• Journal of the Korean Society for Railway
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• v.6 no.3
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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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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• v.11 no.2
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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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• v.18 no.3E
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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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.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1243-1251
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• 2011

This paper proposes a new vehicle model recognition using scale invariant feature transform to car's headlights image. Proposed vehicle model recognition raises the accuracy using "homogeneity" calculated from the distribution of features. In the experiment with 400 test images taken from 54 different vehicles, proposed method has 90% recognition rate and 16.45 homogeneity.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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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.

• MALSORI
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• no.48
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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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• v.19 no.3
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• pp.345-358
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• 2012

In a parametric sample selection model, the distribution assumption is critical to obtain consistent estimates. Conventionally, the normality assumption has been adopted for both error terms in selection and main equations of the model. The normality assumption, however, may excessively restrict the true underlying distribution of the model. This study introduces the $S_U$-normal distribution into the error distribution of a sample selection model. The $S_U$-normal distribution can accommodate a wide range of skewness and kurtosis compared to the normal distribution. It also includes the normal distribution as a limiting distribution. Moreover, the $S_U$-normal distribution can be easily extended to multivariate dimensions. We provide the log-likelihood function and expected value formula based on a bivariate $S_U$-normal distribution in a sample selection model. The results of simulations indicate the $S_U$-normal model outperforms the normal model for the consistency of estimators. As an empirical application, we provide the sample selection model for car ownership and a car expense relationship.