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Two-layer coordination architecture HIF detection with μPMU data

JOURNAL OF ENGINEERING-JOE 2018年第15期2018卷 1033-1037页

作者： Wang, Xiaojun Zhang, Yongjie Luo, Yiping He, Jinghan Ling, Ping Fang, Chen Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China State Grid Shanghai Municipal Elect Power Co Elect Power Res Inst Shanghai 200437 Peoples R China

The detection of high-impedance fault (HIF) on distribution network has been one of the most difficult problems. This study presents a data-driven method for HIF detection by using single-ended micro-phasor measurement unit (mu PMU). This approach is based on the two-layer coordination architecture, local side with PMUs and master station for further analysis. At the local side, the PMUs gather the synchronous data and achieve feature extraction with k-means clustering algorithm and principal component analysis. For determining the amounts of data categories, the authors adopt a method based on silhouette coefficient. Meanwhile, send the characteristics to the master station, then detect the HIF fault through the support vector machine. Finally, the method was tested on a 34 nodes distribution network in PSCAD/EMTDC. The results justify the effectiveness and the proposed detection scheme has >85% accuracy.

关键词： pattern clustering support vector machines feature extraction principal component analysis phasor measurement fault diagnosis power distribution faults power engineering computing detection scheme two-layer coordination architecture HIF detection PMU data data-driven method master station synchronous data principal component analysis data categories HIF fault 34 nodes distribution network high-impedance fault detection single-ended microphasor measurement unit feature extraction k-means clustering algorithm silhouette coefficient support vector machine PSCAD-EMTDC

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STUDY OF WIRE SEGMENTING algorithm OF SUBSTATION EQUIPMENT BASED ON 3D POINT CLOUD

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2023年第6期19卷 1785-1803页

作者： Wang, Pengshuai Luo, Yong Wang, Heshan Zhang, Haiquan Zhengzhou Univ Sch Elect & Informat Engn 100 Sci Ave Zhengzhou 450001 Peoples R China

In this paper, a cone search method is proposed to segment the wire from substation equipment based on three-dimensional (3D) point cloud. On the one hand, the segmentation of the wire which connects to substation equipment can improve the recognition rate of the point cloud of substation equipment. On the other hand, it can reduce operation workload and improve the efficiency of the intelligent identification of substation equipment. In the process of wire segmentation, first, the position of substation device is located by rotating, and two endpoints of the wire are found. Second, other endpoints of the wire are found by using k-means clustering algorithm and the nearest neighborhood search algorithm. Then use linear fitting based on three-dimensional point cloud to search the data of wire and solve the problem of defective wire points. Finally, using the cone search to confine search direction, find out the wire points and remove it. The key findings of this study include the following. (a) A cone search method proposed to divide wires from substation equipment in 3D point cloud is effective, and (b) using linear fitting based on three-dimensional point cloud can solve the problem of defective wire points. Experiments show that using the method can remove the wire well in substation equipment.

关键词： Wire segmenting k-means clustering algorithm Linear fitting Substation equipment Cone search method

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Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第2期13卷 23页

作者： Zhang, Shiyu Yang, Qing Gao, Yuchen Gao, Dexin Qingdao Univ Sci Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sci Technol Coll Informat Sci & Technol Qingdao 266061 Peoples R China

During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a k-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame;the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

关键词： electric vehicle charging stations machine vision fire detection YOLOv4 k-means clustering algorithm

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An Efficient Predictive Model for Probability of Genetic Diseases Transmission

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INTERNATIONAL JOURNAL OF ENGINEERING 2017年第8期30卷 1152-1159页

作者： Hamidi, H. Qaribpour, F. KN Toosi Univ Technol Dept Ind Engn Informat Technol Grp Tehran Iran

In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.

关键词： Genetic Disease Transmitting Genetic Diseases k-means clustering algorithm Data Mining Decision Trees

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A computational approach for optimizing vehicles' interior noise and vibration

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INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING 2017年第4期14卷 4690-4703页

作者： Al-Dhahebi, Adel Mohammed Junoh, Ahmad kadri Mohamed, Zamri Muhamad, Wan Zuki Azman Wan Univ Malaysia Perlis Inst Engn Math Arau Perlis 02600 Malaysia Univ Malaysia Pahang Fac Mech Engn Pekan Pahang 26600 Malaysia

This paper proposes a Genetic algorithm (GA) to optimise vehicles' interior noise and vibration caused by powertrain, tire-road surface interaction and type of car. Toward this end, an experimental design was carried out to obtain the noise and vibration data of three local compact-sized cars at stationary and running conditions and varying engine speeds. The acquired data were analysed to obtain sound quality parameters such as loudness and sharpness, sound pressure level and vibration exposures in the interior cabin. Besides that, a k-means clustering algorithm was utilised to cluster the noise and vibration to determine the comfort level in the vehicle's interior cabin. The overall findings indicate that the comfort level is influenced by the types of road surface, powertrain and vehicle design. The results also indicate that the proposed GA approach is reliable and can be utilised by automotive researchers to identify the optimal Noise, Vibration and Harshness (NVH) values for vehicle refinement and noise control.

关键词： k-means clustering algorithm Genetic algorithm Interior Vehicle Noise and Vibration Optimization Sound Quality Structure Vibrations Experimental Designs of NVH

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Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第3期22卷

作者： Singh, Swati Gupta, Sheifali Tanta, Ankush Gupta, Rupesh Himachal Pradesh Univ Elect & Commun Engn Univ Inst Technol Shimla 171005 India Chitkara Univ Inst Engn & Technol Chandigarh 140401 Punjab India Chitkara Univ Sch Engn & Technol Chandigarh 174103 Himachal Prades India

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using k nearest neighbor classifier.

关键词： Image segmentation brightness preserving dynamic fuzzy histogram equalization k-means clustering algorithm support vector machine k nearest neighbor Naive Bayes

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Study on the Grouping of Patients with Chronic Infectious Diseases Based on Data Mining

Study on the Grouping of Patients with Chronic Infectious Di...

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作者： Min Li Shanghai Public Health Clinical Center Fudan University College of Economics and Management Nanjing University of Aeronautics and Astronautics

Objective:According to RFM model theory of customer relationship management,data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different ***:170,246 outpatient data was extracted from the hospital management information system(HIS) during January 2016 to July 2016,43,448 data was formed after the data cleaning.k-means clustering algorithm was used to classify patients with chronic infectious diseases,and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious ***:Male patients accounted for 58.7%,patients living in Shanghai accounted for 85.6%.The average age of patients is 45.88 years old,the high incidence age is 25 to 65 years *** was gathered into three categories:1) Clusters 1—Important patients(4786 people,11.72%,R = 2.89,F = 11.72,M = 84,302.95);2) clustering 2—Major patients(23,103,53.2%,R = 5.22,F = 3.45,M = 9146.39);3) Cluster 3—Potential patients(15,559 people,35.8%,R = 19.77,F = 1.55,M = 1739.09).C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases,the final treatment time(weeks) is an important predictor,the accuracy rate is 99.94% verified by the confusion ***:Medical institutions should strengthen the adherence education for patients with chronic infectious diseases,establish the chronic infectious diseases and customer relationship management database,take the initiative to help them improve treatment *** governments at all levels should speed up the construction of hospital information,establish the chronic infectious disease database,strengthen the blocking of mother-to-child transmission,to effectively curb chronic infectious diseases,reduce disease burden and mortality.

关键词： Data Mining k-means clustering algorithm C5.0 Decision Tree algorithm Customer Relationship Management Patients with Chronic Infectious Disease

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Aerial Disaster Relief Response System Research

Aerial Disaster Relief Response System Research

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作者： Hongyang He Zhenlin Chen Electronic and Information Engineering Guangdong Normal University of Technology

In order to solve the problem of medical supply in the affected areas, we designed a disaster response system to provide medical package transportation and related route detection for five regions. To solve the first problem, we built two optimization models for 3 D boxing. By considering the size of the medical package and cargo bay, the first model of the number of medical packages required in the affected area was established. Solve the distribution plan of medical package by MATLAB. Then, by considering the size of ISO containers and shipping containers and the number of cargo containers, a three-dimensional packing optimization model is established. Solve in MATLAB to determine the type and number of drones needed. This resulted in a complete disaster response system consisting of a drone fleet and a medical packag To solve the second problem, we use cluster analysis. First, the five disaster areas were classified into three categories to determine the three best cluster centers as the best locations for the three ISO containers. Then, the Euclidean distance was taken as the similarity between the two points, and the three optimal positions were obtained by k-means clustering algorithm as x6=(18.45,-66.40),x7=(18.40,-66.16),x8=(18.27,-68.84)Finally, according to the demand characteristics of the disaster area and the video reconnaissance capability of the drone, the corresponding ISO container was allocated.

关键词： Three-dimensional packing Nonlinear optimization model k-means clustering algorithm 0-1 programming model MATLAB

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An Analysis of Government Subsidies Enterprise Training Based On CDC algorithm

An Analysis of Government Subsidies Enterprise Training Base...

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The Sixth International Conference on Information and Management Sciences

作者： Menz-Ru Huang Ruey-Gwo Chung Tung-Shou Chen Hsuan-Yi Cheng Yung-Ching Lin Department of Industrial Education and Technology National Changhua University of Education 500 Taiwan China Department of Industrial Education and Technology National Changhua University of Education 500 Taiwan China Graduate School of Computer Science and Information Technology National Taichung Institute of Technology Graduate School of Computer Science and Information Technology National Taichung Institute of Technology Graduate School of Computer Science and Information Technology National Taichung Institute of Technology

Taiwan government subsidizes more than 10 billion funds to enterprises every year for on-the-job training The main goal is to guide enterprise to provide on-the-job training for employees after obtains the suitable subsidy fund,and improves quality of work ***,after various enterprises receive the subsidy,whether can really provide effective training which fulfills the market demand is worth to *** this research using data mining method to analysis database from Bureau of Employment Vocational Training(BEVT)in Taiwan base on Combined Density-and Constraint-based algorithm (CDC).Since CDC algorithm has the characteristic which can reduce the effect of noise distortion and outliers,it can be more accurate and faster to analysis the data in order to find the relationship between the amount of government subsidy and the effectiveness of on-the-job *** algorithm basically has two *** division stage,it uses the concept of data density to clustering the data,and isolates the noise distortion and outliers simultaneously in order to discover the cluster centroid,then take the k-mean cluster center as the concept,take larger cluster as the center, merges into smaller clusters with limited mean error,finally completes the calculation *** finding shows significant positive correlation between the amount of government subsidy and the return on investment of training. Since enterprise has links training investment to organizational performance,therefore,it will also receive more government *** if the enterprise continue conduct structure training program,the enterprise will have more complete quality training environment to continuously re-invest in and upgrade the competence of their human resources.

关键词： CDC on-the-job Training k-means clustering algorithm

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Evaluation of a language learning application in Facebook

Evaluation of a language learning application in Facebook

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International Conference on Information, Intelligence, Systems and Applications

作者： Christos Troussas Maria Virvou Jaime Caro kurt Junshean Espinosa Department of Informatics University of Piraeus Department of Computer Science University of the Philippines Quezon City

ISBN: (纸本)9781479907694

Facebook applications are more convenient and flexible to use, since students tend to invest a good deal of time in the use of such technologies, given that Facebook retains the educational quality, as it is widely used in instructional contexts. To this direction, our Facebook application promotes language learning. Furthermore, it incorporates machine learning techniques in order to offer user classification based on multiple user characteristics. Our resulting Facebook language learning application has been evaluated by both instructors and Facebook users. The results of the evaluation showed that Facebook provides opportunities within curriculum education and learning and user classification in such applications promotes the cognitive procedure.

关键词： Evaluation Facebook k-means clustering algorithm Language learning

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