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Hadoop Processing Methods for Large Scale Video Data

IEEE ACCESS 2024年 12卷 176247-176258页

作者： Wu, Guangzhi Guangzhou Univ Party Propaganda Dept Guangzhou 510000 Peoples R China

As the continuous advancement of the We-media era, there are more and more short video users. However, there are still problems with low data processing efficiency and serious video damage in the current processing framework for large-scale video data. To address this issue, research is conducted to improve the artificial fish swarm algorithm using particle swarm optimization algorithm, and the improved algorithm is applied to the distributed system architecture Hadoop to enhance Hadoop's computation speed and accuracy of data. The study first conducts comparative experiments on the improved algorithm. The outcomes denoted that the accuracy of the improved algorithm was as high as 98.7%, while the error rate was only 0.9%. The recall and precision of video data were 92.1% and 96.5%, respectively, and the computational performance was significantly better than other comparative algorithms. The Hadoop data processing framework optimized after the improved algorithm was further tested, and the outcomes denoted that the response accuracy of the optimized data processing framework was as high as 98.6%, and the response time was only 0.32 seconds. From the above outcomes, the proposed improved algorithm can optimize the processing capability of the data processing framework for large-scale video data. Moreover, the optimized data processing framework can not only be used for large-scale video data processing, but also widely used in other fields to raise computational accuracy.

关键词： Fish Data processing Mathematical models Distributed databases Accuracy Clustering algorithms particle swarm optimization Tail Linear programming Flowcharts Video data particle swarm optimization algorithm artificial fish swarm algorithm Hadoop framework data processing

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Collaborative planning of electric vehicle integrated charging and swapping stations and distribution network for carbon emission reduction

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ENERGY REPORTS 2024年 12卷 5846-5862页

作者： Liu, Wenjin Xin, Shijin Zhang, Zhaoxu Fan, Changhui Hao, Guojie Xu, Qing State Grid Gansu Elect Power Co Baiyin Power Supply Co Baiyin 730900 Peoples R China

Electric vehicle (EV) integration into the distribution network (DN) is a significant measure for reducing carbon dioxide emissions, yet existing studies have not fully explored the carbon reduction potential of the EV. This article proposes an innovative collaborative planning framework for electric vehicle integrated charging and swapping stations (EVICSS) and DN for carbon emission reduction. Initially, the division of responsibilities and mechanism analysis for emission reduction in DN are presented. Subsequently, the ladder carbon price model is presented to incentivize carbon mitigation efforts. And, considering the architecture and functioning mode of the EVICSS, the operational model for EVICSS equipment is established to deliver high-quality services to EV users, taking into account fast charging stations (FCS), fast battery swapping stations (FBSS), and cascade energy storage system (CESS) comprehensively. Taking into account the economic implications of carbon trading, a collaborative planning strategy for EVICSS and the DN is formulated to minimize overall costs while maximizing carbon emission reductions. Then an modified integrated gravitational search algorithm and particle swarm optimization algorithm (MIGSA-PSOA) is customizedly developed to solve this complicated optimization problem. Finally, case studies based a real DN are conducted to illustrate the effectiveness of the collaborative planning model. The results demonstrated it can effectively respond to DN power regulation and realize carbon emission reduction. Compared with independent planning, collaborative planning can increase the planning cost and total cost to 15.6% and 33.1% respectively.

关键词： Electric vehicle Collaborative planning Electric vehicle integrated charging and swapping stations Low carbon Gravitational search algorithm particle swarm optimization algorithm

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Distributed state estimation-based resilient controller design for IoT-enabled microgrids under deception attacks

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APPLIED ENERGY 2024年 374卷

作者： Ponnarasi, L. Pankajavalli, P. B. Lim, Yongdo Sakthivel, R. Bharathiar Univ Dept Comp Sci Coimbatore 641046 India Sungkyunkwan Univ Dept Math Suwon 440746 South Korea Bharathiar Univ Dept Appl Math Coimbatore 641046 India

This paper addresses the distributed state estimation-based centralized control design problem for Internet- of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks.

关键词： Microgrid Resilient state estimation Deception attacks particle swarm optimization algorithm

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Short-term wind speed forecasting based on a hybrid model that integrates PSO-LSSVM and XGBoost

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1138-1143页

作者： Shi, Yanhua Zhengzhou Univ Technol Sch Informat Engn 18 Yingcai St Zhengzhou 450053 Henan Peoples R China

A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model's remarkable capability to enhance prediction performance and accuracy.

关键词： wind speed prediction least squares support vector machine particle swarm optimization algorithm deep learning combination model

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A novel design model of flow channel paths for additive manufacturing

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RAPID PROTOTYPING JOURNAL 2024年第6期30卷 1230-1248页

作者： Li, Dongfei Wang, Hongtao Dai, Ning Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China

PurposeThis paper aims to propose a method for automatic design of additive manufacturing (AM) flow channel paths driven by path length and pressure loss. The research focuses on the automatic design of channel paths, intending to achieve the shortest flow channel length or minimum pressure loss and improve the design efficiency of AM ***/methodology/approachThe initial layout of the flow channels is redesigned to consider the channels print supports. Boundary conditions and constraints are defined according to the redesigned channels layout, and the equation consisting of channel length and pressure loss is used as the objective function. Then the path planning simulation is performed based on particle swarm algorithm. The proposed method describes the path of flow channels using spline cures. The spline curve is controlled by particle (one particle represents a path), and the particle is randomly generated within the design space. After the path planning simulation is completed, the generated paths are used to create 3D *** study 1 demonstrates the automatic design of hydraulic spool valve. Compared to conventional spool valve, the pressure loss was reduced by 86% and the mass was reduced by 83%. The design results of case study 2 indicate that this approach is able to find the shortest channel path with lower computational ***/valueThe automatic design method of flow channel paths driven by path length and pressure loss presented in this paper provides a novel solution for the creation of AM flow components.

关键词： Additive manufacturing Flow channel path Automatic design Pressure loss particle swarm optimization algorithm Hydraulic valve

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Structure Deterioration Identification and Model Updating for Prestressed Concrete Bridges Based on Massive Point Cloud Data

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APPLIED SCIENCES-BASEL 2024年第21期14卷 10007页

作者： Sun, Zhe Zhao, Sihan Liang, Bin Liu, Zhansheng Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China Beijing Univ Technol Chongqing Res Inst Chongqing 401121 Peoples R China Natl Key Lab Bridge Safety & Resilience Beijing 100124 Peoples R China

As a critical component of the transportation system, the safety of bridges is directly related to public safety and the smooth flow of traffic. This study addresses the aforementioned issues by focusing on the identification of bridge structure deterioration and the updating of finite element models, proposing a systematic research framework. First, this study presents a preprocessing method for bridge point cloud data and determines the parameter ranges for key algorithms through parameter tuning. Subsequently, based on the massive point cloud data, this research explores and optimizes the methods for identifying bridge cracks and spatial deformations, significantly enhancing the accuracy and efficiency of identification. On this basis, the particle swarm optimization algorithm is employed to optimize the key parameters in crack detection, ensuring the reliability and precision of the algorithm. Additionally, the study summarizes the methods for detecting bridge structural deformations based on point cloud data and establishes a framework for updating the bridge model. Finally, by integrating the results of bridge crack and deformation detection and combining Bayesian model correction and adaptive nested sampling methods, this research sets up the process for updating finite element model parameters and applies it to the analysis of actual bridge point cloud data.

关键词： point cloud model crack detection particle swarm optimization algorithm spatial deformation detection bridge finite element model updating

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A motor imagery EEG signal optimized processing algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2024年 101卷 38-51页

作者： Geng, Xiaozhong Zhang, Xi Yue, Mengzhe Hu, Weixin Wang, Linen Zhang, Xintong Yu, Ping Long, Duo Yan, Hui Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China Jilin Inst Chem Technol Sch Informat & Control Engn Jilin 132022 Peoples R China Jilin Univ Finance & Econ Sch Accounting Changchun 130117 Peoples R China Suqian Univ Sch Management Suqian 223800 Peoples R China

Feature extraction and classification is a difficult area in motor imagery electroencephalogram (EEG) signal processing. In order to improve the classification accuracy of EEG signals, both a feature extraction method based on the combination of LMD-CSP and a classification algorithm based on the fusion of PSO-SVM are proposed. Firstly, the extended informax ICA algorithm is used to denoise the signal and reduce the influence of noise on the signal. Then, the pre-processed EEG signals are decomposed into multiple Product Function (PF) components by Local Mean Decomposition (LMD), and the most discriminative PF component is selected. Next, feature extraction is carried out from the selected PF components using the Common Space Pattern (CSP). Finally, the obtained features are input into a Support Vector Machine (SVM) classifier improved by particle swarm optimization (PSO) for classification recognition. The experimental results show that compared with the traditional CSP-SVM method, CapsNet method, WPT-CSP+CNN method, FDCSP-SVM method, and EMD-CNN method, the classification accuracy of the proposed method is increased by 26.94 %, 14.9 %, 13.34 %, 8.34 % and 4.04 %, respectively. This further proves the superiority of the proposed method for EEG signal processing technology.

关键词： Local mean decomposition Common spatial pattern particle swarm optimization algorithm Support vector machine

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Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第8期75卷 1493-1509页

作者： Saemi, Saeed Komijan, Alireza Rashidi Tavakkoli-Moghaddam, Reza Islamic Azad Univ Dept Ind Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Firoozkooh Branch Firoozkooh Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic algorithm (GA), Firefly algorithm (FA), and particle swarm optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes.

关键词： COVID-19 conditions scheduling integrated crew pairing and rostering problem meta-heuristics genetic algorithm particle swarm optimization algorithm

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Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation

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ENERGY 2024年 312卷

作者： Zhou, Yifei Wang, Shunli Feng, Renjun Xie, Yanxin Fernandez, Carlos Southwest Univ Sci & Technol Engn & Technol Ctr Sch Informat Engn Mianyang Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks by introducing dropout mechanisms. At the same time, we also optimize the traditional multi-layer perceptron model to ResMLP, which is improved by introducing residual linkage, and then integrate the two optimization models. Finally, the synergistic effect and attention mechanism of genetic algorithm and particle swarm optimization are used to optimize its parameters. We then rigorously tested the model on nine datasets, including HPPC, DST and BBDST, at different temperatures of 5 degrees C, 15 degrees C and 35 degrees C. Using MAE, RMSE and MAXE benchmarks, our research results show that the proposed hybrid model outperforms the benchmark algorithm, achieving significantly enhanced performance and higher accuracy, and the maximum SOC estimation error is kept below 4.53 %. In addition, experimental evaluation at different temperatures shows the robustness and adaptability of the proposed algorithm.

关键词： State of charge Lithium-ion battery BiLSTM Multiple perceptrons particle swarm optimization algorithm Genetic algorithm

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Optimal Scheduling of Island Microgrids with Seawater Pumped Storage Plants for Multi-Energy Complementarity

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第2期20卷 185-196页

作者： Su, Shiwei Chai, Huarui Lin, Jianbo Yi, Chengming Li, Xin China Three Gorges Univ Hubei Prov Engn Res Ctr Intelligent Energy Technol Yichang 443002 Hubei Peoples R China Three Gorges Univ Sch Elect Engn & Renewable Energy Yichang 443002 Hubei Peoples R China

The rapid development of new energy sources, such as offshore wind power and photovoltaic power, has provided a new solution to the problem of power supply for islands far from the mainland. Wave energy is a kind of renewable energy originated from the ocean, but the existing island power supply programs seldom consider this favorable natural condition. In addition, seawater variable-speed pumped storage is a new idea to consume offshore wind power and improve the reliability of coastal and island power systems. In view of the stochastic and intermittent nature of new energy sources, this paper adopts seawater variable-speed pumped storage power plants as energy storage equipment, and put forward an island power supply scheme with wind power, photovoltaic power generation, wave power generation, pumped storage power plants and diesel generator sets as a multifunctional complementary isolated grid. Firstly, wave energy generators, wind farms, photovoltaic farms, pumped storage power stations and diesel generator sets are modeled separately. Then, considering their respective operating conditions, constraints and load requirements, the optimal scheduling of island microgrids with multi-energy complementarity is constructed. Finally, based on the improved particle swarm optimization algorithm, the model is solved. According to the wind power photovoltaic and wave power output curves of several typical scenarios in an island far away from the mainland, the cost and benefit of different schemes are compared to verify the effectiveness of the optimal scheduling in this paper. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： seawater pumped storage plant wave energy generation optimal scheduling particle swarm optimization algorithm

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