• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.555-564
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• 2001

Wheel-rail noise is normally classified into three catagories ： rolling, squeal and impact noise. In this paper, rolling noise caused by the irregularity between a wheel and rail is analysed as follows： The irregularity between the wheel and rail is assumed as combination of sinusoidal profiles. Wheel-rail contact stiffness is linearized by using Hertzian contact theory, and then contact force between the wheel and rail is calculated. Vibration of the rail and wheel is calculated theoretically by receptance method or FEM depending on the geometry of wheel or rail for the frequency range of 100-5000Hz, important for noise generation. The radiation caused by those vibration is computed by BEM. To verify this analysis tools, rolling noise is calculated by preceding analysis steps using typical roughness data and it is compared with experimental rolling noise data. This analysis tools show reasonable results and used for the prediction of KTX rolling noise.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.623-628
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• 2009

There is an increasing demand for efficient high power/weight auxiliary power supplies for use on high speed traction application. Many new conversion techniques have been proposed to reduce the voltage and current stress of switching components, and the switching losses in the traditional pulse width modulation(PWM) converter. Especially, the phase shift full bridge zero voltage switching PWM techniques are thought most desirable for many applications because this topology permits all switching devices to operate under zero voltage switching(ZVS) by using circuit parasitic components such as leakage inductance of high frequency transformer and power device junction capacitance. The proposed topology is found to have higher efficiency than conventional soft-switching converter. Also it is easily applicable to phase shift full bridge converter by applying an energy recovery snubber consisted of fast recovery diodes and capacitors.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.321-324
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• 2008

Recently, there is an increasing demand for efficient high power/weight auxiliary power supplies for use on high speed traction application. many new conversion techniques have been proposed to reduce the voltage and current stress of switching components, and the switching losses in the traditional pulse width modulation(PWM) converter. Among them, the phase shift full bridge zero voltage switching PWM techniques are thought most desirable for many applications because this topology permits all switching devices to operate under zero voltage switching(ZVS) by using circuit parasitic components such as leakage inductance of high frequency transformer and power device junction capacitance. The proposed topology is found to have higher efficiency than conventional soft-switching converter. Also it is easily applicable to phase shift full bridge converter by applying an energy recovery snubber consisted of fast recovery diodes and capacitors.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.229-230
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• 2022

With the development of digital technology, construction companies at home and abroad are in the process of computerizing work and site information for the purpose of improving work efficiency. To this end, various technologies such as BIM, digital twin, and AI-based safety management have been developed, but the accuracy and completeness of the related technologies are insufficient to be applied to the field. In this paper, the learning data that has undergone a pre-processing process optimized for recognition of construction information based on structural members is trained on an existing artificial intelligence model to improve recognition accuracy and evaluate its effectiveness. The artificial intelligence model optimized for the structural member created through this study will be used as a base technology for the technology that needs to confirm the safety of the structure in the future.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.1-41.1
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• 2021

We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding magnetogram (or UV/EUV images) from 2011 to 2015 except for every June and December by the SDO (Solar Dynamic Observatory) satellite. We evaluate the model by comparing pairs of actual magnetogram (or UV/EUV images) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.10
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• pp.1443-1453
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• 2010

We simulated the roughing train of the rod mill of SEAH BESTEEL Inc. using finite element method to inhibit surface cracks initiation on workpiece. We designed 2nd pass (square roll) and applied to this roll in the roughing train of the rod mill. Also, we proposed new pass schedule, which changed roll gap of 3rd and 4th groove by using finite element method. We used shear damage model, which is dependent on shear stress ratio and compared the number of damaged elements on workpiece. A damaged element means surface crack. Consequently, after 2nd pass (square roll) is changed, the error rate decreased by 1.43% when compare to that of the old groove. And the number of damaged elements in the new pass schedule decreased by 37.6%, which is less than present pass schedule.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1311-1322
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• 2011

Gesture is applied in many applications such as smart-phone, tablet-PC, and web-browser since it is a fast and simple way to invoke commands. For gesture applications, a gesture designer needs to consider both user and system during designing gestures. In spite of development of gesture design tools, some difficulties for gesture design still remains as followings; first, a designer must design every gesture manually one by one, and, second, a designer must repeatedly train gestures. In this paper, we propose a gesture proposal system that automates gesture training and gesture generation to provide more simple gesture design environment. Using automation of gesture training, a designer does not need to manually train gestures. Proposed gesture proposal system would decrease difficulties of gesture design by suggesting gestures of high recognition possibility that are generated based on mahalanobis distance calculation among generated and pre-existing gestures.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.6
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• pp.659-664
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• 2015

The current railway wireless network uses various communication scheme depending on the section of railway. Therefore, in the train, it is required a specific communication device for the corresponding communication scheme to communicate and exchange data with the control center. To enhance data rate and support various services, LTE-R scheme has been developed for the next generation railway communications. To use both the existing communication and LTE-R schemes, it is required an integration method for encompassing all communication schemes. In this paper, we overview existing railway communications and LTE-R scheme. Then, we develop a network integration method which can be applied by using one terminal in the train. In addition, implementation issues for the integration are also considered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.1-7
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• 2016

Although the second driving device of KTX, which consists of the wheel and the axle reduction gears unit, is a mechanically integrated structure, its preventive maintenance (PM) requires two separate intervals due to the different technical requirements. In particular, these subsystems perform attaching and detaching work simultaneously according to the maintenance directive. Therefore, to reduce the unnecessary amount of PM and high logistic availability of the train, it is important to optimize PM with regard to reliability-centered maintenance toward a cost-effective solution. In this study, fault tree analysis and reliability of the subsystems, considering the criticality of the components, were performed using the data derived from field data in maintenance. The cost optimization of the PM was derived from a genetic algorithm considering the target reliability and improvement factor. The cost optimization was derived from a maximum of the fitness function of the individual in generation. The optimal TBO of them using the genetic algorithm was 2.85x106 km, which is reduced to approximately 21% compared to the conventional method.