高速公路行驶车辆信息测量方法的研究
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摘要
公路作为一种人造结构物,其承受重复荷载的能力和次数(使用寿命)是有一定限量的。随着我国经济的发展,和现代化高速运输工具的兴起,现代公路路面也不断呈现新的状况,面对这些新情况,特别是重载超限车辆对公路路面的破坏性影响,旧的道路设计、管理规范需要修正和更新。并且,交通荷载基础数据作为战略储备需求的反映,只有具备了长期而准确的测量信息,决策者才能够以此为依据做出符合实际情况的决策,所以相关的路面信息测试和研究更显重要。而此研究的重点,就在于对路面动态车辆信息的测量。
本文研究了一种以类神经网络机器算法支持向量机的扩展应用——最小二乘支持向量机为算法基础,DSP(TMS320F2812)和简单应变式传感器为硬件支持的路面行驶车辆动态信息测量方法。论文首先介绍了动态车辆信息测量的意义,技术重点和研究现状,描述了基于应变传感器的测试系统的工作原理。接着介绍了用以进行车辆分类的支持向量分类机和用以进行车辆动态称重的回归机算法的原理。详细研究了针对该测试系统的基于知识和经验的数据特征提取方法,建立了包含参数预测,验证,传感器融合定量分析等算法的车辆分类识别和回归称重的软件系统。最后在DSP(TMS320F2812)平台上实现了算法系统,结合相关数据进行了相关实验,论证结果。
As man-made structures, Highway’s load enduring capacity and durability (useful life) are certainly finite. With China's economic development, and the construction of modern transport, nowadays, more and more new situations of road surface state has been presented. In the face of these new situations, especially the devastating effects on road surface brought by overrunning heavy-duty vehicles, old road design and management need to be amended and the norms need to be updated. In addition, the traffic load based on data is a reflection of the demand for strategic reserves, so only with a long-term scaled, accurate data and information being evidence, decision-makers will be able to give out the correct decision-making according with the actual situation, then road information tests and researches look more important. And the focus of this study lies on the information measurement for dynamic vehicle on road surface.
In this paper, a DSP (TMS320F2812) and simple strain sensors hardware supporting information measurement method for dynamic vehicle which utilizes the Least Squares Support Vector Machine algorithm that it is an extension of Support Vector Machine being similar with neural network was researched. At first, the paper introduces the significance, technology focus and research actuality of information measurement for dynamic vehicle, describes the working principle of testing system based on strain sensors. Then the paper introduces the elements of vehicles classification using Support Vector Classification algorithm and dynamic weighing system which uses Support Vector Regression algorithm. After that, the paper studies in detail for the data feature extraction methods based on human knowledge and experience, and constructs a software system for vehicle classification and regressive dynamic weighing which contains parameters establishing prediction, verification, quantitative analysis by sensors fusion. Finally, we use DSP (TMS320F2812) platform to achieve the algorithm system, run experiments with the related data, and verify the results.
本文研究了一种以类神经网络机器算法支持向量机的扩展应用——最小二乘支持向量机为算法基础,DSP(TMS320F2812)和简单应变式传感器为硬件支持的路面行驶车辆动态信息测量方法。论文首先介绍了动态车辆信息测量的意义,技术重点和研究现状,描述了基于应变传感器的测试系统的工作原理。接着介绍了用以进行车辆分类的支持向量分类机和用以进行车辆动态称重的回归机算法的原理。详细研究了针对该测试系统的基于知识和经验的数据特征提取方法,建立了包含参数预测,验证,传感器融合定量分析等算法的车辆分类识别和回归称重的软件系统。最后在DSP(TMS320F2812)平台上实现了算法系统,结合相关数据进行了相关实验,论证结果。
As man-made structures, Highway’s load enduring capacity and durability (useful life) are certainly finite. With China's economic development, and the construction of modern transport, nowadays, more and more new situations of road surface state has been presented. In the face of these new situations, especially the devastating effects on road surface brought by overrunning heavy-duty vehicles, old road design and management need to be amended and the norms need to be updated. In addition, the traffic load based on data is a reflection of the demand for strategic reserves, so only with a long-term scaled, accurate data and information being evidence, decision-makers will be able to give out the correct decision-making according with the actual situation, then road information tests and researches look more important. And the focus of this study lies on the information measurement for dynamic vehicle on road surface.
In this paper, a DSP (TMS320F2812) and simple strain sensors hardware supporting information measurement method for dynamic vehicle which utilizes the Least Squares Support Vector Machine algorithm that it is an extension of Support Vector Machine being similar with neural network was researched. At first, the paper introduces the significance, technology focus and research actuality of information measurement for dynamic vehicle, describes the working principle of testing system based on strain sensors. Then the paper introduces the elements of vehicles classification using Support Vector Classification algorithm and dynamic weighing system which uses Support Vector Regression algorithm. After that, the paper studies in detail for the data feature extraction methods based on human knowledge and experience, and constructs a software system for vehicle classification and regressive dynamic weighing which contains parameters establishing prediction, verification, quantitative analysis by sensors fusion. Finally, we use DSP (TMS320F2812) platform to achieve the algorithm system, run experiments with the related data, and verify the results.
引文
1张日丽.二级公路重交通水泥砼路面破坏原因探析.科学之友. 2008, 10(29):47~50
2吕焕祥.一种新型公路收费站行驶车辆检测器.浙江交通职业技术学院学报. 2003, 1(1): 48~51
3张铁壁.多传感器数据融合技术在智能应变检测中的应用[J].河北工程技术高等专科学报, 2003, 02(2):26~29
4王宜举,修乃华.非线性规划理论与算法.北京科学出版社. 2002: 35~37
5 Nash S G, Sofer A. Linear and Nonlinear Programming. McGraw-Hill Companies Inc. USA. 1996:361~369
6 Cristianini Nello, Shawe-Taylor John.支持向量机导论.电子工业出版社. 2004:213~220
7 R. Fletcher. Practical Methods of Optimization. John Wiley and Sons: Chichester and New York. 1987, 4(9):1313~1320
8 SUYKENS J A K. Least squares support vector machine classifiers [J]. Neural Process Letter. 1999, 9(3) :293~299.
9杨奎河,单甘霖,赵玲玲.最小二乘支持向量机在故障诊断中的应用.计算机科学. 2007, 134 (11): 289~291
10阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究[J].控制与决策. 2003, 18(3):358~360
11张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策. 2005, 20(6): 621-624
12李烨,蔡云泽,尹汝泼,许晓鸣.基于证据理论的多类分类支持向量机集成.计算机研究与发展. 2008, 45 (4): 571~578
13吴德会.适应智能质量控制的多分类支持向量机.信息与控制. 2007, 36(2):118~198
14 Lendasse A, Simon G, Wertz V, et al. Fast Boot strap for Least Square Support Vector Machines. Proceedings of European Symposium on Artificial Neural Networks, 2004:525~530
15 V. Cherkassky, Y. Ma. Selecting of the Loss Function for Robust Linear Regression.Neural computation, 2002:1075-1089
16 Lin Chunfu, Wang Shengde. Training Algorithm for Fuzzy Support Vector Machines with Noisy Data [J]. Pattern Recognition Letters. 2004, 25(14): 1647~1656.
17 F. E. H. Tay, L Shen. Fault diagnosis based on rough set theory [J]. Engineering Application of Artificial Intelligence, 2003,16(1):39~43
18 Caruana.R. Multitask learning. Machine Learning, 2(28):41~75
19肖健华.基于支持对象的野点检测方法.计算机工程. 2003, 29(11):43~45
20 Mark Last,Abraham Kandel. Automated Detection of Outliers in Real-World Data. Department of Information Systems Engineering. 2007, 7(4):17~21
21 Edwin M.Knorr, et al. Outliers and data mining Finding exceptions in data. The Faculty of Graduate Studio, 2002:64~75
22 Dietterich T.G, Bakiri G. Solving multi-class learning problems error-correcting output codes. Journal of Artifical Intelligent Research, 1995:263~286
23虞和济,陈长征,张省,等.基于神经网络的智能诊断.北京冶金工业出版社, 2002:63~66
24 Hsu C. W, Lin CJ. A Comparison of Methods for Multiclass Support Vector Support Vector Machines. 2002, 13 (2):415~425
25邓乃扬,田英杰.数据挖掘中的新方法:支持向量机.北京科技出版社, 2004:
278~291
26 Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research. 2003, 12(1):149~198
27 J.Weston, S. Mukherjee ,Chapelle, M. Pontil,T. Poggio, V. Vapnik. Feature Selection for SVMs. Machine Learning. 2006, 8(4):47~52
28宋海鹰,桂卫华,阳春华.模糊偏最小二乘支持向量机的应用研究.系统仿真学报. 2008, 20(5):1344~1352
29王定成,姜斌.在线稀疏最小二乘支持向量机回归的研究.控制与决策. 2007, 22(2):132~137
30 Vapnik.V. The nature of statistical learning theory. New York:Springer, 2004:216-218
31 Duan, K., Keerthi, S. & Poo, A . Evaluation of simple performance measures for tuning SVM hyperparameters. Control Division Technical Report. 2001,01(11):86~90
32甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版). 2007, 41(2):245~248
33张浩然,韩正之,李昌刚.基于支持向量机的非线性系统辨识[J].系统仿真学报.2003, 15(1):119~121
34翟翌立,戴逸松.多传感器数据自适应加权融合估计算法的研究.计量学报. 1998, 19(1):69~74
35张舟锁,李凌均,何正嘉.基于支持向量机的多故障分类器及应用.机械科学与技术. 2004, 23(5):536~601
36翟翌立等.基于总均方误差最小条件下的多传感器最优数据融合算法.吉林工学院学报. 1996, 7(17):82～84
37 Wold S, Kettaneh Wold N, Skagerberg B. Nonlinear PLS modeling partial least squares projection to latent structures [J]. Chemometrics and Intelligent Laboratory Systems. 1989, 7(12):53~65
38 Ma Junshui, Theiler James, Perkins Simon. Accurate on-line support vector regression [J]. Neural Computation. 2003,15:2683-2703
39 SU YKENS J.A.K. Weighted least squares support vector machines. Robustness and sparse approximation [J]. 2002, 48 (1):85~105
40 Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM parameters and noise estimation for SVM regression. Department of Electrical and Computer Engineering, 2008:54-55
41 Tony Jebara. Multi-Task Feature and Kernel Selection for SVMs. Computer Science Department, 2005:10~27
42 Down T, Gates K.E, Masters A. Exact simplification of support vector solutions. J of Machine Learning Research. 2001 ,2(1):293~297.
43 Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification. National Taiwan University. 2008, 5(21):6~7
44 Cortes C, Vapnik V. Support Vector Network. Machine Learning. 1995, 20(1):273~297
2吕焕祥.一种新型公路收费站行驶车辆检测器.浙江交通职业技术学院学报. 2003, 1(1): 48~51
3张铁壁.多传感器数据融合技术在智能应变检测中的应用[J].河北工程技术高等专科学报, 2003, 02(2):26~29
4王宜举,修乃华.非线性规划理论与算法.北京科学出版社. 2002: 35~37
5 Nash S G, Sofer A. Linear and Nonlinear Programming. McGraw-Hill Companies Inc. USA. 1996:361~369
6 Cristianini Nello, Shawe-Taylor John.支持向量机导论.电子工业出版社. 2004:213~220
7 R. Fletcher. Practical Methods of Optimization. John Wiley and Sons: Chichester and New York. 1987, 4(9):1313~1320
8 SUYKENS J A K. Least squares support vector machine classifiers [J]. Neural Process Letter. 1999, 9(3) :293~299.
9杨奎河,单甘霖,赵玲玲.最小二乘支持向量机在故障诊断中的应用.计算机科学. 2007, 134 (11): 289~291
10阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究[J].控制与决策. 2003, 18(3):358~360
11张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策. 2005, 20(6): 621-624
12李烨,蔡云泽,尹汝泼,许晓鸣.基于证据理论的多类分类支持向量机集成.计算机研究与发展. 2008, 45 (4): 571~578
13吴德会.适应智能质量控制的多分类支持向量机.信息与控制. 2007, 36(2):118~198
14 Lendasse A, Simon G, Wertz V, et al. Fast Boot strap for Least Square Support Vector Machines. Proceedings of European Symposium on Artificial Neural Networks, 2004:525~530
15 V. Cherkassky, Y. Ma. Selecting of the Loss Function for Robust Linear Regression.Neural computation, 2002:1075-1089
16 Lin Chunfu, Wang Shengde. Training Algorithm for Fuzzy Support Vector Machines with Noisy Data [J]. Pattern Recognition Letters. 2004, 25(14): 1647~1656.
17 F. E. H. Tay, L Shen. Fault diagnosis based on rough set theory [J]. Engineering Application of Artificial Intelligence, 2003,16(1):39~43
18 Caruana.R. Multitask learning. Machine Learning, 2(28):41~75
19肖健华.基于支持对象的野点检测方法.计算机工程. 2003, 29(11):43~45
20 Mark Last,Abraham Kandel. Automated Detection of Outliers in Real-World Data. Department of Information Systems Engineering. 2007, 7(4):17~21
21 Edwin M.Knorr, et al. Outliers and data mining Finding exceptions in data. The Faculty of Graduate Studio, 2002:64~75
22 Dietterich T.G, Bakiri G. Solving multi-class learning problems error-correcting output codes. Journal of Artifical Intelligent Research, 1995:263~286
23虞和济,陈长征,张省,等.基于神经网络的智能诊断.北京冶金工业出版社, 2002:63~66
24 Hsu C. W, Lin CJ. A Comparison of Methods for Multiclass Support Vector Support Vector Machines. 2002, 13 (2):415~425
25邓乃扬,田英杰.数据挖掘中的新方法:支持向量机.北京科技出版社, 2004:
278~291
26 Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research. 2003, 12(1):149~198
27 J.Weston, S. Mukherjee ,Chapelle, M. Pontil,T. Poggio, V. Vapnik. Feature Selection for SVMs. Machine Learning. 2006, 8(4):47~52
28宋海鹰,桂卫华,阳春华.模糊偏最小二乘支持向量机的应用研究.系统仿真学报. 2008, 20(5):1344~1352
29王定成,姜斌.在线稀疏最小二乘支持向量机回归的研究.控制与决策. 2007, 22(2):132~137
30 Vapnik.V. The nature of statistical learning theory. New York:Springer, 2004:216-218
31 Duan, K., Keerthi, S. & Poo, A . Evaluation of simple performance measures for tuning SVM hyperparameters. Control Division Technical Report. 2001,01(11):86~90
32甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版). 2007, 41(2):245~248
33张浩然,韩正之,李昌刚.基于支持向量机的非线性系统辨识[J].系统仿真学报.2003, 15(1):119~121
34翟翌立,戴逸松.多传感器数据自适应加权融合估计算法的研究.计量学报. 1998, 19(1):69~74
35张舟锁,李凌均,何正嘉.基于支持向量机的多故障分类器及应用.机械科学与技术. 2004, 23(5):536~601
36翟翌立等.基于总均方误差最小条件下的多传感器最优数据融合算法.吉林工学院学报. 1996, 7(17):82～84
37 Wold S, Kettaneh Wold N, Skagerberg B. Nonlinear PLS modeling partial least squares projection to latent structures [J]. Chemometrics and Intelligent Laboratory Systems. 1989, 7(12):53~65
38 Ma Junshui, Theiler James, Perkins Simon. Accurate on-line support vector regression [J]. Neural Computation. 2003,15:2683-2703
39 SU YKENS J.A.K. Weighted least squares support vector machines. Robustness and sparse approximation [J]. 2002, 48 (1):85~105
40 Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM parameters and noise estimation for SVM regression. Department of Electrical and Computer Engineering, 2008:54-55
41 Tony Jebara. Multi-Task Feature and Kernel Selection for SVMs. Computer Science Department, 2005:10~27
42 Down T, Gates K.E, Masters A. Exact simplification of support vector solutions. J of Machine Learning Research. 2001 ,2(1):293~297.
43 Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification. National Taiwan University. 2008, 5(21):6~7
44 Cortes C, Vapnik V. Support Vector Network. Machine Learning. 1995, 20(1):273~297