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

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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Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Nia, Parastoo Sadeghi Hesar, Hamed Danandeh Sahand Univ Technol Fac Biomed Engn Tabriz Iran

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time-frequency and time-statistical features. Prior to data classification, we employ two techniques - particle swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.

关键词： Phonocardiogram particle swarm optimization algorithm Feature Selection Classification

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Equivalent circuit modeling and state-of-charge estimation of lithium titanate battery under low ambient pressure

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JOURNAL OF ENERGY STORAGE 2024年 77卷

作者： Li, Guishu Xie, Song Guo, Wenqi Wang, Qilin Tao, Xin Civil Avit Flight Univ China Civil Aircraft Fire Sci & Safety Engn Key Lab Sichuan Prov Guanghan 618307 Sichuan Peoples R China

The low ambient pressure during the flight of aircraft has a significant impact on the performance and safety of the onboard power battery. In order to ensure the safe operation of the battery system, it is very important to accurately estimate and manage the state-of-charge (SOC) of the battery. In this work, the equivalent circuit modeling (ECM) of lithium titanate battery (LTB) is studied in detail, and the influence of analog circuit model parameters in low ambient pressures is discussed for the first time. The forgetting factor recursive least square algorithm is introduced to accurately identify the ECM parameters of the LTB under different pressures online. The particle swarm optimization algorithm is innovatively proposed to optimize the covariance matrix of the Kalman filter algorithm. The verification shows that the root mean square error of the ECM of LTB under different ambient pressures is less than 0.025. In the SOC estimation process, the noise covariance matrixes of the extended Kalman filter and the unscented Kalman filter are optimized by the particle swarm algorithm. The optimized SOC estimation absolute error is less than 3 %, especially at 96 kPa and 30 kPa, where the absolute error is less than 2 %.

关键词： Low-pressure environment State of charge Kalman filtering particle swarm optimization algorithm Recursive least square

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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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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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Short-term mathematical prediction model of air quality based on CEEMD-ELM-PSO

Short-term mathematical prediction model of air quality base...

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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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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 NIPOS-SVM Model for Evaluation of Water Richness of Coal Seam Roof Aquifer-A Case Study of the Xinhu Coal Mine in Huaibei, China

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WATER 2024年第24期16卷 3670-3670页

作者： Xing, Maolin Wang, Qiqing Xu, Jie Li, Wenping China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China CCTEG Xian Res Inst Grp Co Ltd Xian 710077 Peoples R China State Key Lab Coal Mine Disaster Prevent & Control Xian 710077 Peoples R China Shaanxi Key Lab Prevent & Control Technol Coal Min Xian 710077 Peoples R China

The sandstone roof of coal seams, with its high porosity and developed fissures, serves as a favorable reservoir for groundwater. Predicting and assessing the water-bearing capacity of the sandstone roof in coal seams is crucial for the rational development of coal tunnels, ensuring safe and efficient production in mining areas. This study targets the Cenozoic bottom aquifer of the No. 81 mining area of the Xinhu Coal Mine. By analyzing the geological and hydrogeological conditions of the mining area, it was found that the primary water-bearing strata of the coal seam roof are the Permian sandstone fracture waters. Key factors for evaluating the water richness of the sandstone aquifer were identified as aquifer thickness, aquifer depth, core recovery rate, coal seam dip angle, brittleness-plasticity ratio, and the sand-mud interlayer index. A novel particle swarm optimization algorithm incorporating improved sine chaos mapping (NIPSO) to enhance the support vector machine (SVM), thereby constructing the NIPSO-SVM model, was applied for quantitative evaluation of water richness in the study area. Experimental results indicated that the NIPSO-SVM model has high accuracy and practical engineering application value in predicting water richness, which is significant for ensuring the safe production of coal mines.

关键词： water-richness support vector machine model particle swarm optimization algorithm NIPSO GIS

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