The Triple M of Organizations: Man, Management and Myth
What has management to do with myths? And how does gender enter the stage? This book identifies frequently used key arguments in gender discussions on management and organizations and will unmask them as myths. Be it that management is rational, be it tha
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DOI 10.1007/s12206-012-0812-x
Road profile estimation using wavelet neural network and 7-DOF vehicle dynamic systems†Ali Solhmirzaei1,*, Shahram Azadi2 and Reza Kazemi2 1
Technical and Engineering Department, Mapna Locomotive Company, Mapna Group, Tehran, Iran 2 Department of Mechanical Engineering, K. N. Toosi University, Tehran, Iran (Manuscript Received October 1, 2011; Revised March 18, 2012; Accepted May 2, 2012)
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Abstract Road roughness is a broad term that incorporates everything from potholes and cracks to the random deviations that exist in a profile. To build a roughness index, road irregularities need to be measured first. Existing methods of gauging the roughness are based either on visual inspections or using one of a limited number of instrumented vehicles that can take physical measurements of the road irregularities. This paper more specifically focuses on the estimation of a road profile (i.e., along the "wheel track"). This paper proposes a solution to the road profile estimation using a wavelet neural network (WNN) approach. The method incorporates a WNN which is trained using the data obtained from a 7-DOF vehicle dynamic model in the MATLAB Simulink software to approximate road profiles via the accelerations picked up from the vehicle. In this paper, a novel WNN, multi-input and multi-output feed forward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feed forward network. The training formulas based on BP algorithm are mathematically derived and a training algorithm is presented. The study investigates the estimation capability of wavelet neural networks through comparison between some estimated and real road profiles in the form of actual road roughness. Keywords: Road profile; Simulation; Wavelet neural network; BP algorithm; Estimation ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1. Introduction A road profile is one of the most effective vehicle environmental conditions that influences ride, handling, fatigue, fuel consumption, tire wear, maintenance costs, and vehicle delay costs. Therefore, establishment of methods for road profile measurement is completely essential. Currently, many routines are available for road profile measurement. Most of them measure vertical deviations of the road surface along the traveling wheel path. The American Society of Testing and Materials (ASTM) standard E867 [1] defines road roughness as the deviations of a pavement surface from a
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