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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Foqha, Tareq Alsadi, Samer Refaat, Shady S. Arab American University Department of Electrical Engineering Jenin Palestine Palestine Technical University-Kadoorie Department of Electrical Engineering Tulkarm Palestine University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798350306262

Microgrids are gaining popularity due to their improved reliability and resilience. However, their protection is challenging due to bidirectional current flow and varying fault current levels in different operating modes. This paper proposes the use of dual-setting directional overcurrent relays for microgrid protection. To demonstrate their effectiveness, a comparative analysis of dual-setting relays and conventional relays is conducted. The relay coordination problem is formulated as a nonlinear programming problem, and the settings of relays are optimally determined using genetic algorithm and an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to minimize the overall relay operating time for primary relays. The proposed scheme is implemented on the distribution section of the IEEE-14 bus system, demonstrating a reduction in total system operating time with dual-setting directional overcurrent relays. © 2024 IEEE.

关键词： Genetic algorithms

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Traffic Density Estimation by Distributed Proxy Model Learning for Internet-of-Vehicle

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IEEE Internet of Things Journal 2025年第12期12卷 19972-19980页

作者： Li, Qilei Cheng, Jing-An Gao, Mingliang Chen, Jinyong Jeon, Gwanggil Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

Autonomous driving has been significantly advanced in todays society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understanding traffic density to enable intelligent traffic management. With the rapid improvement in deep neural networks (DNNs), the accuracy of density estimation has markedly improved. However, there are two main issues that remain unsolved. Firstly, current DNN-based models are excessively heavy, characterized by an overwhelming number of training parameters (millions or even billions) and substantial computational complexity, indicated by a high number of FLOPs. These requirements for storage and computation severely limit the practical application of these models, especially on edge devices with limited capacity and computational power. Secondly, despite the superior performance of DNN models, their effectiveness largely depends on the availability of large-scale data for training. Growing privacy concerns have made individuals increasingly hesitant to allow their data to be publicly used for model training, particularly in vehicle-related applications that might reveal personal movements, which leads to data isolation issues. In this paper, we address these two problems at once with a systematic framework. Specifically, we introduce the Proxy Model Distributed Learning (PMDL) model for traffic density estimation. PMDL model is composed of two main components. First, we introduce a proxy model learning strategy that transfers fine-grained knowledge from a larger master model to a lightweight proxy model, i.e., a proxy model. Second, we design a distributed learning strategy that trains multiple proxy models with privacy-aware local data and seamlessly aggregates these models via a global parameter server. This ensures privacy protection while significantly improving estimation performance compared to training models with limited, isolated data. We tested

关键词： Differential privacy

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Influence Distribution for Misinformation Containment Under Competitive Activation Models

Influence Distribution for Misinformation Containment Under ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gu, Ming Chen, Wei-Neng Hu, Xiao-Min Jeon, Sang-Woon School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9781665410205

The widespread adoption of social networks facilitates the dissemination of authentic information while also accelerating the spread of misinformation, such as rumors. The propagation of positive information can enhance user awareness and mitigate the hazards of misinformation. The misinformation containment (MC) problem aims to identify a set o k nodes that initiate the spread of positive information, maximizing its influence while minimizing the hazards of misinformation. The greedy approach, which employs extensive Monte Carlo simulations to estimate influence, is time-consuming and can only prioritize either propagation or containment, but not both. This paper studies the MC problem under competitive activation models. Based on geometric models of probability, we calculate the approximate probabilities of nodes being activated by positive information and misinformation at various times. Taking into account the two-hop theory, we propose a consistent and efficient computational method to assess node influence distribution from the perspectives of propagation and containment. This method strikes a balance between propagation and containment, surpassing degree centrality, further informing a heuristic solution to the MC problem. The heuristic solution's overall performance surpasses that of greedy approaches, which can only prioritize one aspect. Experiments on real-world networks demonstrate that our approach effectively balances the propagation of positive information and misinformation containment with low time complexity. © 2024 IEEE.

关键词： Information dissemination

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Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

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Energy engineering 2023年第1期120卷 69-86页

作者： Saeed Akbari Seyed Saeed Fazel Hamed Hashemi-Dezaki School of Railway Engineering Iran University of Science and TechnologyTehran13114-16846Iran Department of Electrical and Computer Engineering University of KashanKashan***Iran Research and Innovational Center for Electrical Engineering(RICE) Faculty of Electrical EngineeringUniversity ofWest Bohemia(UWB)Pilsen30100Czech Republic

The networking of microgrids has received significant attention in the form of a smart *** this paper,a set of smart railway stations,which is assumed as microgrids,is connected *** has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility ***,the operational costs of stations under various conditions decrease by applying the proposed *** smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking *** regenerative braking is one of the essential ***,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based *** networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are *** proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB ***,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

关键词： Energy management system(EMS) smart railway stations coordinated operation photovoltaic generation regenerative braking uncertainty scenario-based model mixed-integer linear programming(MILP)

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Detection of Malicious URLs using Ensemble learning techniques

Detection of Malicious URLs using Ensemble learning techniqu...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Arjun, D Shashank Samhitha, D Sai Padmavathi, A. Hemprasanna, A. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electrical and Computer Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350384659

The growing risk of cyber-attacks and information vulnerability has become a major problem in today's dynamic digital environment. The necessity for strong security solutions is more critical than ever due to the development of attack techniques and the spread of social engineering strategies. Hackers commonly utilize malicious Uniform Resource Locators (URLs) to trick users and launch different kinds of attacks. This research uses the power of ensemble learning to tackle this problem by mixing various machine learning techniques like XGBoost, Extra Tree Classifier, and Random Forest. This ensemble model not only performs exceptionally well at identifying hazardous URLs, but it also provides a scalable cybersecurity solution, acting as a protector against developing online threats. © 2023 IEEE.

关键词： Machine learning

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction 23

Self-training through Classifier Disagreement for Cross-Doma...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Sun, Kai Zhang, Richong Samuel, Mensah Nikolaos, Aletras Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781450394161

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023 ACM.

关键词： Sentiment analysis

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A hybrid deep learning-based approach for rolling bearing fault prognostics 22

A hybrid deep learning-based approach for rolling bearing fa...

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22nd IFAC World Congress

作者： Neto, Domício Petrella, Lorena Henriques, Jorge Gil, Paulo Cardoso, Alberto Coimbra Portugal CTS-UNINOVA Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal

ISBN: (纸本)9781713872344

Predictive Maintenance (PdM) has the potential to revolutionize the industry by providing advanced techniques to assess the condition of an industrial system and yield key information that can help optimize maintenance planning and prevent unexpected faults and breakdowns. Nevertheless, PdM is far from being universally applied and it is still the subject of increasing research. Thus, developing new approaches has great relevance to help PdM become a practical reality for the industry. PdM can also bring benefits in terms of sustainability, by reducing human and material resources waste, which is one of the main objectives of Circular Manufacturing initiatives. In this context, rolling bearings are one of the most studied components, as most industrial systems with rotating mechanisms contain bearings, which are prone to a number of faults caused by natural and unnatural wear. In this work, an hybrid Deep Learning (DL) approach is proposed, combining a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network to predict Remaining Useful Life (RUL) using rolling bearing vibration data preprocessed with the Short-Time Fourier Transform (STFT). This model was trained and validated using the PRONOSTIA public dataset, which is a popular benchmark for rolling bearing prognostics. The obtained results are satisfactory, providing RUL estimates close to the true values in most test cases, proving the competitiveness of the approach and its potential. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Circular Manufacturing Deep Learning Predictive Maintenance Remaining Useful Life Rolling Bearings

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A Comparative Study on Different Optimization Algorithms for Solving Economic Dispatch Problem 4

A Comparative Study on Different Optimization Algorithms for...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

ISBN: (纸本)9798350306262

Economic dispatch is one of the mathematical optimization problems in power system operation and planning. It aims to find the most efficient output for generating units that meets the demand of the load at the lowest possible cost while satisfy all operational constraints. This paper examines numerous methods to address the economic dispatch problem, including deterministic approaches like the Lagrange multiplier method, metaheuristic optimization algorithms such as the Genetic Algorithm, the Firefly Algorithm, the Harris-Hawks optimization algorithm, and their hybridizations. The study also utilizes PowerWorld Simulator, a software package that solves economic dispatch problems using sequential linear programming. Two different case studies have been conducted on IEEE 5-bus and 30-bus test systems for demonstrating the effectiveness of the proposed algorithms. The results of various case studies showed that the deterministic methods are the most effective for solving the economic dispatch problem. It was also shown that the hybrid algorithms, which combine the strengths of different optimization techniques, can achieve a significant enhancement in total cost compared to the conventional metaheuristic methods. © 2024 IEEE.

关键词： Genetic algorithms

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ESSL: Enhanced Spatio-Temporal Self-Selective Learning Framework for Unsupervised Video Anomaly Detection 26

ESSL: Enhanced Spatio-Temporal Self-Selective Learning Frame...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Qun Pan, Xubei Xiao, Fu Bhanu, Bir School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of California RiversideCA United States

ISBN: (纸本)9781643684369

Unsupervised Video Anomaly Detection (UVAD) utilizes completely unlabeled videos for training without any human intervention. Due to the existence of unlabeled abnormal videos in the training data, the performance of UVAD has a large gap compared with semi-supervised VAD, which only uses normal videos for training. To address the problem of insufficient ability of the existing UVAD methods to learn normality and reduce the negative impact of abnormal events, this paper proposes a novel Enhanced Spatio-temporal Self-selective Learning (ESSL) framework for UVAD. This framework is designed for capturing both the appearance and motion features through effective network structures by solving the spatial and temporal jigsaw puzzles. Specially, we develop a Self-selective Learning Module (SLM) for UVAD, which prevents the model learning abnormal features and enhances the model by selecting normal features. Experimental results on three benchmark datasets show that the proposed method not only surpasses the state-of-the-art UVAD works, but also achieves the performance comparable to the classic semi-supervised methods for video anomaly detection that needs normal videos selected manually. Code is available at: https://***/xusuger/ESSL. © 2023 The Authors.

关键词： Anomaly detection

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Carbon Quantum Dot Fluorescent Stickers for Biochip Authentication 33

Carbon Quantum Dot Fluorescent Stickers for Biochip Authenti...

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33rd IEEE Asian Test Symposium, ATS 2024

作者： Baban, Navajit Singh Abdelhameed, Mohammed Elbeh, Mahmoud Ramadi, Khalil Song, Yong-Ak Bhattacharjee, Sukanta Karri, Ramesh Chakrabarty, Krishnendu New York University Division of Engg. Abu Dhabi United Arab Emirates Indian Institute of Technology Department of Computer Science and Engineering Guwahati India New York University Department of Electrical and Computer Engineering New York United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ United States

ISBN: (纸本)9798331529161

Microfluidic biochips are widely used in biomedical research, clinical diagnostics, and point-of-care testing. However, their complex supply chains make them vulnerable to counterfeiting, overbuilding, and intellectual property (IP) piracy. We present fluorescent carbon quantum dot (CQD) stickers1 that can be integrated with the polydimethylsiloxane (PDMS) based biochips for authentication. The stickers can be plasma-bonded to biochips made of glass and silicon. A protective spin-coated PDMS layer makes them obscured and tamperproof. However, they are detectable under UV light and can be authenticated via spectral analysis. The scheme exhibits unique excitation-dependent responses associated with the variability of the CQD sizes. This makes it ideal for physical authentication. Reliability studies concerning mechanical, photonic, and thermal degradation have demonstrated highly stable results. The stability of CQDs within the PDMS, their robust excitation-based emission fluorescence response, and the use of waste polypropylene masks make this a sustainable and robust authenticator for biochips. © 2024 IEEE.

关键词： Polydimethylsiloxane

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