The gear has a wide range of transmitted force as various gear ratios are possible using a combination of teeth. It can transmit power reliably and cause relatively little vibration and noise. For this reason, the application of reducers of electric vehicles is being expanded. Vibration noise generated from gears is propagated into the quiet interior of a vehicle, causing various claims. In most gear studies, transmission error has been pointed out as the main cause of vibration noise of gears. Transmission errors have various causes, including design factors, manufacturing factors, and assembly factors. In general, when predicting transmission error through finite element analysis, design factors play an important role without considering manufacturing factors or assembly factors. In this study, relationships among important design variables (gear module, compensation rate, load torque, and transmission error) in gear design were investigated using analytical and experimental methods. In addition, a method of predicting gear meshing stiffness through the predicted gear transmission error was proposed to obtain variation of meshing stiffness due to changes of gear design parameters.

In general, it is noted that the time domain technique becomes difficult to predict with the use of the accurate fatigue life, due to the lack of dynamic information of the structure. When the multi-axial stress is generated by the random vibration excitation in the mechanical structure, the fatigue analysis should have performed in the frequency domain as based on the multi-axial PSDs due to the problems presented above. Notably, Premont proposed a method to calculate the equivalent stress using PSDs in the frequency domain. In calculating the equivalent stress PSD, the phase difference between the multi-axial stress components was not considered at that time. This study propose a frequency domain fatigue analysis technique which can calculate the equivalent stress from the multi-axial PSD, as it works considering the phase difference that can appear in the real vibration excited structure. To verify this method, the conventional time-domain method as similar to a multi-axial rainflow method, is compared with the proposed frequency domain method in a simple simulation model. The multi-axial PSD and finally the von Mises stress model is reviewed, according to whether the phase difference between the multi-axial stress components is considered or not is analyzed.

This paper contributes to development of a new method for detecting rear-side vehicles and estimating the positions for blind spot region or providing the lane change information by using vision systems. Because the real image acquired during car driving has a lot of information including the target vehicle and background image as well as the noises such as lighting and shading, it is hard to extract only the target vehicle against the background image with satisfied robustness. In this paper, the target vehicle has been detected by repetitive image processing such as sobel and morphological operations and a Kalman filter has been also designed to cancel the background image and prevent the misreading of the target image. The proposed method can get faster image processing and more robustness rather than the previous researches. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.

This study proposes a map-based control method to improve a vehicle’s lateral stability, and the performance of the proposed method is compared with that of the conventional model-referenced control method. Model-referenced control uses the sliding mode method to determine the compensated yaw moment; in contrast, the proposed map-based control uses the compensated yaw moment map acquired by vehicle stability analysis. The vehicle stability region is calculated by a topological method based on the trajectory reversal method. The performances of modelreferenced control and map-based control are compared under various road conditions and driving inputs. Model-referenced control uses a control input to satisfy the linear reference model, and it generates unnecessary tire lateral forces that may lead to worse performance than an uncontrolled vehicle with step steering input on a road with low friction coefficient. The simulation results show that map-based control provides better stability than model-referenced control.