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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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Design and Control of a Novel Hydraulic-Driven Humanoid Hand

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2024年第3期21卷

作者： Tao, Zhenguo Li, Xu Feng, Haibo Fu, Yili Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Zhejiang Lab Intelligent Robot Res Ctr Hangzhou 311100 Zhejiang Peoples R China

The robots with humanoid hands can take the place of humans in carrying objects and using tools efficiently in different situations. This paper presents a novel hydraulic-driven robotic hand named the WLRH-II (the second-generation wheel-legged robot humanoid hand) which bears heavy load and has strong robustness. This paper focuses on the design of humanoid hand driving and transmission mechanisms, kinematic and static analyses. First, the linkage mechanism is proposed according to the particle swarm optimization algorithm. Then, the kinematics and the statics of WLRH-II are analyzed in grasp state. In addition, the hydraulic servo control method is used to control the position of a hand through angle feedback. Finally, position tracking and load experiments are completed to verify the reasonableness and effectiveness of the control. The robotic hand can grasp objects of 30kg with 2.85-kg self-weight. The WLRH-II has a high load/self-weight ratio of 10.55 in this paper, which is rare in similar humanoid robotic hands.

关键词： Hydraulic-driven humanoid hand mechanism design particle swarm optimization algorithm high load/self-weight ratio

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Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

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ENERGY 2023年第PartA期262卷

作者： Du, Xiaoyi Wu, Dongdong Yan, Yabo Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

In this paper, GM(1,N-r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm;(2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables;and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N-r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

关键词： Electricity consumptions forecasting Grey forecasting model GM(1,N-r) model Grey incidence analysis particle swarm optimization algorithm

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A New Two-Stage Approach for a Bi-Objective Facility Layout Problem Considering Input/Output Points Under Fuzzy Environment

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IEEE ACCESS 2019年 7卷 134083-134103页

作者： Mohamadi, Arash Ebrahimnejad, Sadoullah Soltani, Roya Khalilzadeh, Mohammad Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran *** Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj *** Iran KHATAM Univ Dept Ind Engn Tehran *** Iran

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

关键词： Facility layout problem different preference representation structures goal programming genetic algorithm particle swarm optimization algorithm fuzzy sets

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A flexible orchestration of lightweight AI for edge computing in low-voltage distribution network

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Fan, Yuanliang Wu, Han Li, Zewen Lin, Jianli Li, Lingfei Huang, Xinghua Chen, Weiming Chen, Beibei Fujian Elect Power Co Ltd Elect Power Res Inst Fuzhou Peoples R China

Recent years, the tremendous number of distributed energy resources, electric vehicles are integrated into the Low-Voltage Distribution Network (LVDN), large amount of data are generated by edge devices in LVDN. The cloud data centers are unable to process these data timely and accurately, making it impossible to meet the demand for fine-grained control of LVDN. To solve the above problems, this paper proposes a flexible orchestration of lightweight artificial intelligence (AI) for edge computing in LVDN. Firstly, the application requirements of LVDN are analysed through feature extraction of its historical data, and a lightweight AI library is constructed to meet its requirements. Secondly, based on the multi-factor priority, a flexible orchestration model is established, to allow the lightweight AI embedded in the edge devices of the LVDN. Finally, the particle swarm optimization algorithm is used to provide the best solution. The simulation results show that the method proposed in this paper can support the deployment of AI at the edge. It can significantly improve the utilization of edge computing resources, and reduce the pressure of cloud computing and the time of application processes.

关键词： lightweight AI edge computing low-voltage distribution network cloud-edge computing particle swarm optimization algorithm

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A Multi-Producer Group-Search-optimization Method-Based Maximum-Power-Point-Tracking for Uniform and Partial Shading Condition

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IEEE ACCESS 2020年 8卷 184688-184696页

作者： Li, Lijuan Chen, Yongdong Liu, Hongliang Tang, Wenbing Liu, Mengni Wu, Jun Li, Zeyu Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Xiangtan Univ Coll Math & Computat Sci Xiangtan 411105 Peoples R China Changde Power Supply Co State Grid Hunan Elect Power Co Changde 415001 Peoples R China

The power-voltage (P-U) curve of PV array shows multiple power points, which brings challenges to fast and accurately tracking of the global maximum power point. Considering the nonlinearity and the multi-peak characteristics of PV array output curve under the condition of partial shadow, a multi-producer group search optimization (MGSO) method for maximum power point tracking (MPPT) is proposed in this paper. In the MGSO, the characteristics and operations of three categories of members, including producers, scroungers, and rangers, are set according to the P-U characteristics. The number of producers is determined by the number of peaks. The initial position of each producer locates dispersedly to each peak region which makes the producers not fall into local optimum. The search strategy is simplified by the proposed angle transformation function of scroungers and omitting the ranger. The results of simulation and comparison demonstrate that the proposed MGSO method can effectively track the maximum power point under the uniform irradiance or partially shaded conditions, and also increase the utilization of solar energy.

关键词： Maximum power point trackers optimization Lighting Software algorithms particle swarm optimization Photovoltaic systems Licenses PV utilization group search optimization algorithm maximum power point tracking particle swarm optimization algorithm photovoltaic system

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A triangular acoustic emission source location method considering its anisotropic propagation behavior on the surface of finger-joint-laminated board

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WOOD MATERIAL SCIENCE & ENGINEERING 2024年

作者： Zhao, Jialong Li, Ming Fang, Saiyin Mao, Feilong Shen, Zhihui Zhao, Yue Qin, Gezhou Zhu, Daigen Southwest Forestry Univ Sch Machinery & Transportat Kunming 650224 Yunnan Peoples R China Anhui Polytech Univ Minist Educ Key Lab Adv Percept & Intelligent Control High End Wuhu 241000 Anhui Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu Anhui Peoples R China

To understand the anisotropic propagation characteristics of acoustic emission (AE) signals in finger-joint-laminated boards, an improved planar localization method for AE sources is proposed. Initially, the AE source was generated by the pencil-lead break (PLB) at a fixed position on the surface of the glued wood specimen, and two AE sensors were arranged in different directions at a fixed distance of 60 mm, and the AE propagation velocity was calculated according to the time difference of arrival (TDOA). For clarity, the grain parallel direction was defined as 0 degrees, with velocities measured every 10 degrees through a counterclockwise rotation. Using these measurements, two angular anisotropic wave velocity models were developed. Subsequently, three AE sensors arranged linearly on the specimen surface pinpointed the AE source's location. This source location problem was formulated as a multi-parameter optimization model, constrained by the geometric relationships between the AE source and the sensors. A particle swarm optimization (PSO) algorithm was utilized to estimate the AE source's angles relative to each sensor. The findings indicate that the located AE sources deviated by 6.0-19.0 mm from the predetermined PLB positions, with inaccuracies largely attributed to the anisotropic AE wave velocity models' precision.

关键词： Non-destructive testing wood anisotropy wave velocity model particle swarm optimization algorithm

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Multi-agent learning mechanism design and simulation of multi-echelon supply chain

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 168卷

作者： Sun, Jun-Yan Tang, Jian-Ming Chen, Zhi-Rui Shaanxi Univ Sci & Technol Fac Mech & Elect Engn Xian 710021 Peoples R China Xian Aerosp Precis Electromech Inst Xian 710010 Peoples R China

A supply chain multi-agent learning mechanism based on the particle swarm optimization algorithm is designed. The manufacturer's profit and the product utility are taken as objective functions to explore the influences of social learning ability and self-learning ability on the manufacturer's optimal price, optimal advertising in-vestment, optimal profit, product utility, and supply chain competition. This research demonstrates the in-fluences of different learning abilities on the evolution of the supply chain. The simulation results show that the most appropriate social learning ability can enhance competition between manufacturers and improve product utility. With the enhancement of self-learning ability, manufacturers have a wider range of pricing, products are more diverse, and consumers have a wider choice of goods. This study shows certain guiding significance for the scientific management of the supply chain and the optimization of the competitive strategy of the enterprises on the chain.

关键词： particle swarm optimization algorithm Multi-echelon supply chain Multi-agent Learning mechanism Simulation analysis

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Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics

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SCIENTIA IRANICA 2016年第3期23卷 1429-1440页

作者： Mahmoodirad, A. Sanei, M. Islamic Azad Univ Cent Tehran Branch Dept Math Tehran Iran

This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is an NP-hard, three meta-heuristics algorithms, namely Differential Evolution (DE), particle swarm optimization (PSO), and Gravitational Search algorithm (GSA), are developed for the first time for this kind of problem. To the best of our knowledge, neither DE, nor PSO, nor GSA have been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Supply chain network design Differential evolution particle swarm optimization algorithm Gravitational search algorithm Taguchi experimental Design

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A novel integrated hydrogen and natural gas liquefaction process utilizing a modified double mixed refrigerant process pre-cooling system

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APPLIED THERMAL ENGINEERING 2023年 224卷

作者： Geng, Jinliang Sun, Heng China Univ Petr Natl Engn Res Ctr Oil & Gas Pipeline Transportat S MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Beijing 102249 Peoples R China

The hydrogen liquefaction process is energy-intensive due to the lower liquefaction temperature. To improve the energy efficiency of the hydrogen liquefaction process, a novel integrated liquefaction process employing a modified double mixed refrigerant process pre-cooling is proposed and analyzed. The three-stage adiabatic ortho-para hydrogen conversion makes the process compact. Furthermore, the particle swarm optimization algorithm is utilized to find the optimal decision variables. The result reveals that the total specific energy consumption (SEC) of the process is 6.5921 kWh/kgLH2-LNG, which is 15.83% lower than the base case. The SECs for the production of LNG and LH2 are 0.2445 kWh/kgLNG and 6.3476 kWh/kgLH2. Moreover, the exergy analysis and thermodynamic evaluation reveal that the exergy efficiency, coefficient of performance, and figure of merit of the process are 49.26%, 0.2306, and 0.49, exhibiting high thermodynamic performance. Finally, the sensi-tivity analysis indicates that the evaporation pressure of the sub-cooling refrigerant has the greatest impact on the performance of the process. The proposed process has an important reference value in the design of the combined production of LH2 and LNG from industrial by-products and the H2 liquefaction process.

关键词： Integrated liquefaction process Mixed refrigerant Liquefied hydrogen Liquefied natural gas particle swarm optimization algorithm

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