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IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Qu, Hairong Zhang, Runnan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Hohai Univ Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9781665416061

After entering the new era, people's living standard has been significantly improved, the concept of environmental protection has been deeply rooted. People pursue a greener and healthier lifestyle, and the concern for air quality has become more and more intense. People need to continuously analyze relevant mechanisms to help people predict air environment quality effectively and prevent relevant hazards in time. It is difficult to obtain a better prediction effect under different AQI fluctuation trends in the traditional single machine learning model for air quality prediction. In order to solve the problem, the prediction method is improved, and the ELM-PSO algorithm is used to predict the future AQI, which helps to analyze the future air change trends from a macro perspective. This bit combines Beijing's short-term air quality data and proposes an air quality short-term prediction model based on complementary ensemble empirical modal decomposition and an optimal limit learning machine. Firstly, the data are decomposed by CEEMD to reduce the non-smoothness of the data, thus reducing the impact of non-smoothness on the prediction accuracy;then ELM model prediction is performed for each decomposed series obtained;the output weights of the limit learning machine are used to PSO optimization search, and then construct the prediction model based on the limit learning machine;finally, all the prediction components are superimposed to obtain the final results. The results surface that the prediction model proposed in this paper has high accuracy in short-term air quality prediction.

关键词： Air quality prediction Extreme learning machine particle swarm optimization algorithm Complementary ensemble empirical modal decomposition Machine learning

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Global optimization algorithm for Cloud Service Composition

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第10期E104D卷 1580-1591页

作者： Yang, Hongwei Xue, Fucheng Liu, Dan Li, Li Feng, Jiahui Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun Peoples R China

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

关键词： web service composition beetle antennae search algorithm particle swarm optimization algorithm global optimization algorithm

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Hadoop Processing Methods for Large Scale Video Data

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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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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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Oral cancer detection using convolutional neural network optimized by combined seagull optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 87卷

作者： Huang, Qirui Ding, Huan Razmjooy, Navid Nanyang Inst Technol Sch Informat Engn Nanyang 473004 He Nan Sheng Peoples R China Ankara Yildirim Beyazit Univ Dept Ind Engn Ankara Turkiye Saveetha Sch Engn SIMATS Dept Comp Sci & Engn Div Res & Innovat Chennai 602105 Tamil Nadu India Islamic Univ Coll Tech Engn Najaf Iraq

Early diagnosis of oral cancer is crucial for improving patient outcomes and saving lives. However, inaccurate and improper diagnosis can hinder effective treatment. This paper presents a novel method for detecting oral cancer using an optimized version of Convolutional Neural Network (CNN). While basic CNNs have been widely used for image classification tasks, the incorporation of the Seagull optimization algorithm and particle swarm optimization algorithm in optimizing the CNN architecture specifically for oral cancer detection is a unique approach that is provided in this study. By combining these algorithms, the proposed method optimizes the CNN's architecture, parameters, and training process specifically for oral cancer detection. This optimization enhances the performance and accuracy of the CNN in identifying cancerous regions in oral images. Unlike previous approaches, our method incorporates advanced image processing techniques, including noise reduction, contrast enhancement, and data augmentation, to enhance the quality of input data extracted from the Oral Cancer (Lips and Tongue) images (OCI) dataset. The optimized CNN architecture uses its ability to learn intricate patterns and features from the enhanced images, enabling more accurate identification of cancerous regions. To evaluate the effectiveness of our approach, we compare it against Textural analysis, FCM, CNN, R-CNN, and ResNet-101 using four measurement indices. Results demonstrate that our proposed CSOA-based CNN system achieves the highest accuracy rate (96.94%) compared to other methods, indicating its superior performance in oral cancer detection. Furthermore, our precision rate of 94.65% and recall rate of 91.60% highlight the model's high correctness and positive classification ability. Finally, our proposed method achieves the highest F1-score (88.55%), emphasizing its superiority over other comparative methods. Through our innovative integration of the Seagull optimization algorithm a

关键词： Oral cancer Diagnosis Convolutional neural network Deep learning Combined seagull optimization algorithm particle swarm optimization algorithm

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Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Xu, Lei Rongke Digital Energy Informat Technol Beijing Co Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

关键词： Energy hub systems Renewable energy integration Multi-objective optimization particle swarm optimization algorithm System reliability

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Performance Study of Random Layout Light Source for Visible Light Communication System

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PHOTONICS 2024年第12期11卷 1127-1127页

作者： Ke, Xizheng Zheng, Yuwei Liang, Jingyuan Qin, Huanhuan Xian Univ Technol Fac Automat & Informat Engn Xian 710048 Peoples R China Shaanxi Civil Mil Integrat Key Lab Intelligence Co Xian 710126 Peoples R China

In indoor visible light communication, a rational layout of light sources is required to ensure that multiple users at different locations in the room can obtain better communication quality and achieve uniform coverage of optical power on the receiving surface. The article investigates the performance of an indoor visible light communication system when the random layout of the Matern hardcore point process is used and compares it with the communication performance under the Poisson point process and the binomial point process. A particle swarm optimization algorithm is introduced based on the Matern hardcore point process, where the points generated under the Matern hardcore point process are used as the initial positions of the particles, and optimization adjustments are made according to the objective function to find the optimal layout. The results show that compared with the Poisson point process and the binomial point process, the use of the Matern hardcore point process to randomly lay out the LEDs makes the light intensity in the system more uniform and the numerical fluctuation of the received power is smaller. The uniformity of the indoor illumination after the combination of the Matern hardcore point process and the particle swarm optimization algorithm reaches 0.84, the deviation of the peak power is reduced by 20%, and the average signal-to-noise ratio value is 0.86, which is an increase in the average signal-to-noise ratio compared to the average signal-to-noise ratio before optimization.

关键词： visible light communication light source layout matern hardcore point process particle swarm optimization algorithm signal-to-noise ratio

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Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 046118-046118页

作者： Xu, Mengjie Mo, Jiahui Xia, Chenyan Yang, Lei Ji, Feng Chen, Jingjing Li, Xingfei Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Hefei Univ Technol Anhui Prov Key Lab Measuring Theory & Precis Instr Hefei 230009 Anhui Peoples R China Tianjin Univ State Key Lab Precise Measurement Technol & Instru Tianjin 300072 Peoples R China

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1-2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.

关键词： magnetohydrodynamic linear motion sensor error analysis particle swarm optimization algorithm variational mode decomposition

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Application of Fuzzy Predictive Control for Smoke Sensing Alarm in Automatic Train Driving Control

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IEEE ACCESS 2024年 12卷 150719-150738页

作者： Huang, Peili Zhengzhou Police Univ Sci Publ Order Dept Zhengzhou 450000 Peoples R China

The efficacy of traditional smoke detection methods is often constrained by the use of fixed threshold judgments, which impedes the ability of these methods to accurately and timely detect smoke, thereby increasing the risk of train operation. The objective of this study is to enhance the precision and reliability of automated driving control, thereby enabling vehicles to adapt to diverse and challenging environments more effectively. Based on fuzzy predictive control and granular computing, the algorithm is improved to solve the problem of rule explosion. Combining the annealing algorithm and particle swarm optimization algorithm to optimize the system parameters, a new fuzzy predictive control system for smoke sensing alarms is designed. The experimental results showed that the proposed algorithm on the Rosenbrook algorithm was concentrated between 45 and 51, demonstrating good stability and GSC, and having an excellent ability to jump out of local optima. Furthermore, the model demonstrated a rapid and stable convergence of the prediction accuracy curve, with an average prediction accuracy of 95.1% and a final value of 97.8%. The proposed smoke sensing alarm fuzzy predictive control has the advantages of being less prone to local optima, high accuracy, short time consumption, and fast response in automatic driving control. It provides new solutions and methods for other similar problems in the field of intelligent transportation.

关键词： Prediction algorithms Accuracy Fuzzy logic Safety optimization Predictive control Temperature sensors Reliability Predictive models Sensors particle swarm optimization Smoke detectors Fuzzy predictive control annealing algorithm particle swarm optimization algorithm smoke alarm metropolis guidelines

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