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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Makridis, Evagoras Grammenos, Andreas Oliva, Gabriele Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis University of Cyprus School of Engineering Department of Electrical and Computer Engineering Aglantzia Nicosia2109 Cyprus University of Cambridge Department of Computer Science and Technology CambridgeCB2 1TN United Kingdom University Campus Bio-Medico of Rome Via Alvaro Del Portillo Department of Engineering Roma21-00128 Italy

ISBN: (纸本)9798350316339

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a timevarying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OpenRC algorithm using column-stochasticity and masspreservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology. © 2024 IEEE.

关键词： Consensus algorithm

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Short-Term Wind Speed Forecasting Using Different LSTM Deep Learning Methods 5th

Short-Term Wind Speed Forecasting Using Different LSTM Deep ...

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5th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Dev, Upama Ahmad, Shameem Uddin, Mohammad Rabib Dhar, Ratnajit Mubarak, Hamza Hazari, Md. Rifat Mekhilef, Saad Seyedmahmoudian, Mehdi Stojcevski, Alex Alshammari, Obaid Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka1229 Bangladesh Department of EEE Chittagong University of Engineering and Technology Chattogram4349 Bangladesh School of Engineering and Built Environment Griffith University SouthportQLD4222 Australia School of Science Computer and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia Department of Electrical Engineering College of Engineering University of Hail Hail2240 Saudi Arabia

ISBN: (纸本)9789819791118

Accurate wind energy forecasting is crucial for Bangladesh as it seeks to diversify its energy mix and reduce dependence on fossil fuels. Moreover, precise predictions can optimize wind farm operations, enhancing grid stability and economic viability in the country's emerging renewable energy sector. This study explores wind energy potentiality for Inani Beach, Bangladesh, by forecasting wind speed using various deep learning models. To evaluate their performance based on important criteria, models including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)-LSTM, and CNN-BiLSTM (Bidirectional LSTM) were used. Input parameters included time, barometric pressure, height, and humidity for 2014–2015. The CNN-BiLSTM model was specifically proposed for wind speed potential forecasting and compared against other DL models. Results indicated the superior performance of CNN-BiLSTM compared to other algorithms, with CNN-LSTM showing the weakest performance. CNN-BiLSTM achieved the best results, boasting Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.36 and 0.52, respectively. In contrast, CNN-LSTM underperformed with MAE and RMSE values of 0.42 and 0.6, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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Deep Learning Approach for Predicting Output Current Ripple in Electric Vehicle Chargers 7

Deep Learning Approach for Predicting Output Current Ripple ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Madumathi, K. Saravanan, T.S. Khandan, K. Lakshmi Raja, R. Kannan, Sathish Rajendiran, M. Mahendra Institute of Technology Department of Electrical And Electronics Engineering Namakkal Tamil Nadu Mallasamudram637503 India Rajalakshmi Engineering College Department of Electrical And Electronics Engineering Chennai Tamil Nadu Thandalam602105 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Pandian Saraswathi Yadav Engineering College Department of Electronics And Communication Engineering Sivagangai Tamil Nadu Madurai630561 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350359299

Significant attention in the field of Electric Vehicle (EV) battery development has been directed towards enhancing the longevity of the batteries. EV batteries commonly experience severe power ripples throughout the charging and power conversion operations, which might cause battery deterioration, especially in low-temperature conditions. The term "ripple"generally refers to undesired variations in the AC voltage when applied to a DC power source. Ripple current is generated by motor drivers, power conversion elements, DC power outlets, on-board chargers, and brake charging. This research addresses the essential need for predicting ripple current to maintain the quality and longevity of EV batteries. The study used Deep Learning (DL) models, namely Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), to predict output current ripple in EV chargers. The EV charger circuit simulation provides a total of 336 data points, 300 of which are used for training and 36 are stored for model testing. Both models are evaluated using performance criteria throughout the testing phase. Bi-LSTM achieves a better coefficient of determination (R2), with a value of 0.9879. A range of error metrics are employed to conduct a full examination, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and root mean squared error (RMSE). The simulation findings reveal that Bi-LSTM significantly improves the prediction accuracy of output current ripple. © 2024 IEEE.

关键词： Long short-term memory

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Red Piranha Optimization Based Fast and Robust Fuzzy C-Means Clustering for Precise Glioma Segmentation in Therapeutic Applications 10

Red Piranha Optimization Based Fast and Robust Fuzzy C-Means...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sophia, G. Ramaraj, Kottaimalai Karthikeyan, K. Prasath Alias Surendhar, S. Ambika, B. Thilagaraj, M. Mv J College of Engineering Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Krishnankoil India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalayam India Aarupadai Veedu Institute of Technology Department of Biomedical Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

Gliomas have cells that resemble glial cells., which are normal brain cells. They are the prevalent kind of tumors;however., in patients with several years to live., they may be a lesser threat., or in patients with only two years to live., they may be more deadly. Chemotherapy and radiation are able to be utilized to decrease the development of brain tumors which are unable to be practically taken away even though medical intervention is still the preferred therapy for these tumors. Magnetic resonance imaging (MRI) for tumor diagnosis because they offer comprehensive images of the structure of the brain. Accurately segmenting tumors from MRls is crucial for both preparing for surgery and diagnostic purposes. Further investigations may not be able to justify manual tumor segmentation. Furthermore., tumor delineation can significantly influence the scheduling of treatment., rate of growth forecasting., and better diagnosis. The present study uses an integrated segmentation algorithm that combines optimization and clustering techniques. One method for creating clusters or groups of identical pixels is fuzzy C-means (FCM) clustering., an unsupervised learning technique. Fast and Robust FCM (FRFCM) addresses the drawbacks of traditional method. Red Piranha Optimization (RPO) is a recently introduced optimization technique that draws inspiration from nature and emulates the community-based hunting act of red piranhas. The suggested RPO-based FCM method was evaluated on a number of BraTS challenge datasets to guarantee its effectiveness. The results of the dissection are contrasted with the corresponding ground truth images. The enhanced dice score value of 92 % is obtained using the developed method. Medical professionals can use findings to help schedule surgical procedures., diagnose patients promptly, and treat them. © 2024 IEEE.

关键词： Chemotherapy

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Blockchain-based Decentralized Stochastic Energy Management in IoT-enabled Smart Grid with Voltage Regulation Coordination

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IEEE Internet of Things Journal 2025年

作者： Zhou, Xiaotian Honari, Kimia Liang, Hao Rouhani, Sara Dick, Scott University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada University of Manitoba Department of Computer Science WinnipegMB Canada

In this paper, a blockchain-based decentralized stochastic energy management scheme is proposed for smart grid-connected households with photovoltaic generation and battery energy storage systems. The proposed scheme autonomously solves the joint optimization problem of maximizing individual household benefits while also minimizing total line loss in the distribution system. Internet-of-Things (IoT) technology is essential for the optimization as continuous sensing of the household power and battery states, coupled with autonomous bidding in voltage regulation auctions, are vital ingredients of this energy management scheme. A double-auction algorithm for coordinated voltage regulation with IoT access control is designed and implemented as a smart contract, which enables proactive voltage issue diagnosis while preserving privacy. The proposed blockchain-based decentralized stochastic energy management scheme consists of two subschemes, corresponding to individual households and the distribution system, respectively. Two decentralized stochastic energy management schemes are proposed for the blockchain platform. Specifically, an individual household energy management scheme is developed to maximize the benefit for each residence, while a collaborative energy management scheme is proposed to optimize the system's joint benefit by minimizing the total line loss. Both schemes are modeled as decentralized Markov decision processes, thus avoiding the need for centralized computation. To enhance the scalability of the smart contract, a novel pruning-based auction algorithm for voltage regulation is developed. A case study on the IEEE 123-Node Test Feeder indicates that residential voltage regulation can be coordinated effectively through the proposed scheme. The algorithm is computationally efficient, with computation time scaling logarithmically with the number of participants. © 2025 IEEE.

关键词： Blockchain

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Generalized Neural Collapse for a Large Number of Classes 41

Generalized Neural Collapse for a Large Number of Classes

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41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Jiachen Zhou, Jinxin Wang, Peng Qu, Qing Mixon, Dustin G. You, Chong Zhu, Zhihui Department of Computer Science The Ohio State University ColumbusOH United States Department of Electrical Engineering & Computer Science University of Michigan Ann ArborMI United States Department of Mathematics The Ohio State University ColumbusOH United States Google Research New York CityNY United States

Neural collapse provides an elegant mathematical characterization of learned last-layer representations, also known as features, and classifier weights within deep classification models. The result not only provides insights into deep models but also catalyzes the development of new techniques for improving them. However, most of the existing empirical and theoretical studies into neural collapse center around scenarios where the number of classes is small relative to the dimensionality of the feature space. This paper introduces a generalization of neural collapse to encompass scenarios where the number of classes surpasses the dimension of feature space, which broadly occurs for language models, information retrieval systems, and face recognition applications. A key technical contribution is the introduction of the concept of softmax code, defined as a collection of points that maximizes the minimum one-vs-rest margin, to describe the arrangement of class-mean features. We provide empirical study to verify the prevalence of generalized neural collapse in practical deep neural networks. Moreover, we provide theoretical study to show that the generalized neural collapse provably occurs under an unconstrained feature model with spherical constraint, subject to specific technical conditions on feature dimension and the number of classes. Copyright 2024 by the author(s)

关键词： Face recognition

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Age of Actuation in a Wireless Power Transfer System

Age of Actuation in a Wireless Power Transfer System

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Nikkhah, Ali Ephremides, Anthony Pappas, Nikolaos Linköping University Department of Computer and Information Science Linköping Sweden University of Maryland Electrical and Computer Engineering College ParkMD United States

ISBN: (纸本)9781665494274

In this paper, we study a model relevant to semantics-aware goal-oriented communications. More specifically, observations from an external process are transmitted through status updates to a battery-powered receiver. From these updates, the receiver is informed about the status of the process and if there is sufficient energy, uses them to perform an actuation to achieve a goal. We consider a wireless power transfer model where the destination receives energy from a dedicated power transmitter and occasionally from the data transmitter. We provide the analysis for the Age of Information (AoI). Furthermore, we propose a new metric, namely the Age of Actuation (AoA) which is relevant when the receiver utilizes the status updates to perform actions in a timely manner. We analytically characterize the AoA and we show that is a more general metric than AoI. We provide the optimization problems for both metrics, and we numerically evaluate our analytical results. © 2023 IEEE.

关键词： Energy transfer

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Liver Disease Prediction Using Ensemble Technique 8

Liver Disease Prediction Using Ensemble Technique

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Tanuku, Sai Rohith Kumar, Addike Ajay Somaraju, Sai Roop Dattuluri, Rushitaa Reddy, Madana Vamshi Krishna Jain, Sambhav Manipal University Department of Computer and Communication Engineering Manipal India Cmr College Department of Computer Science Engineering Telangana India Jntua College of Engineering Department of Electrical and Communication Technology Kalikiri India Ssn College of Engineering Department of Computer Science Engineering Maharashtra Ahmedabad India Bml Munjal University Department of Computer Science Engineering Haryana India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Patiala India

ISBN: (纸本)9781665408165

Liver illness is one of the worst diseases on the planet. It occurs in the human body, most notably in the liver. The liver's primary function is to eliminate waste created by organisms, to store key vitamins required by the body so that they do not go to waste, and to digest meals. This is a highly terrible disease, and the first thing that has to be done is to limit the risk explored by this lethal disease, and early detection can assist save the organism. The amount of people that are disease in the world is approx. 3.5 percent. The number of advancements that are happening in prediction of the disease done through the help of machine learning classification techniques like KNN, random forest SVM, and logistic regression. Other deep learning methods are also incorporated to solve this problem such as artificial neural network and convolution neural network. The methods would definitely increase the life expectancy of the patient suffering from this disease and avoid the chronic liver disease (CLD). The data may be gathered in enormous quantities as a result of the widespread use of bar codes for superior marketable items, the automation of many commercial and government transactions, and the advancement of data gathering systems. The proposed system that has been used ensemble methods such as random forest, xgboost and gradient boost and are combined to get a greater accuracy. © 2022 IEEE.

关键词： Forecasting

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Analysis of Google Play Store's Sentiment Review on Indonesia's P2P Fintech Platform

Analysis of Google Play Store's Sentiment Review on Indonesi...

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Amrie, Syahrul Kurniawan, Sandy Windiatmaja, Jauzak Hussaini Ruldeviyani, Yova Universitas Indonesia Faculty of Computer Science Department of Information Technology Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Computer Science Jakarta Indonesia Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Computer Science Depok Indonesia

ISBN: (纸本)9781665458832

Nowadays there are so many mobile phone-based investment applications, ranging from mutual funds, stocks, and P2P lending. While these investment applications are gaining huge attraction among the general masses, sometimes selecting the right platform still becomes a hot issue. This research aimed to analyze the sentiment on P2P lending applications and to determine the user's response due to the increase in the number of funds distribution during the COVID-19 pandemic. By doing so, this research could give some insight into the new and existing user. Data was obtained through assessment reviews on the Play store platform for the P2P A, P2P B, and P2P C applications. Assessment reviews were classified by using a data mining approach, TF-IDF feature extraction, and Naïve-Bayes classification method. This research showed that P2P A got 77% positive sentiment and 23% negative sentiment, P2P B got 36% positive sentiment and 64% negative sentiment, and P2P C got 68% positive sentiment and 32% negative sentiment. From the results of the study, it was found that P2P A got better results than both P2P B and P2P C. those were 77% positive sentiment with 23% negative sentiment in finance topic, 56% positive sentiment with 44 % negative sentiment in account verification topic, 79% positive sentiment with 21% negative sentiment in apps review, and 99% positive sentiment with 1% negative sentiment in referral topic. © 2022 IEEE.

关键词： Sentiment analysis

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