利用机器学习的方法,基于拉伸系统数据的物体识别

I. Kolysnychenko, V. Tkachov
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After conducting a study of the data on the passage of railway carriages and auto couplings through single-platform scales, it was established that the types of wagons can be categorized by such characteristics as the ratio of the base of the wagon to the length of the wagon between the auto couplings, the axle, weight. To obtain the ratio of the wagon base to the length of the wagon between autocouplings, it is necessary to perform data segmentation and clustering as follows - the wagon base is found as the distance between the middle of two bogies, and the length of the wagon between autocouplings as the middle of the distance between the bogies to the middle of the autocoupling. Practical implications. 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摘要

目标。为了提高张力测量系统的工作质量,将应变测量系统获得的数据以运动物体通道图的形式使用,有必要对应变测量系统的数据进行研究并开发一套算法。有了它,就有可能获得轨道车辆的特性,并以最小的误差对铁路物体进行识别,从而进一步利用所获得的结果,通过张力测量系统构建运动物体的识别和称重系统。研究方法。为了构建通过运动中的单平台铁路尺度识别不同类型运动物体的系统,提出使用机器学习方法,即神经网络和聚类算法。作为科学研究的一部分实现的软件是用Python编程语言编写的,使用numPy、sklearn、statistics和其他库。发现。等机器学习方法使用卷积神经网络,集群、感知器和依靠铁路对象的参考数据,可以使用在乌克兰境内,一些算法获得的解决方案和软件的形式实现,确定汽车的类型的这种特征如购物车的轴,轴的马车,马车的基础比车的长度之间的耦合,车轴的重量。使用特定张力测量系统的重量系数,在刻度校准期间,获得了汽车重量与其类型和每个轴的质量的依赖关系。的创意。通过对铁路车厢和汽车联轴器通过单台秤的数据进行研究,确立了货车的类型可以通过车底与汽车联轴器之间的车长之比、车轴、重量等特征进行分类。为了得到车基与自动联轴器之间的车长之比,需要进行如下的数据分割和聚类——车基为两个转向架中间的距离,自动联轴器之间的车长为转向架与自动联轴器中间距离的中间。实际意义。利用卷积神经网络、聚类、感知机等机器学习方法,得到应变计系统数据处理的算法解决方案,提高了对货车识别的准确性,同时降低了结果对货车速度的依赖,增加了企业称重系统的容量
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of objects based on the data of tenzometrical systems with using methods of machine learning
Objective. To increase the quality of work of tensometric systems, using data obtained from the strain gauge system in the form of maps of the passage of moving objects, it is necessary to conduct research on the data of the strain gauge system and develop a set of algorithms, thanks to which it is possible to obtain the characteristics of railway cars in rolling stock and carry out identification of railway objects objects with a minimum error for further use of the obtained results in the construction of a system of identification and weighing of moving objects through tensometric systems. Research methods. To build a system for identifying different types of moving objects through single-platform railway scales in motion, it is proposed to use machine learning methods, namely neural networks and clustering algorithms. The software implemented as part of scientific research is written in the Python programming language using numPy, sklearn, statistics, and other libraries. Findings. Using such methods of machine learning as convolutional neural networks, clustering, perceptron and relying on the reference data of railway objects that can be used on the territory of Ukraine, a number of algorithmic solutions were obtained and implemented in the form of software, which identify the type of car by such characteristics such as the axle of the cart, the axle of the wagon, the ratio of the base of the wagon to the length of the wagon between the couplings, the weight of the axles. Using the weight coefitient for a specific tensometric system, during the calibration of the scales, the dependence of the weight of the car on its type and the mass of each of the axles was obtained. The originality. After conducting a study of the data on the passage of railway carriages and auto couplings through single-platform scales, it was established that the types of wagons can be categorized by such characteristics as the ratio of the base of the wagon to the length of the wagon between the auto couplings, the axle, weight. To obtain the ratio of the wagon base to the length of the wagon between autocouplings, it is necessary to perform data segmentation and clustering as follows - the wagon base is found as the distance between the middle of two bogies, and the length of the wagon between autocouplings as the middle of the distance between the bogies to the middle of the autocoupling. Practical implications. Using such methods of machine learning as convolutional neural networks, clustering, perceptron and others, an algorithmic solution for data processing of strain gauge systems was obtained, which allows to increase the accuracy of the identification of wagons, while reducing the dependence of the results on the speed of the wagons, which allows to increase the capacity of weighing systems of enterprises
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