• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.917-923
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• 2002

KITECH and ODS performed a study of internal and external noise prediction of the KHST test train. The object of this study was 3 kind of cars; trailer car(TT2), motorized car(TM1) and power car(TP1) and the predicted noise was calculated for the two different driving speeds in free field and tunnel conditions. Data of carbody design and noise sources were delivered from each manufactures. Some of noise sources which were not available in project team, were chosen by experiences of ODS. Internal noise level of each car were predicted for two cases i.e, at 300 km/h and 350 km/h. In addition sound transmission path and dominant noise sources were also investigated of each section of car, which is circular shell typed part of whole carbody. In case of TT2, the dominating sound transmission path is floor in terms or structure-borne noise and air-borne noise. The main noise sources are structure-borne noise from the yaw-damper and air-borne noise from the wheel/rail contact, whereas the dominating sound transmission path of TM1 are floor and sidewall below the window in terms of structure-borne noise. The main noise sources of TM1 are structure-borne noise from motor/gear unit and the yaw-damper in the free field, and air-borne noise from the wheel/rail contact and structure-borne noise from motor/gear unit in the tunnel. Through the external noise prediction for the KHST test train formation, the noise form the wheel/rail contact is estimated as one of the major sources. In addition, the noise specification of sub-component was proposed for managing each sub-surpplier to reach the KHST noise requirement. The specification provide the sound power of machinery part and transmission loss of component of carbody structure. The predicted noise level in each case exceeded the required limit. Through this study, the noise characteristics of the test train were investigated by simulation, and then the actual test will be performed in near future. Both measured and calculated data will be compared and further work for noise reduction will be continued.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.957-965
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• 2011

Recently, railway industry has been developed not only functional parts such as acceleration and high performance of the railway but also emotional parts such as improved ride comfort and blocking noise. However, some important components of railway such as wheel and rail always had exposed too much operation time, cyclic load and rolling contact directly. The variations of load, vibration and chemical compositions were caused of wheel and rail having a lot of different types of contact fatigue damages. Therefore, It is necessary to improve inspection and maintenance technology in order to ensure safety and reliability of railway. Many researchers have already been reported the technology. Magnetic camera, one of the non-destructive testing technique can be used to inspect and evaluate the changes of magnetic field in ferromagnetic and paramagnetic materials with cracks. When an electromagnetic is applied to a specimen, a magnetic field will be distorted around a crack on the specimen. In present paper, the distribution of magnetic property in wheel with cracks using magnetic camera had investigated. The crack can be detected and evaluated by distribution analysis of magnetic field. The magnetic camera technique can be detected and evaluated the crack by rolling contact fatigue.

• 한국컴퓨터정보학회논문지
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• 제25권3호
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• pp.19-26
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• 2020

일반적으로 품질 관리는 많은 제조 공정, 특히 주조 또는 용접과 관련된 공정의 기본 구성 요소가 된다. 그러나 사람이 일일이 수동으로 품질 관리 절차를 하는 것은 종종 시간이 걸리고 오류가 발생하기 쉽다. 최근 고품질 제품에 대한 요구를 만족시키기 위해 지능형 육안 검사 시스템의 사용이 생산 라인에서 필수적이 되고 있다. 본 논문에서는 이를 위해 딥 러닝 기반의 ShuffleDefectNet 결함 감지 시스템을 제안하고자 한다. 제안된 결함 검출 시스템은 NEU 데이터 세트의 결함 검출에 대한 여러 최신 성능들보다 높은 평균 정확도 99.75% 정도를 얻는다. 이 논문에서 여러 다른 트레이닝 데이터로부터 최상의 성능을 탐지하고 탐지 성능을 관찰하였다. 그 결과 ShuffleDefectNet의 전체 아키텍처를 사용할 때 정확성과 속도가 크게 향상됨을 알 수 있었다.

• 한국철도학회논문집
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• 제10권2호
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• pp.137-145
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• 2007

In traditional financial theory, the discount cash flow model(DCF or NPV) operates as the basic framework for most analyses. In doing valuation analysis, the conventional view is that the net present value(NPV) of a project is the measure of the present value of expected net cash flows. Thus, investing in a positive(negative) NPV project will increase(decrease) firm value. Recently, this framework has come under some fire for failing to consider the options of the managerial flexibilities. Real option valuation(ROV) considers the managerial flexibility to make ongoing decisions regarding the implementation of investment projects and the deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real assets based on the Black-Sholes option pricing model, the binomial option pricing model, and the Monte Carlo simulation. This paper uses those models to obtain point estimates of real option value with the G7- HSR350X(high-speed train).

• 한국철도학회:학술대회논문집
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• 한국철도학회 1998년도 창립기념 춘계학술대회 논문집
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• pp.23-30
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• 1998

The construction of the transportation history database system is to serve the scheduling and seat inventory controling. Recently, lots of countries have been faced with the advance era because of the new railway transportation system, like the high speed railway and/or magnetic levitation vehicle system. This can be reasonably translated as those of operators are willing to provide the more various and high quality schedule to the customer. Those operators' these ideas make possible to forecast that scheduling process is going to be complicated more and more The seat inventory control, so to speak Yield Management System(YMS), goes a long way to improve the total passenger revenue at the railway business. The YMS forecasts the number of the last reservation value(DCP# END) and recommends the optimal values on the seat sales. The history database system contains infra-data(ie, train, seat, sales) that will be the foundation of scheduling and seat inventory control application programs. The development of the application programs are reserved to the next step. The database system is installed on the pc platform(IBM compatible), using the DB2(RDBMS). And at next step, the platform and DBMS will be considered whether they can meet the users' requirement or not.

• Structural Engineering and Mechanics
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• 제82권1호
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• pp.31-40
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• 2022

In structural engineering, the material properties of the structures such as elastic modulus, shear modulus, density, and size may not be deterministic and may vary at different locations. The dynamic response analysis of such structures may need to consider these properties as stochastic. This paper introduces a stochastic finite element method (SFEM) approach to analyze moving loads problems. Firstly, Karhunen-Loéve expansion (KLE) is applied for expressing the stochastic field of material properties. Then the mathematical expression of the random field is substituted into the finite element model to formulate the corresponding random matrix. Finally, the statistical moment of the dynamic response is calculated by the point estimation method (PEM). The accuracy and efficiency of the dynamic response obtained from the KLE-PEM are demonstrated by the example of a moving load passing through a simply supported Euler-Bernoulli beam, in which the material properties (including elastic modulus and density) are considered as random fields. The results from the KLE-PEM are compared with those from the Monte Carlo simulation. The results demonstrate that the proposed method of KLE-PEM has high accuracy and efficiency. By using the proposed SFEM, the random vertical deflection of a high-speed railway (HSR) bridge is analyzed by considering the random fields of material properties under the moving load of a train.

• ETRI Journal
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• 제44권6호
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• 드라이브 ㆍ 컨트롤
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• 제12권3호
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• pp.18-24
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• 2015

The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.

• 한국지반공학회논문집
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• 제31권9호
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• pp.29-38
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• 2015

이 연구에서는 열차가 교량 접속부를 통과하면서 발생되는 동적상호작용 문제를 유한요소해석프로그램을 이용하여 검토하였다. 해석대상구간에서의 궤도틀림조건은 부등침하량을 삼각함수를 이용하여 이상화하고, 철도공사 선로 보수기준을 만족하는 최대 궤도틀림 한계치를 해석조건으로 설정하였다. 해석대상이 되는 접속부 구조물의 체결장치 아래 부분은 모두 3차원 입체요소로 모형화하였으며, 해석비용을 줄이기 위하여 1/2 궤도부분만을 모형화하였다. 경계조건은 평면변형률상태에 준하는 것으로 차량-궤도 동적상호작용해석을 수행하였다. 국내 콘크리트궤도용 표준 접속부에서의 궤도틀림에 따른 열차 주행시 동적상호작용해석을 수행하여 윤중감소율과 차체수직진동가속도를 살펴보았으며, 접속부의 시멘트 안정처리골재, 일반골재, 일반토사구간에서의 발생된 최대 Mises응력으로 동적거동을 평가하였다.

• 한국산학기술학회논문지
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• 제18권1호
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• pp.609-617
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• 2017

철도는 전 세계적으로 매우 안전하고 중요한 운송 수단 중 하나이다. 그러나 철도시스템이 복잡도가 높아지고 주행거리 증가 등으로 인해 매년 사고가 지속적으로 발생하고 있다. 특히, 고속열차와 화물열차의 경우, 차축베어링 비정상 과열로 인하여 차축베어링의 기능이 소실되면 차축의 불균등한 하중을 초래한다. 따라서, 차축베어링의 비정상 과열은 심각한 사고 또는 차량 탈선의 원인이 될 수 있다. 이에 따라서 현재 고속열차 운행 중 차축의 비정상적인 발열을 검지하기 위하여 차축온도검지장치(Hot Box Detector, HBD)가 설치되어 운영되고 있다. 본 연구에서는 비정상 차축 과열 발생시, 이를 빠르고 효율적으로 검지하기 위하여 지수가중이동평균(EWMA) 기법을 적용한 차축온도 모니터링 방법을 제안하였다. 또한 제안한 방법에 대하여 통계적으로 설계하였다. 본 연구에서 제안하는 방법은 현재의 차축온도검지 모니터링 방법과 비교하여 비정상 과열의 발생에 대하여 더 좋은 성능으로 평가되었으며 그 수행도는 최대 170% 향상되었다.