-Traffic, to demonstrate the features of causality in traffic anomalies. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Traffic congestion

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