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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Øystein Rognes Solheim Ingeborg Ligaarden Boye Annfelt Høverstad Thomas Trøtscher Markus Andersen Magnus Korpås Dept. of Data Science Statnett SF Oslo Norway Dept. of Electric Power Engineering NTNU Oslo Norway Dept. of Data Science Statnett SF Trondheim Norway Dept. of Computer Science NTNU Trondheim Norway Dept. of Electric Power Engineering NTNU Trondheim Norway

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

This paper presents a method for forecasting the probability of weather-related transmission line failures. The method combines failure statistics, a Bayesian updating scheme and reanalysis weather data to produce fragility curves that model the relationship between weather exposure for a component and the probability of failure. The fragility curves are combined with medium range weather forecasts extracted at each span of the individual transmission lines to obtain a forecast of the hourly probability of failure for each line. A tool that implements the method has been developed and is currently being tested operationally within the Norwegian TSO Statnett. We show the data requirements and demonstrate the use of a dashboard that expose the forecasts to an end-user.

关键词： Power transmission lines Weather forecasting Probabilistic logic data models Power system reliability Bayes methods Reliability

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Momentum-Based Distributed Economic Dispatch of Smart Grids in Time-Varying Directed Networks

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IEEE Transactions on Network science and engineering 2025年

作者： Zhang, Keke Lu, Qingguo Zhou, Qixing Li, Huaqing Xia, Dawen Huang, Tingwen Chongqing University College of Computer Science China Southwest University College of Electronic and Information Engineering China Kaili University College of Microelectronics and Artificial Intelligence College of Big Data Engineering China Texas A&M University at Qatar Science Program Doha23874 Qatar

In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple generators, with the aim of fulfilling load demands at the lowest possible total generation cost, while strictly conforming to the constraints imposed by the local generation capacity. For a faster resolution of the EDP, we propose an efficient distributed accelerated economic dispatch algorithm that incorporates a unified momentum acceleration strategy into the push-pull deviation tracking approach. The involved acceleration strategy under specific momentum parameters includes two well-known acceleration strategies, i.e., the heavy-ball and Nesterov acceleration strategies, which is more flexible and provides additional improvements in convergence. We present rigorously theoretical proof of linear convergence to the optimal dispatch with explicit bounds for step size and momentum parameters. Finally, to verify the effectiveness of our algorithm and the correctness of the theoretical analysis, we conduct simulations of diverse EDP studies in smart grids. © 2013 IEEE.

关键词： Smart power grids

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Hate speech detection in twitter using natural language processing 3

Hate speech detection in twitter using natural language proc...

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pariyani, Bhavesh Shah, Krish Shah, Meet Vyas, Tarjni Degadwala, Sheshang Nirma University Data Science Ahmedabad Gujarat380060 India Sigma Institute of Engineering Department Computer Engineering

ISBN: (纸本)9780738111834

Twitter's central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter's job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate Speech can hurt a person or a community. So, it is not appropriate to use hate speech. Now, due to increase in social media usage, hate speech is very commonly used on these platforms. So, it is not possible to identify hate speeches manually. So, it is essnetial to develop an automated hate speech detection model and this resaech work shows different approaches of Natural Language Processing for classification of Hate Speech through Machine Learning Algorithms. © 2021 IEEE.

关键词： Decision trees

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Earthquake Prediction Through Machine Learning Approach

Earthquake Prediction Through Machine Learning Approach

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Pratik Shrote Priya Dasarwar Snehlata Dongre Prashant Khobragade Intern Center of Excellence Data Science GH Raisoni College of Engineering Nagpur India CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science and Engineering GH Raisoni College of Engineering Nagpur India Dept. of Computer Science and Engineering H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness.

关键词： Seismic measurements Prevention and mitigation Earthquake engineering Disasters Earthquakes Machine learning Real-time systems Hazards Stakeholders Resilience

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SoK: On Gradient Leakage in Federated Learning

arXiv

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arXiv 2024年

作者： Du, Jiacheng Hu, Jiahui Wang, Zhibo Sun, Peng Gong, Neil Zhenqiang Ren, Kui Chen, Chun The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China College of Computer Science and Electronic Engineering Hunan University China Department of Electrical and Computer Engineering Duke University United States

Federated learning (FL) facilitates collaborative model training among multiple clients without raw data exposure. However, recent studies have shown that clients’ private training data can be reconstructed from shared gradients in FL, a vulnerability known as gradient inversion attacks (GIAs). While GIAs have demonstrated effectiveness under ideal settings and auxiliary assumptions, their actual efficacy against practical FL systems remains under-explored. To address this gap, we conduct a comprehensive study on GIAs in this work. We start with a survey of GIAs that establishes a timeline to trace their evolution and develops a systematization to uncover their inherent threats. By rethinking GIA in practical FL systems, three fundamental aspects influencing GIA’s effectiveness are identified: training setup, model, and post-processing. Guided by these aspects, we perform extensive theoretical and empirical evaluations of SOTA GIAs across diverse settings. Our findings highlight that GIA is notably constrained, fragile, and easily defensible. Specifically, GIAs exhibit inherent limitations against practical local training settings. Additionally, their effectiveness is highly sensitive to the trained model, and even simple post-processing techniques applied to gradients can serve as effective defenses. Our work provides crucial insights into the limited threats of GIAs in practical FL systems. By rectifying prior misconceptions, we hope to inspire more accurate and realistic investigations on this topic. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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High Quality Global Routing Algorithm Based on Multi-Stage Optimization

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2024年第4期36卷 607-614页

作者： Genggeng, Liu Zhenyu, Pei Ning, Xu College of Computer and Data Science Fuzhou University Fuzhou350116 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

Global routing is a crucial step in VLSI physical design. To address the problems that the low utilization rate of capacity and easy to fall into local optimum in the existing global routing algorithms, a high-quality global routing algorithm based on multi-stage optimization is proposed. First, the virtual capacity dynamic adjustment strategy is introduced in the pre-connection stage and the routing reorganization stage to improve the utilization rate of capacity. Then, in the routing reorganization stage, the routing subarea adaptive expansion strategy is proposed to narrow the search space and improve the routing efficiency. Finally, the A* algorithm heuristic search is combined with the global search capability of the particle swarm algorithm to avoid the algorithm to choose the shortest path in the routing process, and thus avoid the algorithm to fall into the local optimum. Experimental results comparing with existing global routing algorithms on ISPD benchmark circuits show that the routing results of proposed algorithm reduce the wirelength by 6.0% to 22.8%. © 2024 Institute of Computing Technology. All rights reserved.

关键词： Routing algorithms

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Predicting Customer Purchase Intent Using Logistic Regression: A data-Driven Approach

Predicting Customer Purchase Intent Using Logistic Regressio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： C. Rajarajachozhan G. Karthika A. Rajakrishnammal M. Mano P. Nithish Kumar E. Jayahari Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu Dept. of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu Dept. of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development.

关键词： Logistic regression Adaptation models Accuracy Pipelines Companies Predictive models Real-time systems data models Predictive analytics Business

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Jagjit Singh Dhatterwal Kiran Malik Purushottam Das Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering (AIML) GL Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field.

关键词： Knowledge acquisition Knowledge based systems System integration Cognition Robustness Maintenance Medical diagnosis Informatics Medical diagnostic imaging Viruses (medical)

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Adaptive prescribed performance based on recursive nonsingular terminal sliding mode control for quad-rotor systems under uncertainty and disturbance: Real-time validation

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Aerospace science and Technology 2024年 147卷

作者： Han, Gao Mofid, Omid Mobayen, Saleh Khooban, Mohammad Hassan School of Aerospace Science and Technology Xidian University Xi'an710071 China Department of Electrical Engineering University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method. © 2024 Elsevier Masson SAS

关键词： Unmanned aerial vehicles (UAV)

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The microscale organization of directed hypergraphs

arXiv

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arXiv 2024年

作者： Lotito, Quintino Francesco Vendramini, Alberto Montresor, Alberto Battiston, Federico Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Trento38123 Italy Department of Network and Data Science Central European University Vienna1100 Austria

Many real-world complex systems are characterized by non-pairwise – higher-order – interactions among system’s units, and can be effectively modeled as hypergraphs. Directed hypergraphs distinguish between source and target sets within each hyperedge, and allow to account for the directional flow of information between nodes. Here, we provide a framework to characterize the structural organization of directed higher-order networks at their microscale. First, we extract the fingerprint of a directed hypergraph, capturing the frequency of hyperedges with a certain source and target sizes, and use this information to compute differences in higher-order connectivity patterns among real-world systems. Then, we formulate reciprocity in hypergraphs, including exact, strong, and weak definitions, to measure to which extent hyperedges are reciprocated. Finally, we extend motif analysis to identify recurring interaction patterns and extract the building blocks of directed hypergraphs. We validate our framework on empirical datasets, including Bitcoin transactions, metabolic networks, and citation data, revealing structural principles behind the organization of real-world systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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