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共 1643 条记录，以下是341-350 订阅

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Prediction of Sulfur Hexafluoride (SF6) Gas Pressure Time Series Data Based on Adaptive Particle Swarm optimization- Long Short Term Memory (APSO-LSTM) 23

Prediction of Sulfur Hexafluoride (SF6) Gas Pressure Time Se...

引用

3rd International conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Zhu, Baochang Zhang, Yang Yin, Jun Shi, Junjie Li, Li State Grid Tianjin Electric Power Corporation Tianjin300310 China State Grid Corporation of China Beijing100031 China

ISBN: (纸本)9798400700200

Sulfur hexafluoride (SF6), an insulating gas widely used in high voltage electrical equipment, has a strong greenhouse effect. In recent years, with the rapid development of China's power transmission, transformer and distribution network, SF6 usage and emissions continue to increase. Through the extraction of satellite test data, it is found that the atmospheric content of SF6 in China is increasing year by year. In order to achieve the goal of "carbon peaking and carbon neutrality", it is of great significance to control SF6 use and emission in the power industry. In order to improve the accuracy of SF6 gas pressure prediction in complex power grid environment, a prediction method of SF6 gas pressure based on APSO-LSTM neural network was proposed. The simulation experiment uses the measured data of SF6 gas pressure for 3 months to train and test the APSO-LSTM model. The results show that the model reduces the prediction error of SF6 gas pressure effectively. Therefore, this paper has theoretical implementation and practical reference value for the research and strategy formulation of emission reduction measures in the power industry. © 2023 ACM.

关键词： Particle swarm optimization (PSO)

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Optimal Energy Efficiency Techniques and Security Enhancement in Wireless Sensor network Using Machine Learning 3

Optimal Energy Efficiency Techniques and Security Enhancemen...

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3rd International conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Manikandan, S. Suganthi, S. Gayathiri, R. Anna University Chennai Kongunadu Polytechnic College Tamilnadu Trichy India K Ramakrishnan College of Technology Electronics and Communication Tamilnadu Trichy India Sri Sairam Engineering College Electronics and Instrumentation Tamilnadu Chennai India

ISBN: (纸本)9781665462754

Speech recognition, natural language processing, and image processing are just a few of the many applications that machine learning (ML) has been employed in. The use of ML in wireless sensor networks (WSNs) has recently captured the attention of many researchers, broadening the field of study. Load balance and resource utilization are the problem with WSNs. Numerous routing algorithms can resolve these problems by using less energy and extending the network lifetime in WSNs through optimum routing techniques. Additionally, there are frequently issues like rigidity, taking into account various factors, and relying on models. Machine learning has demonstrated its effectiveness in creating effective procedures to deal with difficult issues in a variety of network features. In a wireless sensor network, routing is the process of determining the best path for data transmission between various sensor nodes in accordance with the situation. A lot of machine learning has been applied to creating energy-efficient routing methods. The key contribution of this study is to give a concise overview of clustering in wireless sensor networks based on three major categories, including classical, optimization, and machine learning techniques. © 2022 IEEE.

关键词： Machine learning

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LoRa-Based IoT Architecture Using Ant Colony optimization for Intelligent Traffic System 3rd

LoRa-Based IoT Architecture Using Ant Colony Optimization fo...

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3rd International conference on Machine Learning, Image Processing, network Security and Data Sciences, MIND 2021

作者： Simaiya, Sarita Lilhore, Umesh Kumar Sandhu, Jasminder Kaur Snehi, Jyoti Garg, Atul Manhar, Advin Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of CSE KIET Group of Institutions Delhi-NCR Ghaziabad India Institute of Engineering Chandigarh University Mohali India JECRC University Jaipur India

ISBN: (纸本)9789811958670

Worldwide, approaches have been created to address urban transportation concerns, including the rising prevalence of traffic crashes, congestion, and poor traffic management. Intelligent traffic systems (ITSs) solutions for metropolitan subways are one solution toward such problems. Long-range wide area network (LoRa) is a technological evolution utilized with LP WAN. This paper presents an IoT-based modified LoRa architecture and machine learning technique implemented toward an intelligent traffic network. The existing LoRa protocol has been modified by adding a cross-layer smart and connected communication among interconnected nodes. It improves the monitoring of the nodes and helps take accurate traffic decisions. The technology uses an ant colony optimization-based machine learning algorithm to respond in real-time based on routes’ traffic volume. This research mainly focused on examining the use of LoRa technological advances in traffic control, vehicle tracking. It also examines the platform’s best LoRa network implementation criteria. It develops a LoRa cross-layer architecture that overcomes the shortcomings of the traditional LoRa network by making it easier to do intelligent assessment and flexible network and communication service providers. The proposed modified LoRa architecture accomplishes genuinely outstanding in terms of effectiveness, accuracy, and F-measure as compared to standard LoRa. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Security Risk Management System for the Construction and Operation of Smart Port Area Based on BP Neural network Algorithm 3

Security Risk Management System for the Construction and Ope...

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3rd International conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Cui, Di Sun, Guoqing Zhan, Xiaotiao Safety and Environmental Protection Division China Waterborne Transportation Research Institute Beijing China

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority! © 2023 Elsevier B.V.. All rights reserved.

关键词： Data mining

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A k-means based multi-AUV hydroacoustic sensor network data acquisition algorithm 23

A k-means based multi-AUV hydroacoustic sensor network data ...

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3rd International conference on Robotics and control Engineering, RobCE 2023

作者： Song, Haoxuan Chen, Mingzhi Merchant Marine College Shanghai Maritime University Shanghai201306 China College of Mechanical Engineering University of Shanghai for Science and Technology Shanghai201306 China

ISBN: (纸本)9781450398107

With the development of science and technology, people have made new progress in the exploration of the ocean. In order to study the ocean in greater depth, implementation monitoring of the ocean is required. Due to the complex underwater communication environment, underwater information data collection is more difficult compared to land, and autonomous underwater vehicles (AUVs) are often needed to assist in the collection. How to plan the cruise path of AUVs is a major problem in data collection. To address this problem, a hybrid optimization algorithm based on k-means clustering algorithm is proposed in this paper, which can plan multiple AUVs for data collection. After simulation experiments, the effectiveness of the algorithm is determined. Compared with the SOM-based (Self-Organizing Map) algorithm, the length of the planned path and the algorithm response time of this algorithm are better than the SOM-based algorithm, which can save energy for AUVs and extend the life of sensors. © 2023 ACM.

关键词： Autonomous underwater vehicles

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Reinforcement Learning for Sustainability: Adapting in large-scale heterogeneous dynamic environments 3

Reinforcement Learning for Sustainability: Adapting in large...

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3rd IEEE International conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Dusparic, Ivana Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland

ISBN: (数字)9781665451420

ISBN: (纸本)9781665451420

Reinforcement learning (RL) has seen major breakthroughs in the recent years, most notably outperforming humans Atari, Go, and StarCraft games. RL use is also being investigated in a range of practical applications, from medical diagnostic to news personalization, and in particular in self-adaptive systems [1]. In this talk, we focus on applications of RL for optimization of resource use in city infrastructures [2], for example, urban traffic control [3], smart grid [4], and communication networks [5], [6], aimed at improving their sustainability and addressing the ever-increasing demand on urban resources. We first analyze properties that make these systems ameanable to RL-based optimization, and then present a range of techniques we developed for enabling the use of RL for optimization in such environments, namely, multi-agent multi-objective optimization, adaptation in non-stationary environments, online transfer learning, state space adaptation, and RL explainability. We close by discussing further challenges in enabling RL deployment in self-adaptive large scale infrastructures, including development of new algorithms that can ensure seamless lifelong adaptivity, including adapting to previously unseen situations.

关键词： Heuristic algorithms Urban areas Transfer learning Reinforcement learning Games Traffic control Smart grids

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3rd EAI International conference on Edge Computing and IoT, EAI ICECI 2022

3rd EAI International Conference on Edge Computing and IoT, ...

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3rd EAI International conference on Edge Computing and IoT, EAI ICECI 2022

ISBN: (纸本)9783031289897

The proceedings contain 21 papers. The special focus in this conference is on Edge Computing and IoT. The topics include: Evaluation of Higher Education System;defense Mechanisms Against Audio Adversarial Attacks: Recent Advances and Future Directions;an Empirical Study of Worldwide Plastic Waste Mitigation;prediction for Surface Subsidence of Shield Construction in Water-Rich Sand Egg Stratum Based on Edge Intelligence;highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence;adversarial Example Attacks in Internet of Things (IoT);training Node Screening in Decentralized Trusted Federated Learning;exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing;federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices;the Data Exchange Protocol over Multi-chain Blockchain Using Zero-Knowledge Proof;a Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence;LSTM-DAM: Malicious network Traffic Prediction for Cloud Manufacturing System;a Hybrid Task Offloading and Service Cache Scheme for Vehicular Edge Computing;on Enhancing Transmission Performance for IoV Based on Improved Greedy Algorithm;Precise Segmentation on Poly-Yolo, YoloV5 and FCN;Research on DAG Based Consensus Mechanism for Adjustable Load Metering Data;a Detection and Information Processing Model Based on an Automatic Mechanism for Tax Payment control in Developing Countries;IEC-FOF: An Industrial Electricity Consumption Forecasting and optimization Framework;demons Hidden in the Light: Unrestricted Adversarial Illumination Attacks.

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Research on intelligent agricultural decision-making system based on Bayesian optimization 3

Research on intelligent agricultural decision-making system ...

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3rd International conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Yan, Xiaoying Software Engineering Institute of Guangzhou Guangdong Guangzhou510300 China

ISBN: (纸本)9781510663473

Along with social development, the problems of insufficient rural labor and mismatch between labor intensity and economic returns have become a greater obstacle to rural development;therefore, the development of agricultural intelligence to improve agricultural productivity will become the new direction of modern agricultural development. Based on Internet technology, intelligent agriculture adopts digital technologies such as intelligent perception, network transmission and big data processing to provide decision basis for agricultural planting, production and pest control, or directly deploy decision information to automated farm equipment, so as to use agricultural resources reasonably and efficiently. However, the development of smart agriculture is constrained by the low accuracy of automation control and decision information. In this paper, we optimize the threshold value of each agricultural production decision data through machine learning algorithm, use Bayesian optimization to learn the agricultural production environment and crop growth data, iteratively optimize the threshold value to get the global best value, improve the accuracy of automation control and reduce the risk of agricultural production decision, and effectively improve agricultural The economic returns of agriculture can be effectively improved. © 2023 SPIE.

关键词： Machine learning

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Performance Study of a Permanent-Magnet Eddy Current Damper for Guns Recoil control 3

Performance Study of a Permanent-Magnet Eddy Current Damper ...

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3rd International conference on Defence Technology, ICDT 2022

作者： Liu, Ning Wang, Nan-Nan Shen, Yan-Ping Wang, Biao-Biao School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China CETC les Information System Group Co. Ltd The 28th Research Institute of CETC Nanjing210007 China

Eddy current damper (ECD) is a promising device for dissipating kinetic energy in dynamic systems, this study is conducted to analyze the dynamic characterization and structural optimization of linear ECD under guns launch load. The recoil resistance of ECD under guns launch load is highly nonlinear at high recoil velocity, and the recoil resistance curve shows an increase-decrease-increasing shape like a "saddle", which is not conducive to the stability of ECD during operation. The effects of several structural parameters on the dynamic characterization of ECD were studied in details. Results show that the air-gap size, the thickness of the magnetic conduction tube and conductive tube all have a notable influence on the recoil resistance curve of ECD. Moreover, BP neural network and genetic algorithm were used for structural optimization and the optimal value was verified by FEM. The optimization results indicate that the "saddle"shape of optimized recoil resistance curve is basically invisible. The fullness of the recoil resistance curve is significantly improved from 84.93% to 92.1%, which guaranteeing a smooth recoil movement. Finally, an impact loading test system for eddy current damper was built and the experimental results were basically consistent with the simulation results. © Published under licence by IOP Publishing Ltd.

关键词： Structural optimization

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Research on Fault Recovery Method for Distribution networks with Microgrids Based on Improved BPSO Algorithm 1

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3rd International Joint conference on Energy, Electrical and Power Engineering, CoEEPE 2023

作者： Liang, Yuan Hou, Zufeng Zhao, Ruifeng Pan, Kaiyan Chen, Jiandian Liu, Haixin Zhuhai Power Supply Bureau Guangdong Power Co. Ltd. Zhuhai519000 China Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou510000 China Dongfang Electronics Cooperation Yantai264010 China

ISBN: (数字)9789819739400

ISBN: (纸本)9789819739394

Due to the integration of microgrids in distribution networks, more safety factors need to be considered for fault recovery. Currently, fault recovery methods have problems such as high computational complexity and long fault recovery time. A fault recovery model for distribution networks with microgrids was established with the objective functions of minimum network loss, minimum number of switch actions, and minimum loss of electricity. The improved binary particle swarm optimization algorithm and genetic algorithm were combined to solve the model. The feasibility of this fault recovery method was verified by analyzing various fault recovery scenarios. Compared to traditional restoration methods, the proposed method has fast convergence speed, strong adaptability, and good restoration effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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