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For a driving vehicle, a self-excited vibration of a pair of steerable wheels about their steering axis accompanied by tramp is called shimmy. Shimmy is caused by the coupling effects of the complicated actions of wheel and tire and the tramp motion of front wheel axle. Because front axle is no longer used on passenger cars shimmy occurring is not considerable. But in commercial vehicles using front wheel axle suspension system shimmy should be considered in design process.<br/> In this paper, the model closed to a practical vehicle was developed to analyze the shimmy of a commercial vehicle, and the effects of various design parameters to shimmy were observed by dynamic simulation with multibody dynamics program, DADS. The validity of developed model and analysis results were verified by practical vehicle experiments.
An analysis of handling characteristics of a vehicle is performed for step and pulse steering input, which may be very useful in suspension design stage. Many developed computer programs for vehicle dynamics require test data of compliance effects for proto type car. Therefore, these programs are not suitable for automobile development stage. Using the raw design data of suspension and steering system, we analyze the vehicle behavior for step and pulse steering input with commercial multibody dynamics program, ADAMS. Simulated results are in good agreement with vehicle test results. Vehicle handling characteristic parameters which are very useful in automobile suspension design are evaluated from the analysis.<br/>
Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, R2 values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.