• 한국철도학회논문집
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• 제6권3호
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• pp.149-155
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• 2003

The development of railway vehicles involves the proper selection of design parameters not only to achieve high speed but also to reduce the vibration of the train. In this study an analytical model of a freight car is developed to find the critical speed. The freight car can generate the snake motion of the lateral and yawing motion of the car body, the bogie, and the wheelset. Numerical analysis for the nonlinear equation motions with 17 degrees of freedom showed the running stability and critical speed due to the snake motion. Also, the vibration modes of the freight car was calculated using ADAMS/RAIL, which showed that the critical speed have the yawing modes of the car body and the bogie. Finally this paper shows that the snake motion of the vehicle can be controlled with the modifications of the design parameters.

• 한국자동차공학회논문집
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• 제15권5호
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• pp.180-187
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• 2007

This paper presents a mathematical vehicle model that is designed to analyze the dynamic performance and to develop various safety control systems. Wheel slip controllers for ABS is also formulated to improve the vehicle response and to increase the safety on slippery road. Validation of the model and controller is performed by comparison with a commercial software package, CarSim. The result shows that performances of developed vehicle model are in good accordance with those of the CarSim on various driving conditions. Developed ABS controller is applied to the vehicle model and CarSim model, and it achieves good control performance. ABS controller improves lateral stability as well as longitudinal one when a vehicle is in turning maneuver on slippery road. A driver model is also designed to control steer angle of the vehicle model. It also shows good performance because the vehicle tracks the desired lane very well.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.95-102
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• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• The Journal of the Acoustical Society of Korea
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• 제18권3E호
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• pp.3-8
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• 1999

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In this approach, multiple model sets are made according to several noise masking levels and then a model set appropriate for the estimated noise level is selected automatically in recognition phase. According to speaker independent isolated word recognition experiments in car noise environments, the proposed method using model sets with only two masking levels reduced average word error rate by 60% in comparison with spectral subtraction method.

• 한국자동차공학회논문집
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• 제14권4호
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• pp.92-98
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• 2006

In this paper, a planar car model with 3 degrees of freedom was analyzed using the concept of the roll center. To avoid ambiguity, force components which require experimental data were excluded. Only kinematic approach was used to find the position and orientation of the vehicle body and the position of the roll center. The roll center was found by the pole with infinitesimal movement and Kennedy-Aronhold theorem. Centrodes, which are the loci of instantaneous centers of planar motion, were constructed with analyzed results to show characteristics of vehicle body motion. To verify the presented analysis method in this paper, the locus of the roll center and the motion of a 3 D.O.F. planar car model were compared with those of the 1 D.O.F. model.

• 한국멀티미디어학회논문지
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• 제14권10호
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• pp.1243-1251
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• 2011

본 논문에서는 차량의 헤드라이트 영상에 Scale Invariant Feature Transform(SIFT) 알고리즘을 적용하여 획득한 특징점을 이용하여 차량의 모델을 인식하는 차종 인식 방법을 제안한다. 보다 정확도 높은 차종 인식을 구현하기 위해서 특징점들의 분포로부터 동질성(homogeneity)을 계산하여 인식 정확성의 척도로 두었다. 제안한 방법의 성능을 평가하기 위해 국내 54종의 차량 영상으로부터 촬영된 400장의 실험 영상을 이용해 실험한 결과, 제안한 방법은 90%의 인식률과 16.45의 평균 동질성을 보였다.

• 한국생산제조학회지
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• 제21권3호
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• pp.368-375
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• 2012

Durability performance evaluation of automotive components is very important and time consuming task. In this paper, to reduce vehicle component development time and cost virtual testing simulation technology is used to evaluate durability performance of a passenger car front aluminum sub-frame. Multibody dynamics based vehicle model and virtual test simulation model of a half car road simulator are validated by comparisons between rig test results and simulation results. Durability life prediction of the sub-frame is carried out using the model with road load data of proving ground which can evaluate accelerated durability life. We found that the durability performance of the sub-frame is sufficient and it can be predicted within short time compared to rig test time.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.27-32
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• 2024

Recently, there has been a growing interest in integrating physical toys into video gaming within the game content business. This paper introduces a novel method that leverages low-cost camera as an alternative to using sensor attachments to meet this rising demand. We address the limitations associated with low-cost cameras and propose an optical design tailored to the specific environment of model car recognition. We overcome the inherent limitations of low-cost cameras by proposing an optical design specifically tailored for model car recognition. This approach primarily focuses on recognizing the underside of the car and addresses the challenges associated with this particular perspective. Our method employs a transfer learning model that is specifically trained for this task. We have achieved a 100% recognition rate, highlighting the importance of collecting data under various camera exposures. This paper serves as a valuable case study for incorporating low-cost cameras into vision systems.

• 대한음성학회지:말소리
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• 제48호
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• pp.141-153
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• 2003

Researches into robust recognition in noise environments, especially in car environments, are being carried out actively in speech community. In this paper we will report on three types of corpora that SiTEC (Speech Information TEchnology & industry promotion Center) has created for research into speech recognition in car noise environments. The first is the recordings of 900 Korean native speakers, distributed according to gender, age, and region, who uttered application words in car environments. The second is the collections of mixed noise in 3 car types by model while setting up various noise patterns which can be obtained with the car engine on or off, at different driving speed, and in different road conditions with windows open or closed. The third is the recordings of simulated speech by HATS (Head and Torso Simulator) in car environments with the internal and external noise factors added. These three types of recordings were all made through synchronized 8 channel microphones that are fixed in a car. The creation and applications of these corpora will be reported on in detail.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.345-358
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• 2012

표본선택 모형을 최우추정법으로 추정할 때 오차항의 분포를 제대로 가정하는 것이 매우 중요하다. 표본선택 모형의 선택 방정식과 본 방정식의 오차항 분포를 일반적으로 이변량 정규분포로 가정하지만, 이 가정이 오차항의 실제 분포를 과도하게 제약할 가능성이 있다. 본 연구는 표본선택 모형의 오차항 분포로 $S_U$-정규분포를 도입한다. $S_U$-정규분포는 분포의 비대칭성과 초과첨도를 허용한다는 측면에서 정규분포보다 훨씬 유연하면서, 동시에 정규분포를 극한분포의 형태로 포함하고 있다. 또한 정규분포처럼 다변량 분포함수가 존재하기 때문에 표본선택 모형과 같은 다변량 모형에서도 활용할 수 있다. 본 논문은 $S_U$-정규분포를 이용한 표본선택 모형에서 로그우도 함수와 조건부 기댓값을 도출하고, 시뮬레이션을 통해 정규분포 모형과 추정성과를 비교한다. 또한 자동차 보유 가구들의 자동차 유지비에 관한 실제 데이터를 이용하여 $S_U$-정규분포 표본선택 모형의 추정결과를 제시한다.