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arXiv 2025年

作者： Anastasiou, Andreas Papaioannou, Savvas Kolios, Panayiotis Panayiotou, Christos G. Cyprus Department of Electrical and Computer Engineering Cyprus Department of Computer Science University of Cyprus Cyprus

Nowadays, unmanned aerial vehicles (UAVs) are increasingly utilized in search and rescue missions, a trend driven by technological advancements, including enhancements in automation, avionics, and the reduced cost of electronics. In this work, we introduce a collaborative model predictive control (MPC) framework aimed at addressing the joint problem of guidance and state estimation for tracking multiple castaway targets with a fleet of autonomous UAV agents. We assume that each UAV agent is equipped with a camera sensor, which has a limited sensing range and is utilized for receiving noisy observations from multiple moving castaways adrift in maritime conditions. We derive a nonlinear mixed integer programming (NMIP) -based controller that facilitates the guidance of the UAVs by generating non-myopic trajectories within a receding planning horizon. These trajectories are designed to minimize the tracking error across multiple targets by directing the UAV fleet to locations expected to yield targets measurements, thereby minimizing the uncertainty of the estimated target states. Extensive simulation experiments validate the effectiveness of our proposed method in tracking multiple castaways in maritime environments. © 2025, CC BY-NC-SA.

关键词： Drones

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Data-Driven Diagnosis of Multi-Class Open-Circuit Switch Faults in Three-Phase Front-End Converters

Data-Driven Diagnosis of Multi-Class Open-Circuit Switch Fau...

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2024 International Conference on computer, Electronics, electrical engineering and their Applications, IC2E3 2024

作者： Ghosh, Sourabh Singh, Vikash Singh, Asheesh Kumar Hassan, Ehtesham Singh, Sri Niwas Motilal Nehru National Institute of Technology Allahabad Electrical Engineering Department Prayagraj India Kuwait College of Science and Technology Department of Computer Science and Engineering Kuwait Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350388534

This paper analyzes the application of benchmark classification techniques for multi-class diagnosis of open-circuit switch faults (OCSFs) in three-phase front-end converters (TP-FECs). OCSFs result in harmonic distortion, secondary faults, and unscheduled shutdowns in power utilities. Moreover, due to the inherent difference between OCSFs and short-circuit switch faults, the same diagnosis techniques do not apply to both types of faults. This paper presents a detailed study on a two-level TP-FEC to generate single-, double-, and triple-switch OCSFs, resembling real-world scenarios. The single-moment fault data with 624 observations is captured through 13 distinct features to extract the fault characteristics during each of the 38 classes of OCSFs under varying load demands. Later, the paper analyzes the performance of neural networks (NNs), support vector machines (SVMs), ensemble bagged trees (EBTs), and k-nearest neighbours (KNNs) for multi-class diagnosis of OCSFs. The evaluation employed cross-validation with optimal hyperparameter combinations to train different models. Through rigorous experiments, we demonstrate the superiority of NNs in classifying multi-class OCSFs with a testing accuracy of 97.50%, achieving an improvement of 0.4%, 2.4%, and 3.8% compared to SVM, KNN, and EBT, respectively. Furthermore, our experiments underscore the efficacy of NNs and SVMs over other state-of-the-art methods for diagnosing OCSFs. © 2024 IEEE.

关键词： Harmonic distortion

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Price-Discrimination Game for Distributed Resource Management in Federated Learning

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 124-128页

作者： Zhang, Han Yang, Halvin Zhang, Guopeng China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom

In federated learning (FL) systems, the parameter server (PS) and clients form a monopolistic market, where the number of PS is far less than the number of clients. To improve the performance of FL and reduce the cost to incentive clients, this letter suggests distinguishing the pricing of FL services provided by different clients, rather than applying the same pricing to them. The price is differentiated based on the performance improvements brought to FL by clients and their heterogeneity in computing and communication capabilities. To this end, a price-discrimination game (PDG) is formulated to comprehensively address the distributed resource management problems in FL, including multi-objective trade-off, client selection, and incentive mechanism. As the PDG includes a mixed-integer nonlinear programming problem, a distributed semi-heuristic algorithm with low computational complexity and low communication overhead is designed to solve the Nash equilibrium (NE) of the PDG. The simulation result verifies that the NE achieves a good tradeoff between the training loss, training time, and the cost of motivating clients to participate in FL. © 2019 IEEE.

关键词： Energy utilization

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Multi-Hop D2D Cluster Formation and Resource Allocation for Scalable Video Multicast 22

Multi-Hop D2D Cluster Formation and Resource Allocation for ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Nie, Hao Wang, Wei Dai, Rui Liu, Chenyan Wang, Zhixing Xu, Peng Huazhong University of Science and Technology Wuhan China University of Cincinnati Department of Electrical Engineering and Computer Science Cincinnati United States Centrin Data Vally Technology Co. Ltd Wuhan China

ISBN: (纸本)9798331509712

Scalable video coding (SVC) associated with adaptive modulation and coding (AMC) has provided an excellent solution for transmitting real-time videos. However, some users have bad channel conditions with the base station (BS) and need help getting a good quality of service (QoS). With the fifth generation (5G) of the cellular network, these users can also receive most of the data by device-to-device (D2D) transmissions. In this paper, we focus on live video delivery scenarios. We adopt the multicast Multi-Hop D2D and reuse spectrum spatially to help broadcast the scalable videos. We aim to maximize all users' average quality of service (QoS) by jointly optimizing D2D cluster heads (CHs) selection and channel reuse. The problem is an integer non-linear programming problem, and we decompose the problem into two subproblems: the D2D cluster formation problem and the resource allocation problem. To solve the first subproblem, we propose a greedy algorithm to form D2D clusters that can broadcast more efficiently. We convert the second subproblem to a combination of two partial graph coloring problems in two conflict graphs. An algorithm similar to the recursive largest first (RLF) algorithm but considering the conflicts in both graphs is provided for the second subproblem. We execute numerical simulations to validate the performance of our proposal. © 2024 IEEE.

关键词： Scalable video coding

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LSTM Network Assisted Content Caching at the Edge for Video on Demand 5

LSTM Network Assisted Content Caching at the Edge for Video ...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Darwich, Mahmoud Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350338287

Video-on-demand (VoD) services often suffer from long start-up delays and poor streaming quality due to distance from centralized data centers. In this work, we develop a long short-term memory (LSTM) network to predict future video requests and intelligently cache content at the edge for optimized VoD delivery. We train the LSTM model on a large proprietary dataset of VoD access logs capturing real-world sequential patterns. Using caching simulations, our approach increases cache hit ratios by 56% compared to recency-based caching and 41% over popularity-based schemes. By proactively caching predicted videos at the edge, our method reduces average video startup time by 32% and decreases upstream bandwidth consumption by 48%. These substantial gains demonstrate the value of forecasting VoD popularity using LSTMs and harnessing edge computing to cache content closer to viewers. Our intelligent edge caching system can improve quality of experience for users and lower costs for providers. © 2023 IEEE.

关键词： Long short-term memory

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Hybrid Classification Method for Anomaly Detection in Injection Moulding Process 6

Hybrid Classification Method for Anomaly Detection in Inject...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xuan, Ng Yao Ge, Hailin Feng, Lu Wen Nam, Doan Ngoc Chi Li, Xiang National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore National University of Singapore Department of Mechanical Engineering Singapore

ISBN: (纸本)9798350356618

Industrial and environmental challenges faced in injection moulding processes push manufacturing companies to embrace smart manufacturing and adopt machine learning to deal with anomaly detection. Challenges arisen from imbalanced dataset gives rise to the development of our novel hybrid classification method. A novel hybrid classification method for anomaly detection in injection moulding is proposed in this paper. The proposed method involves feature selection with K-Means Clustering to remove noise data prior to Multi-Layer Perceptron. Feature selection helps to reduce the high-dimension of the dataset. K-Means Clustering helps to remove noise data that will have similar patterns with data of another class label. Multi-Layer Perceptron is used to verify the training accuracy and to build train model. Real industrial dataset of injection moulding process from a local manufacturing company is used to demonstrate the effectiveness of the method in this paper. The proposed hybrid method outperforms other classical models such as Multi-Layer Perceptron (MLP), Decision Tree (DT) and CNN (Convolutional Neural Network). It successfully increases the failure detect rate and reduces false reject rate up to 90%. © 2024 IEEE.

关键词： Smart manufacturing

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Battery Swapping System for the Electric Vehicles 3

Battery Swapping System for the Electric Vehicles

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pradhan, Rohan Saxena, Shaswat Singh, Akarsh Kumar Ali, Jabir Srm Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad India Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9781665462006

As the whole world is diverting toward electric vehicles, keeping in mind India's constantly deteriorating pollution conditions (3), India is trying its best to adopt electric cars in its mainstream vehicle market. As most companies are already launching their electric cars, charging them is time-consuming and might hold us back in urgent situations. This paper aims to create an application to facilitate people with a quick alternative to the conventional battery charging method by leveraging the method of battery swapping (1). This would be helpful in emergencies. This application is named 'Swap Easy' because it makes battery swapping easy, quick, and convenient. © 2022 IEEE.

关键词： Electric vehicles

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An Investigation into the Practical Use of Internet of Things Technology in Smart City Applications 1

An Investigation into the Practical Use of Internet of Thing...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Das, Rashmi Prava Muduli, Debendra Luhach, Ashish Kumar CV Raman Global University Computer Science and Engineering Department Bhubaneswar India Papua New Guinea University of Technology Department of Electrical and Communication Engineering Morobe Lae Papua New Guinea

ISBN: (纸本)9798350361780

This study presents a comprehensive method of mixing randomly generated simulated datasets to analyze several Internet of Things (IoT) applications in the context of smart cities Using matplotlib and numpy libraries, combined with Python programming language, analysis simulate real-time data on temperature, air quality and energy consumption. The learning methodology includes making pie charts, line plots, bar graphs etc. to present the findings graphically for understanding the application of Internet of Things in smart cities. Devery of smart city experiences, weather water system, traffic reduction, temperature over time, smart homes, smart parking areas. From temperature change to smart home to parking lot and city specific of car reduction experiences, each model provides insight into the dynamic features of a smart city implementation method of learning that combines visual techniques and simulated randomly produced information providing a strong foundation for analyzing potential technological impacts Enhances the discussion on smart and sustainable urban development towards the goal of creating resilient and adaptive smart cities. © 2024 IEEE.

关键词： Data visualization

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The use of PSS2A system stabilisers to damp electromechanical swings in medium voltage networks with distributed energy sources

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Archives of electrical engineering 2022年第3期71卷 717-719页

作者： Paszek, Stefan Nocoń, Adrian Pruski, Piotr Department of Electrical Engineering and Computer Science Silesian University of Technology Akademicka 10 str. Gliwice44-100 Poland

In this paper, the design issue of effective damping of electromechanical swings in a medium voltage network with distributed generation by the use of a PSS2A type power system stabiliser is described. This stabiliser was installed in the generating unit with the highest rated power. Time constants of correction blocks, as well as the main gain, were determined by analyzing a single-machine system, generating unit – infinite bus. The time constants were calculated on the basis of the frequency-phase transfer functions both of the electromagnetic moment to the voltage regulator reference voltage and of the generator voltage to the voltage regulator reference voltage, under the assumption of an infinite and real value of the generating unit inertia time constant for various initial generator loads. The main stabiliser gain was calculated by analyzing the position, on the complex plane, of eigenvalues of the state matrix of the single-machine system, linearised around a steady operating point, at the changed value of this gain. © 2022 Polish Academy of sciences. All rights reserved.

关键词： Voltage regulators

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