• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.190-198
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• 2002

Full scale durability test in the laboratory is an essential of any fatigue life evaluation of components or structures of the automotive vehicle. Component testing is particularly important in today's highly competitive industries where the design to reduce weight and production costs must be balanced with the necessity to avoid expensive service failure. Generally, hydraulic road simulator is used to carry out the fatigue test and the vibration test. In this paper, the algorithm and software to realize the real road profile are developed. The operation software for simultaneously controlled multi-axial road simulator is developed and the input and output data are displayed window based PC controller in the real time. Futhermore, the software to generate the real road profile are developed. The validity of the software are verified by applying the belgian road, the city road, the highway, and the gravel road. The results of the above experiment show that the real road profiles are realized well after 10th iteration.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1275-1280
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• 2004

This paper presents the construction of an active suspension system of a one-wheel car model by using fuzzy reasoning. The car model is approximately described by a nonlinear two degrees freedom system subject to excitation from a road profile, and the active control force is constructed by actuating a pneumatic actuator, and the degradation of the performance due to the delay of the pneumatic actuator is improved by inserting a compensator. The fuzzy control is obtained by single input rule modules fuzzy reasoning, and the excitation from the road profile is estimated by using a disturbance observer. The experimental result shows that the proposed active suspension system much improves the performance in the vibration suppression of the car model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.413-418
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• 2005

Vibration test using the MAST(Multi Axial Simulation Table) is more reliable test than conventional testing process focused on one directional vibration test. The former test could be possible with a advanced control algorithm and hardware supports so that most of the operation is automatically conducted by MAST system itself except the input information that is derived from the measured data. That means the reliability of the vibration test is highly depended on the input profile than any other cases before. In this paper, the optimal algorithm based on energy method is introduced so that the best combination of candidated input PSD data could be constructed. The optimal algorithm renders time information so that the vibration fatigue test is completely possible for any measured signals one wants. The real road test is conducted in short intervals containing some rough roads and the candidated input PSD is obtained from the extra road in proving ground. The testing is targeted for the electronically operated door and seat.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.341-343
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• 1995

In this paper, an algorithm is presented to recogniz the driving direction of a vehicle and obstacles in front of it based on highway road image. The algorithm employs a neural network with 27 sub sets obtained from the road image as its input. The outputs include the direction of the vehicle movement and presence or absence of obstacles. The road image, obtained by a video camera, was digitized and processed by a personal computer equipped with an image processing board.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.8
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• pp.720-728
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• 2012

It is important for identification of noise and vibration problem of tire to consider influence of interaction between road and tire. A quantification of road noise is a challenging issue in vehicle NVH due to extremely complicated transfer paths of road noise as well as the difficulty in an experimental identification of input force from tire-road interaction. A noise caused by tire is divided into road noise(structure-borne noise) and pattern noise(air-borne noise). Pattern noise is caused by pattern shape of tire, which has larger than 500 Hz, but road noise is generated by the interactions between a tire and a vehicle body. In this paper, we define the quantitative analysis for road noise caused by interactions between tire and road parameters. For the identification of road noise, the chassis dynamometer that is equipped $10mm{\times}10mm $ square cleat in the semi-anechoic chamber is used, and the tire spindle forces are measured by load cell. The vibro-acoustic transfer function between ear position and wheel center was measured by the vibro-acoustic reciprocity method. In this study three tires with different type of mechanical are used for the experiment work.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.612-616
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• 2007

This paper describes a procedure to improve road noise using DPDS estimation. We can estimate a body local stiffness at chassis mounting point where the path of road input vibration by DPDS with experiment and FE simulation. DPDS result from FE model has a good correlation with experiment data. FE model DPDS shows weak points among chassis mounting points. Body panel thickness and shape were changed to meet DPDS target. Improved DPDS of critical points makes a road noise level lower.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1098-1104
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• 2011

This paper presents the development of the surface profile sensing system (SPSS) and its application to analysis of road surface. The SPSS which can robustly reconstruct the road input profiles from the intermixed data with the vehicle's dynamic motion, is implemented using the multi-sensor system with the optimally shaped transfer function. The performance of this system is evaluated by a series of experimental works in the devised simulator. And a real car test equipped with the proposed system is performed in the proving ground over both deterministic and random road surfaces. Finally, a feasibility of the system is investigated considering the road model.