2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT)最新文献_第6页

Research on Insulator Detection Method Based on Scene Recognition 基于场景识别的绝缘子检测方法研究

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00031

Zhimin Li, Fan Yang, Tian Tan, Xu Lu, Jie Tian

引用次数: 0

Comprehensive energy efficiency rating evaluation model of enterprise power based on grid big data 基于电网大数据的企业电力综合能效等级评价模型

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00025

Wenrui Yan, Fanmao Jiang, Aihua Liu, J. Xu, S. Zhang

{"title":"Comprehensive energy efficiency rating evaluation model of enterprise power based on grid big data","authors":"Wenrui Yan, Fanmao Jiang, Aihua Liu, J. Xu, S. Zhang","doi":"10.1109/iceert53919.2021.00025","DOIUrl":"https://doi.org/10.1109/iceert53919.2021.00025","url":null,"abstract":"In the enterprise power comprehensive energy efficiency rating evaluation model, there is a problem of unclear definition of user attributes, which affects the evaluation accuracy. The enterprise power comprehensive energy efficiency rating evaluation model is designed based on power grid big data. Preprocess massive big data information, screen out effective and complete user data, establish user portrait based on power grid big data, and clarify user attribute labels. The comprehensive energy efficiency evaluation index system of enterprise power is designed according to the user portrait. Based on the evaluation of each bottom index, it is scored by the normal distribution method. Calculate the weight of the same level elements, construct the power comprehensive energy efficiency grade evaluation model, and use the clustering algorithm to realize the grade evaluation. The results show that the average accuracy of the model is 95.23%, which is 12.79% and 8.53% higher than the results of the enterprise power comprehensive energy efficiency grade evaluation model based on decision tree and random forest. Therefore, this model can effectively determine the comprehensive energy efficiency level of enterprises.","PeriodicalId":278054,"journal":{"name":"2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131255148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Research on filed investigation techniques for storage and transportation accidents of hazardous goods in ports 港口危险货物储运事故现场调查技术研究

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00062

Lili Jiang, Chao Han, Fengyun Chen

引用次数: 0

Research on infrared image segmentation technology of transmission equipment based on local area Medoidshift clustering algorithm 基于局部mididshift聚类算法的传输设备红外图像分割技术研究

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00032

Biwu Yan, Tao Li, Yifan Guo, Mengshi Zhao

{"title":"Research on infrared image segmentation technology of transmission equipment based on local area Medoidshift clustering algorithm","authors":"Biwu Yan, Tao Li, Yifan Guo, Mengshi Zhao","doi":"10.1109/iceert53919.2021.00032","DOIUrl":"https://doi.org/10.1109/iceert53919.2021.00032","url":null,"abstract":"The extraction of the fault area in the infrared image is the critical process in the intelligent identification of power equipment faults. Since the infrared image has the characteristics of low contrast, regional gray unevenness, and blurring, there exist great difficulties in fast and accurate image segmentation. To meet the demand of infrared image field processing for mobile inspection of power equipment, this paper proposes an algorithm for extracting faulty areas based on local area Medoidshift clustering. The method combines the characteristics of the thermal fault area and the grayscale adjustment mechanism for the similar pixels in the neighborhood so that the pixels in the fault area are clustered under the Medoidshift algorithm. At the same time, to speed up the clustering process, a neighborhood clustering method based on segmenting the entire image by iteratively computing the current target cluster mean is adopted. Experimental tests on the typical infrared images show that the proposed method is effective in region extraction. Compared with other methods, the method in this paper has better performance in the speed and accuracy of fault region extraction.","PeriodicalId":278054,"journal":{"name":"2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of Digital twin for intelligent transportation system 智能交通系统数字孪生研究综述

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00064

L. Bao, Qiulan Wang, Yan Jiang

{"title":"Review of Digital twin for intelligent transportation system","authors":"L. Bao, Qiulan Wang, Yan Jiang","doi":"10.1109/iceert53919.2021.00064","DOIUrl":"https://doi.org/10.1109/iceert53919.2021.00064","url":null,"abstract":"Digital Twin (DT) is attracting the research interest of the traffic community in the last few years due to improvement of intelligent traffic management through the simulation of the transportation system predicting potential problems and optimizing traffic operation, which is considered as one of the most effective solutions of current traffic problems. In this paper, we propose a new DT concept of traffic based on characteristics of DT and connotation of traffic. We have summarized the difference and relationship between traditional traffic simulation and DT. A three layers technical architecture was proposed, including data access layer, calculation and simulation layer and management and application layer. Besides, we have analyzed the key technologies of DT in construction of traffic scenario and future applications of traffic DT. Results show intelligent expressway, self-driving, ITS remain the main developing directions of DT for traffic; although data mining, cloud computing and other data processing technologies have made some progress, in the face of massive traffic data, data loading technology and information extraction technology still need to be strengthened.","PeriodicalId":278054,"journal":{"name":"2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117079259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Optimizing Stencil Codes with Exploiting Data Reuse 利用数据重用优化模板代码

2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT) Pub Date : 2021-10-01 DOI: 10.1109/iceert53919.2021.00018

Xun Chang, Li Shen, Qiong Wang

{"title":"Optimizing Stencil Codes with Exploiting Data Reuse","authors":"Xun Chang, Li Shen, Qiong Wang","doi":"10.1109/iceert53919.2021.00018","DOIUrl":"https://doi.org/10.1109/iceert53919.2021.00018","url":null,"abstract":"Stencil code is widely used in the field of scientific computing. Currently, researchers are focusing on performance optimization for stencil applications by data-level parallelism or thread-level parallelism. Using vector/SIMD instructions, which is commonly used to achieve data-level parallelism, could effectively improve the performance of computation with a large number of repetitive operations, but usually limited due to the access memory bandwidth, or data and control dependencies. The Scalable Vector Extension (SVE), which is Vector-Length Agnostic (VLA), as the new generation of ARM’s vector ISA, could make vectorization more flexible by ignoring the vector register length, and has replaced the older Neon SIMD technology. In this paper we design ARM SVE instructions to implement and optimize 2d5p, 2d9p, 3d7p, and 3d27p stencil codes that are all the most common types using some classical optimization strategies like loop unrolling or data reuse. Our experiments on ARM processors using different vector lengths from 128-bit to 2048-bit show that our program could obtain performance improvements of up to 2.88x over directly vectorized code, 8.91x compared to Neon, and 16.31x for scalar code. In addition, we provide a set of templates that could be flexibly configured when stencil codes change, which can help directly generate efficient ARM SVE instructions. This work will provide great convenience for optimizing other stencil codes.","PeriodicalId":278054,"journal":{"name":"2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122265015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0