• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1223-1228
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• 2004

The study for ubiquitous computing infra is proceeding actively, it make possible to use service and access network anywhere, anytime because of wire/wireless communication technology and progress of hardware. Domestically, study for the network mobility support technology which is the key technology for future ubiquitous computing realization have progressed, but that is insufficient. Especially, there is no study for independent mobility support study about railway wireless network. So, this study propose network mobility management technology for mobile network infra in railway and proper network model in train.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.442-449
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• 1998

Since it is essential for traction motors to reduce size and weight to achieve given traction effort, they need high input voltage. But the lack of input voltage occurs periodically due to the characteristics of train system. Therefore traction inverters use over-modulation PWM to maximize inverter's voltage gain. On the other hand, IGBT inverters can use higher frequency twice than GTO ones, which resulted in the need for novel optimal synchronous PWM strategy. This paper suggests that linearly-compensated overmodulation/optimal synchronous PWM strategy and also the simulation results of the method for a real traction motor-intertia model are presented.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.241-243
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• 2005

The study for ubiquitous computing infra is proceeding actively, it make possible to use service and access network anywhere, anytime because of wire/wireless communication technology and progress of hardware. Domestically, study for the network mobility support technology which is the key technology for future ubiquitous computing realization have progressed, but that is insufficient. Especially, there is no study for independent mobility support study about railway wireless network. So, this study propose network mobility management technology for mobile network infra in railway and proper network model in train.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.226-233
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• 2000

This study has been performed to develop the practical method to estimate the change of dynamic characteristics of rolling stocks passing on the high speed turnout system. Each part of turnout system are modeled in consideration of alignment, enter angle and amount of deflection and they are used to achieve dynamic analysis with a train model. Analysis results are compared with test results to confirm its validation

• Journal of Environmental Impact Assessment
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• v.12 no.4
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• pp.271-279
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• 2003

Temperature and humidity forecasting have been performed using artificial neural networks model(ANN). We composed ANN with multi-layer perceptron which is 2 input layers, 2 hidden layers and 1 output layer. Back propagation algorithm was used to train the ANN. 6 nodes and 12 nodes in the middle layers were appropriate to the temperature model for training. And 9 nodes and 6 nodes were also appropriate to the humidity model respectively. 90% of the all data was used learning set, and the extra 10% was used to model verification. In the case of temperature, average temperature before 15 minute and humidity at present constituted input layer, and temperature at present constituted out-layer and humidity model was vice versa. The sensitivity analysis revealed that previous value data contributed to forecasting target value than the other variable. Temperature was pseudo-linearly related to the previous 15 minute average value. We confirmed that ANN with multi-layer perceptron could support pollutant dispersion model by computing meterological data at real time.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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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).

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1077-1092
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• 2007

The structural analysis model for estimate of load carrying capacity of railway bridge on service line is important to determine safety of bridges in service, we need to take response of bridge exactly, applying analysis model similar to the real railway bridge most. Track structure which is to distribute loads and decrease vibrations occurred from running train is constructed on the railway bridges. And it is important factor which should be considered to understand exact dynamic and static responses of bridge. But track structure is currently classified as a none structural members in the structural analysis model for estimating load carrying capacity of railway bridge and not considered in analysis model. That's the reason it is difficult to understand exact behavior of bridges. Therefore, the major objective of this study is to develop an analytical track-bridge model which is similar to real railway bridges considering track structure for safety assessment of railway bridge on service line to be effectively done.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.