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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

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IEEE/ACM Transactions on Networking 2023年第5期31卷 2086-2100页

作者： Sahay, Rajeev Nicoll, Serena Zhang, Minjun Yang, Tsung-Yen Joe-Wong, Carlee Douglas, Kerrie A. Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Autonomy and Underwater Systems Division Saab Inc West LafayetteIN47907 United States STR WoburnMA01801 United States GoForward Inc. San FransiscoCA94104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States School of Engineering Education Purdue University West LafayetteIN47907 United States

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction. The code and four of the datasets used in this work are available at https://***/Jess-jpg-txt/sln-learning. © 2023 IEEE.

关键词： Social networking (online)

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One-Step Plasma Jet Deposition and Self-Sintering of Gold Nanoparticle Inks on Low-Temperature Substrates

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第5期3卷 197-204页

作者： Manzi, Jacob Varghese, Tony Eixenberger, Josh Prakasan, Lakshmi Estrada, David Subbaraman, Harish Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States Boise State University Department of Electrical and Computer Engineering Micron School of Material Science and Engineering BoiseID83725 United States Boise State University Micron School of Material Science and Engineering Center for Advanced Energy Studies BoiseID83725 United States Idaho National Laboratory Idaho FallsID83415 United States INFlex Labs LLC BoiseID83706 United States

Flexible electronics on low-temperature substrates like paper are very appealing for their use in disposable and biocompatible electronic applications and areas like healthcare, wearables, and consumer electronics. Plasma-jet printing (PJP) uses a dielectric barrier discharge plasma to focus aerosolized nanoparticles onto a target substrate. The same plasma can be used to change the properties of the printed material and even sinter in situ. In this work, we demonstrate the one-step deposition of gold structures onto flexible and low-temperature substrates without the need for thermal or photonic postprocessing. We also explore the plasma effect on the deposition of the gold nanoparticle ink. The plasma voltage is optimized for the sintering of the gold nanoparticles, and a simple procedure for manufacturing traces with increased adhesion and conductivity is presented, with a peak conductivity of 6.2\times 10{5} S/m. PJP-printed gold LED interconnects and microheaters on flexible substrates are developed to demonstrate the potential of this single-step sintered deposition of conductive traces on low-temperature substrates. © 2022 IEEE.

关键词： Sintering

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Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory 32

Improving Graph Machine Learning Performance Through Feature...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Said, Anwar Ahmad, Obaid Ullah Abbas, Waseem Shabbir, Mudassir Koutsoukos, Xenofon Vanderbilt University Computer Science Department NashvilleTN United States University of Texas at Dallas Electrical Engineering Department RichardsonTX75080 United States University of Texas at Dallas Systems Engineering Department RichardsonTX United States Information Technology University Computer Science Department Lahore Pakistan

ISBN: (纸本)9798350395440

Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how certain patterns of external control measures can steer system dynamics towards desired states. Distinguished from other structure-function methodologies, NCT's predictive capabilities can be coupled with deploying Graph Neural Networks (GNNs), which have demonstrated exceptional utility in various network-based learning tasks. However, the performance of G NN s heavily relies on the expressiveness of node features, and the lack of node features can greatly degrade their performance. Furthermore, many real-world systems may lack node-level information, posing a challenge for GNNs. To tackle this challenge, we introduce a novel approach, NCT-based Enhanced Feature Augmentation (NCT-EFA), that assimilates average controllability, along with other centrality indices, into the feature augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA, on six benchmark GNN models across two experimental setting-solely employing average controllability and in combination with additional centrality metrics-showcases an improved performance reaching as high as 11 %. Our results demonstrate that incorporating NCT into feature enrichment can substantively extend the applicability and heighten the performance of GNNs in scenarios where node-level information is unavailable. © 2024 IEEE.

关键词： Graph neural networks

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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Analysis of Effective IBR Actuators for Forced Oscillation Mitigation

Analysis of Effective IBR Actuators for Forced Oscillation M...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Lakshmi Thotakura, N Aga Zhao, Yi Liu, Yilu The University of Tennessee Department of Electrical and Computer Engineering KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798350352528

Forced Oscillations (FOs) occurring at frequencies close to a system's natural oscillation mode can induce resonance and widespread of high-amplitude oscillations. Different from mitigation approaches for natural oscillations, addressing FO mitigation requires special attention due to uncertainties of the FO source localization. Currently, accurate identification of FO source at the component level is time-consuming and unreliable. Supplementary FO damping control (FODC) through Inverter-Based Resources (IBR) is one state-of-the-art FO mitigation method. Selecting effective IBRs for FO mitigation is a critical step to guarantee the FO damping performance. This study explores how to select the most effective IBRs for damping FO resonant event with an inter-area natural oscillation. The study results in the l2l-bus mesh system show that the FODC through the IBRs near the FO source and close to the generators with high participation factor of natural oscillations can effectively mitigate FO events than other locations. © 2025 IEEE.

关键词： Damping

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A Fusion of Handcrafted Feature-Based and Deep Learning Classifiers for Heart Murmur Detection

A Fusion of Handcrafted Feature-Based and Deep Learning Clas...

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2022 Computing in Cardiology, CinC 2022

作者： Imran, Zaria Grooby, Ethan Malgi, Vinayaka Vivekananda Sitaula, Chiranjibi Aryal, Sunil Marzbanrad, Faezeh Electrical and Computer Systems Engineering Department Monash University Australia Electrical and Computer Engineering Department The University of British Columbia Canada School of Information Technology Deakin University Australia

ISBN: (纸本)9798350300970

As part of George B. Moody Physionet Challenge 2022, our team Melbourne Kangas, proposed an algorithm for identifying abnormal heart sounds from paediatric phono-cardiograms (PCGs). We developed a Deep Learning (DL) approach and a handcrafted feature-based approach. The DL classifier was based on bidirectional long-short-term-memory and Mel-frequency cepstrum coefficients from raw PCG signals. The feature-based approach used non-negative matrix factorisation to denoise PCG signals and then extracted the features based on the whole and segmented recordings, followed by feature selection. A random under-sampling boosting classifier for murmur classification and robust boosting classifier for outcome classification were given the subset of features. The feature-based performed better than the DL classifiers on the validation set. The feature-based classifier received a weighted accuracy of 0.632 (29th out of 41 teams) and a challenge cost of 11,735 (3rd out of 39 teams) on the test set. Decision fusion of the two approaches decreased 10-fold cross-validation results. © 2022 Creative Commons.

关键词： Classification (of information)

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Using Factor Analysis to Increase Accuracy of Neural Networks for Predicting Student Test Scores 54

Using Factor Analysis to Increase Accuracy of Neural Network...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Brown, Michael Scott Momtaz, Maliha Mai, Aaron Modi, Dhruvil University of Maryland Baltimore County Information Systems Department BaltimoreMD United States Hood College Computer Science and It Department FrederickMD United States Texas A&m College Station Computer Science and Engineering Department College StationTX United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States

ISBN: (纸本)9798350351507

This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research

关键词： Support vector machines

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Secure Communication With UAV-Enabled Aerial RIS: Learning Trajectory With Reflection Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Tang, Xiao Jiang, Tianqi Liu, Jinxin Li, Bin Zhai, Daosen Yu, F. Richard Han, Zhu School of Electronics and Information Northwestern Polytechinical University Xi'an China China Helicopter Research and Development Institute Jingdezhen China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Electrical and Computer Engineering University of Houston Houston USA

Reconfigurable intelligent surfaces (RISs) have manifested huge potential in enhancing information security by actively intervening the wireless propagation, yet the security gain may still be limited depending on the RIS deployment. In this paper, we propose to employ an unmanned aerial vehicle (UAV) mounting a RIS to enable on-demand reflection, noted as an aerial RIS (ARIS). The ARIS is then exploited to assist the anti-eavesdropping communications established through a conventional fixed-deployed RIS to further enhance the wireless secrecy. The secure communication is investigated by jointly optimizing the reflection at both RISs as well as the trajectory of the ARIS to maximize the average secrecy rate during the flight. To facilitate effective algorithm design, the formulated security problem is decomposed and solved in a double-layer framework. The outer layer tackles the flying trajectory through deep reinforcement learning while the inner layer solves for reflection phase shift design with manifold optimization. Finally, simulation results demonstrate the learned trajectory in various topologies as well as the superior performance of our proposal in terms of security provisioning. IEEE

关键词： Reinforcement learning

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Mitigating Voltage Instability in the Saudi Grid for a Decarbonized, Fully Solar Power System

Mitigating Voltage Instability in the Saudi Grid for a Decar...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Albukhari, Waleed M. Liu, Yilu Till, Abigail Alshuaibi, Khaled M. The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States King Saud University Department of Electrical Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350313604

Lately, solar photovoltaic (PV) has received significant interest due to its economic and environmental benefits. As the integration of renewable energy sources (RES) increases into existing power grids, challenges such as the decrease in short circuit ratio (SCR) are introduced. This paper investigates a case study of the Saudi power grid, examining the voltage stability as the grid transitions from traditional power generation to 100% penetration of solar PV gradually. Moreover, the paper explores the relationship between the increase in solar penetration and the potential effects on the short circuit MVA (SCMVA), which could significantly impact the overall SCR of the system. To meet the North American Electric Reliability Corporation (NERC) recommendation of maintaining SCR at a particular level, synchronous condensers (SC)s were integrated into the grid. The effectiveness of utilizing SCs to maintain the system voltage at optimal levels in a fully solar grid is also considered. In addition, this paper covers the weak grid analysis via utilizing the Newton-Raphson load flow method, along with PV and QV curve analyses. The purpose behind that is to determine weak bus locations in need of voltage improvements, and to meet the amount of reactive power to be injected. Finally, the amount of power being delivered by SCs will be added progressively in three scenarios to show the enhancements on SCR more precisely. © 2024 IEEE.

关键词： Reactive power

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