PARAMETER CLASSIFICATION SOFTWARE BASED ON CHARACTERIZERS AND KNOWLEDGE BASE FOR ELECTRONIC ENGINE CONTROL UNIT

S. Kuznichenko, T. Tereshchenko, I. Buchynska, Viktoriia Klepatska
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Abstract

The article discusses the issues of increasing the efficiency of the classification process of cards of electronic control units of a car engine. The analysis of the existing software for editing calibration tables in electronic engine control unit, which has tools for determining calibrations and data recognition, was carried out. The limits of use of such software products are conditioned by a small number of specified classes of calibration tables and low data processing speed. The analysis of testing results of classification methods using spectral decomposition demonstrated that a system based on this method requires complex transformations of the results of spectral decomposition. The use of spectral decomposition as a solution of the classification problem is possible if some characteristics of the input data are determined and used as data for classification. It was developed a data classification algorithm that uses characterizers to compute a clearly identified characteristic of the input matrix. The software package for the implementation of the developed algorithm was carried out by using the .NET Framework and the C # programming language. The testing of the classification system performance performed by using the developed software system on a small sample of maps. The results of preliminary testing showed that the system determines correctly the class of the provided card after training. Further testing on the Mercedes-Benz Bosch EDC16C31 / EDC16CP31 car block family showed that in cases of a large number of training images, the result meets the requirements. The performed tests allowed us to determine the optimal number of images for training and the time required for this.
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基于特征和知识库的发动机电控单元参数分类软件
本文讨论了提高汽车发动机电控单元卡片分类效率的问题。对现有的发动机电子控制单元标定表编辑软件进行了分析,该软件具有标定和数据识别工具。此类软件产品的使用局限于指定类别的校准表数量较少和数据处理速度较低。对光谱分解分类方法的测试结果分析表明,基于该方法的分类系统需要对光谱分解结果进行复杂的变换。如果确定输入数据的某些特征并将其用作分类数据,则可以使用光谱分解作为分类问题的解决方案。开发了一种数据分类算法,该算法使用特征符来计算输入矩阵的明确识别特征。采用。net框架和c#编程语言对所开发算法的软件包进行了实现。利用开发的软件系统在小样本地图上进行了分类系统的性能测试。初步测试结果表明,经过训练,系统能够正确判断所提供卡片的类别。在梅赛德斯-奔驰博世EDC16C31 / EDC16CP31车块家族上进一步测试表明,在训练图像数量较多的情况下,结果满足要求。执行的测试使我们能够确定用于训练的最佳图像数量和所需的时间。
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