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Prediction of wind energy location by parallel programming using MPI-based kmeans clustering algorithm

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 5451-5473页

作者： Ponnuswamy, Priya Palaniappan, Shabariram Chokkalingam PSG Inst Technol & Appl Res Dept Comp Sci & Engn Coimbatore Tamil Nadu India PSG Inst Technol & Appl Res Dept Comp Sci & Engn Coimbatore 641062 Tamil Nadu India

Renewable energy resources, like the power of the wind, are the essential sources of energy in today's world. To keep down the greenhouse energy degasification and stop global warming, it is very important to predict the exact location where maximum wind energy is generated. The total amount of electrical power produced by a turbine relies more on the speed of the wind, pressure created by the wind, and weather conditions. The proposed method predicts the maximum power generated in a particular location using the current conditions of the weather, pressure generated through wind, and its speed. The speed of the wind and pressure of the wind are clustered using the Message Passing Interface (MPI) based kmeans clustering algorithm. The system minimizes the amount of time required for clustering and it is done by MPI. clustering is formed with the help Euclidean distance of each point. The wind data is collected and formed into three clusters such as low, medium, and high based on the speed of the wind. The parameters for evaluation are considered as the speed of the wind, and the direction of the wind are determined. The results show that the speed of the wind varies up to 25 km/s with the power of 2000 watts. The proposed method compares the execution time of sequential and MPI-based kmeans clustering for different numbers of clusters. The sequential kmeans clustering algorithm takes 3% to 7% more time compared to MPI-based kmeans clustering algorithm. For the maximum cluster size of 5, the MPI-based kmeans clustering algorithm produces the result in 0.42 s.

关键词： Parallel clustering message passing interface wind power prediction wind pressure wind direction weather condition kmeans clustering algorithm

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Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced algorithms

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IEEE ACCESS 2025年 13卷 7284-7292页

作者： Yan, Man Li, Zhou Dong, Zheng Liun, Yiming Chen, Liyun Wu, Xiaosong Wu, Lijun South China Normal Univ Sch Informat & Optoelect Sci & Engn Guangdong Prov Key Lab Nanophoton Funct Mat & Dev Guangzhou 510006 Guangdong Peoples R China Guangzhou Keii Electro Opt Technol Co Ltd Guangzhou 510663 Peoples R China

Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those with small flow rates by only utilizing economical equipments. In this paper, we present a strategy for automating real-time identification and quantification of gases in the mid-infrared band by combining an infrared camera combined with a series optimized algorithms. A basic network DeepLabV3+ is first modified by replacing its Xception backbone with MobileNetv2 for real-time gas detection and segmentation. Then special attention mechanisms tailored to the characteristics of the gas are added into the network to enhance the perception and recognition of the gas edges. The optimized kmeans clustering algorithm is integrated to identify the Region of Interest (ROI) in the image containing the target gas. The quantification of the volume flow rate within the ROI is realized by integrating the radiation transfer model with the optical flow method. The experimental results indicate that the quantification limit of the gas flow rate can reach 0.01 L/min, which is comparable to that obtained by the methods with complicated instruments. Our detection and quantification strategy can find vast applications in hazardous gas monitoring field.

关键词： Cameras Gases Accuracy Real-time systems Training Optical filters Image edge detection clustering algorithms Gas lasers Fluid flow Gas identification gas quantification DeeplabV3+neural network kmeans clustering algorithm optical flow method

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Extraction and expression of deformation features for wear images from four-ball friction test: For classification purpose

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2023年第2期237卷 268-275页

作者： Xiao, Mei Liu, Yu Zhang, Lei Jia, Wenjian Dong, Shi Changan Univ Coll Transportat Engn Xian 710064 Shaanxi Peoples R China Changan Univ Lab & Equipment Management Dept Xian Peoples R China

The shape characteristics of wear spot in the four-ball wear test are often qualitatively identified by human testers based on experience, which are subjective, unquantifiable and biased. This paper proposes an automated method for extracting and expressing the shape features, which is effective for the deformed classification of the wear images. First, the wear spot is segmented from the background, and the geometric parameters of the wear spot are extracted. Based on this, the optimal standard wear spot which match the segmented (or actual) wear spot best is constructed. The established feature variables (i.e. abnormality rate and eccentricity rate) are used to quantitatively assess the deformation degree of segmented wear spot, by comparing the segmented wear spot with the constructed standard wear spot. Finally, kmeans clustering algorithm is used to verify the validity of deformation features in the classification application. Simulation test results show that feature variables are valuable for identifying the deformation of wear images, and three effect evaluation indicators (CH/SI/DB) shows that the classification effect of the wear images is better.

关键词： Deformation features extraction and expression kmeans clustering algorithm standard wear spot reconstruction wear image classification

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Comparative Analysis of Motors with Inner and Outer Reluctance Rotors and PM Stators

Comparative Analysis of Motors with Inner and Outer Reluctan...

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Transportation Electrification Conference and Expo (ITEC)

作者： Badewa, Oluwaseun A. Ionel, Dan M. Univ Kentucky Stanley & Karen Pigman Coll Engn SPARK Lab Lexington KY 40506 USA

ISBN: (纸本)9798350317671;9798350317664

This paper presents a comparison of special outer and inner reluctance rotor motor topologies with permanent magnet (PM) stators having toroidal AC windings. A systematic approach is taken to study and discuss the design geometries, the respective impact on performance, and adoption of technological advancements such as stator-only cooling and hair-pin winding for high slot fill factor. Parametric FEA models are developed for both topologies, and design of experiments (DoE)-based sensitivity analysis is used to study the effect of independent variables on specific performance metrics such as torque, motor loss, torque ripple, and power factor. Inner and outer rotor topologies are compared using the resulting Pareto fronts from multi-objective optimization. Optimized outer rotor designs using both non-rare earth PMs and ferrites are also investigated over selected drive cycles. The efficiency per cycle is evaluated at seven of the most representative points of the drive cycle obtained using a k-means clustering algorithm for the world harmonized light vehicle test procedure (WLTP), Orange County, and parcel truck (Baltimore) drive cycles.

关键词： Electric vehicle synchronous motor Finite Element Analysis (FEA) non-rare earth PM flux switching hair-pin winding sensitivity analysis optimization drive cycle analysis kmeans clustering algorithm

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Improved back propagation neural network method for predicting sulfur content in hot metal

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METALLURGICAL RESEARCH & TECHNOLOGY 2023年第1期121卷

作者： Zhang, Song Gu, Maoqiang Zong, Yanbing Wang, Zhenyang Zhang, Jianliang Jiang, Dewen Pang, Jing Zhang, Shushi Si, Ruishuai Univ Sci & Technol Beijing Sch Met & Ecol Engn Beijing 100083 Peoples R China Bijie Big Data Ind Dev Ctr Bijie 551700 Guizhou Peoples R China Univ Queensland Sch Chem Engn St Lucia Qld 4072 Australia

Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (BPNN) model has a strong ability to solve nonlinear problems. However, BPNN has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining kmeans and BPNN is proposed in this paper. The study showed that compared with the BPNN model and case-based reasoning (CBR) model, the kmeans-BPNN model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The kmeans-BPNN model has the largest HR value, which indicates the highest prediction accuracy. The proposed kmeans-BPNN prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.

关键词： blast furnace hot metal sulfur content BPNN algorithm kmeans clustering algorithm predictive models

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Anomaly electricity detection method based on entropy weight method and isolated forest algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Jianyuan Gu, Chengcheng Liu, Kechen Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin Peoples R China

This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors.

关键词： abnormal power consumption detection isolation forest algorithm kmeans clustering algorithm entropy weight method principal component dimension 11

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