The Gradient Method of Constructing an Image of Mapping for the Classification Problem of the Object Operation Modes

Yuri A. Dementiy, Kirill Nikolaev
{"title":"The Gradient Method of Constructing an Image of Mapping for the Classification Problem of the Object Operation Modes","authors":"Yuri A. Dementiy, Kirill Nikolaev","doi":"10.17213/0136-3360-2021-4-5-64-71","DOIUrl":null,"url":null,"abstract":"The paper describes the solution of the problem of discrimination (classification) of object operation modes by construction of areas corresponding to each of the considered modes types, with subsequent verifica-tion of falling points, defined by the observed states of the object, in one of the areas. As a source of infor-mation about an object, its simulation model and acceptable ranges of object parameters are used. The map-ping under study combines the simulation model function and the gauging function. All this makes it possible to turn the task of training the classifier to the task of finding the image of the mapping. The proposed approach to the solution of the problem is based on the maximization of the informativity of points, approximating the boundary of the area. The area of the required domain and the absolute deviation of the lengths of the edges forming the boundary of the domain from their average value serve as the informativity criterion. The proposed approach is applied to solve the problem of differentiating the operation modes of an object by constructing an image of the mapping and finding its boundary line. The results of the algorithm are analyzed, its advantages and disadvantages are presented. Obtained images of mappings can be used as the basis of a classifier of ob-ject operation modes. The optimization procedure in this case will be a training procedure for the classifier.","PeriodicalId":105792,"journal":{"name":"Известия высших учебных заведений. Электромеханика","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Известия высших учебных заведений. Электромеханика","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17213/0136-3360-2021-4-5-64-71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The paper describes the solution of the problem of discrimination (classification) of object operation modes by construction of areas corresponding to each of the considered modes types, with subsequent verifica-tion of falling points, defined by the observed states of the object, in one of the areas. As a source of infor-mation about an object, its simulation model and acceptable ranges of object parameters are used. The map-ping under study combines the simulation model function and the gauging function. All this makes it possible to turn the task of training the classifier to the task of finding the image of the mapping. The proposed approach to the solution of the problem is based on the maximization of the informativity of points, approximating the boundary of the area. The area of the required domain and the absolute deviation of the lengths of the edges forming the boundary of the domain from their average value serve as the informativity criterion. The proposed approach is applied to solve the problem of differentiating the operation modes of an object by constructing an image of the mapping and finding its boundary line. The results of the algorithm are analyzed, its advantages and disadvantages are presented. Obtained images of mappings can be used as the basis of a classifier of ob-ject operation modes. The optimization procedure in this case will be a training procedure for the classifier.
构造映射图像的梯度方法在目标运行模式分类中的应用
本文描述了通过构建与所考虑的每种模式类型对应的区域来解决物体运行模式的判别(分类)问题,并随后验证其中一个区域中由物体的观察状态定义的落点。作为一个对象的信息来源,它的仿真模型和对象参数的可接受范围被使用。所研究的测绘结合了仿真模型功能和测量功能。所有这些使得将训练分类器的任务转变为寻找映射图像的任务成为可能。该方法基于点的信息量最大化,逼近区域边界。所需区域的面积和构成区域边界的边的长度相对于它们的平均值的绝对偏差作为信息性准则。该方法通过构造映射图像并求其边界线来解决目标运行模式的判别问题。对算法的结果进行了分析,给出了算法的优缺点。获得的映射图像可以作为对象操作模式分类器的基础。在这种情况下,优化过程将是分类器的训练过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信