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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. University of Technology Smartgrid Research Center Durban Department of Electrical Power Engineering Department of Electronics and Computer Engineering Durban South Africa

ISBN: (纸本)9798350389388

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4-6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection. © 2024 IEEE.

关键词： Power transmission lines

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Advanced Helical Antenna Design for X-Band Applications Using AI

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Progress in Electromagnetics research C 2025年 153卷 201-211页

作者： Zeain, Mohammed Yousif Abu, Maisarah Toding, Apriana Zakaria, Zahriladha Alsariera, Hussein Ullah, Ihsan Abdulbari, Ali Abdulateef Yon, Hamizan Taha, Bilal Salman Inam Abbasi, Muhammad Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia Makassar Indonesia Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Information Technology Center University of Technology Baghdad Iraq Antenna Research Center College of Engineering School of Electrical Engineering Universiti Teknologi MARA Malaysia Department of Electrical and Electronics University Tenaga National Malaysia Malaysia

This paper presents the design, fabrication, and characterization of a novel 3D-printed helical antenna operating within the 9.4–10.8 GHz frequency band. The antenna, employing a lightweight paper substrate and a strip-based helical structure, exhibits robust circular polarization characteristics and wideband operation. Rigorous simulations predict a peak CP gain of 11.7 dBi at 9.8 GHz and a high simulated radiation efficiency of 95%. Experimental measurements validate these predictions, achieving a peak CP gain of 11.6 dBi at 9.8 GHz. This research demonstrates the potential of 3D-printed helical antennas for diverse applications in modern wireless communication systems, including 5G, satellite communication, and radar. Furthermore, this study leverages the power of Artificial Intelligence (AI) by employing the Grey Wolf Optimizer (GWO), a sophisticated metaheuristic algorithm, to optimize the antenna’s design. The GWO algorithm is utilized to efficiently search the design space and identify optimal values for key parameters, such as the number of turns, helix pitch, and helix diameter, with the objective of maximizing antenna gain to achieve a target of 15 dBi. This research highlights the potential of AI-driven optimization techniques in advancing the design of high-performance antennas for emerging wireless communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Helical antennas

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Location-guided Head Pose Estimation for Fisheye Image

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 682-697页

作者： Li, Bing Zhang, Dong Huang, Cheng Xian, Yun Li, Ming Lee, Dah-Jye Sun Yat-sen University School of Electronics and Information Technology China Duke Kunshan University Data Science Research Center Kunshan215316 China Brigham Young University Department of Electric and Computer Engineering United States

Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection. Serious fisheye lens distortion in the peripheral region of the image leads to degraded performance of the existing head pose estimation models trained on undistorted images. This paper presents a new approach for head pose estimation that uses the knowledge of head location in the image to reduce the negative effect of fisheye distortion. We develop an end-to-end convolutional neural network to estimate the head pose with the multi-task learning of head pose and head location. Our proposed network estimates the head pose directly from the fisheye image without the operation of rectification or calibration. We also created a fisheye-distorted version of the three popular head pose estimation datasets, BIWI, 300W-LP, and AFLW2000 for our experiments. Experiments results show that our network remarkably improves the accuracy of head pose estimation compared with other state-of-the-art one-stage and two-stage methods. © 2016 IEEE.

关键词： Convolutional neural networks

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Audio-guided implicit neural representation for local imagestylization

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Computational Visual Media 2024年第6期10卷 1185-1204页

作者： Seung Hyun Lee Sieun Kim Wonmin Byeon Gyeongrok Oh Sumin In Hyeongcheol Park Sang Ho Yoon Sung-Hee Hong Jinkyu Kim Sangpil Kim Department of Artificial Intelligence Korea UniversitySeoul 02841Republic of Korea NVIDIA Research Nvidia CorporationSanta ClaraCA 95051USA Graduate School of Culture Technology KAISTSeoul 34141Republic of Korea Hologram Research Center Korea Electronics Technology InstituteSeoul 03924Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul 02841Republic of Korea

We present a novel framework for audio-guided localized image *** often provides information about the specific context of a scene and is closely related to a certain part of the scene or ***,existing image stylization works have focused on stylizing the entire image using an image or text *** a particular part of the image based on audio input is natural but *** work proposes a framework in which a user provides an audio input to localize the target in the input image and another to locally stylize the target object or *** first produce a fine localization map using an audio-visual localization network leveraging CLIP embedding *** then utilize an implicit neural representation(INR)along with the predicted localization map to stylize the target based on sound *** INR manipulates local pixel values to be semantically consistent with the provided audio *** experiments show that the proposed framework outperforms other audio-guided stylization ***,we observe that our method constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input.

关键词： audio guidance image style transfer implicit neural representations(INR)

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Integrating Deep Learning with Structural Bioinformatics using Next-Generation Protein Stability Prediction 7

Integrating Deep Learning with Structural Bioinformatics usi...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jasmine Sugil, A. Merriliance, K. Soundiraraj, N. Manonmaniam Sundaranar University Tirunelveli India Department of Computer Applications And Research Center Tamilnadu Tirunelveli India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Dindigul India

ISBN: (纸本)9798350359299

Protein stability refers to the propensity of a protein molecule to maintain its native folded structure under various environmental conditions. Understanding protein stability is crucial as it directly influences protein function, activity, and interactions within biological systems. Accurate prediction of protein stability is therefore essential for elucidating protein structure-function relationships, guiding protein engineering efforts, and facilitating drug discovery processes. The integration of deep learning methodologies with structural bioinformatics presents a promising avenue for advancing next-generation protein stability prediction systems. This research introduces a novel algorithm, DeepCartesianDDG (DCD), which combines the predictive power of DeepVariantNet with the structural insights provided by the Rosetta 'cartesian-ddg' protocol. Through extensive simulation analysis, the proposed algorithm's performance is evaluated against existing algorithms commonly employed in protein stability prediction. Key simulation metrics including accuracy, precision, recall, and F1-score are utilized for comparison. The results demonstrate that DCD outperforms traditional algorithms, showcasing its efficacy in accurately estimating protein stability values. This research underscores the potential of integrating deep learning techniques with structural bioinformatics for enhancing protein stability prediction, thereby paving the way for advancements in protein engineering and drug discovery. © 2024 IEEE.

关键词： Proteins

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Discriminative Dimension Selection for Enhancing the Interpretability and Performance of Clustering Output 16

Discriminative Dimension Selection for Enhancing the Interpr...

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16th International Conference on Knowledge and Smart technology, KST 2024

作者： Lian, Hrang Kap Waiyamai, Kitsana Konstantinos, Slavakis Haruechaiyasak, Choochart Kasetsart University Department of Electrical Engineering Bangkok Thailand Kasetsart University Department of Computer Engineering Bangkok Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Tokyo Japan Artificial Intelligence Research Group National Electronics and Computer Technology Center Bangkok Thailand

ISBN: (纸本)9798350370737

Discriminative Dimension Selection (DDS) has emerged as a powerful tool for identifying the most relevant features in high-dimensional datasets, enabling interpretable data analysis and visualization. This paper explores the application of DDS which utilizes overlapping clusters and dimensions to enhance the interpretability and performance of the K-means clustering algorithm. Our approach leverages the post-processing capabilities of K-means to selectively retain informative features and discard redundant or irrelevant ones. This refined feature set not only preserves the clustering performance of K-means but also enhances its interpretability and visualization. We demonstrate the effectiveness of our method using a variety of datasets and compare its performance against traditional K-means and the proposed method Overlap-resolved Clustering (ORC). We also test with multiple cluster validity indices such as the Silhouette Coefficient Score (SS), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). Our results consistently show that ORC produces better clustering results and enhances the interpretability of the datasets. This study highlights the potential of DDS as a valuable tool for improving the interpretability and visualization of high-dimensional data analysis using K-means clustering. © 2024 IEEE.

关键词： Visualization

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