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Multiple optimized support vector regression for multi-sensor data fusion of weigh-in-motion system
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2020-06-07 , DOI: 10.1177/0954407020918802
Xiaofeng Liu 1 , Zhimin Feng 1 , Yuehua Chen 1 , Hongwei Li 2
Affiliation  

Weigh-in-motion is an efficient way to manage overload vehicles, and usually utilizes multi-sensor to measure vehicle weight at present. To increase generalization and accuracy of support vector regression (SVR) applied in multi-sensor weigh-in-motion data fusion, three improved algorithms are presented in this paper. The first improved algorithm divides train samples into two sets to construct SVR1 and SVR2, respectively, and then test samples are distributed to SVR1 or SVR2 based on the nearest distance principle. The second improved algorithm calculates the theoretical biases of two training samples closeted to one test sample, and then obtains the bias of the test sample by linear interpolation method. The third improved algorithm utilizes the second improved algorithm to realize adaptive adjustment of biases for SVR1 and SVR2. Five vehicles were selected to conduct multi-sensor weigh-in-motion experiments on the built test platform. According to the obtained experiment data, fusion tests of SVR and three improved algorithms are performed, respectively. The results show that three improved algorithms gradually increase accuracy of SVR with fast operation speed, and the third improved algorithm exhibits the best application prospect in multi-sensor weigh-in-motion data fusion.

中文翻译:

动态称重系统多传感器数据融合的多重优化支持向量回归

动态称重是管理超载车辆的有效方法,目前通常采用多传感器测量车辆重量。为了提高支持向量回归(SVR)在多传感器动态称重数据融合中的泛化性和准确性,本文提出了三种改进算法。第一种改进算法将训练样本分成两组分别构造SVR1和SVR2,然后根据最近距离原则将测试样本分配到SVR1或SVR2。第二种改进算法计算与一个测试样本接近的两个训练样本的理论偏差,然后通过线性插值的方法得到测试样本的偏差。第三改进算法利用第二改进算法实现对SVR1和SVR2的偏差自适应调整。选取五辆车在搭建的测试平台上进行多传感器动态称重实验。根据得到的实验数据,分别对SVR和三种改进算法进行融合测试。结果表明,3种改进算法以较快的运算速度逐渐提高了SVR的精度,其中第3种改进算法在多传感器动态称重数据融合中表现出最佳应用前景。
更新日期:2020-06-07
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