• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.953-960
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• 2009

In the major operation section of urban railway, there has been occurred habitual delay, and delay propagation and spread to consecutive trains. Therefore, this delay cause the increase of operation time and irregular operation, also the increase of crowd and inconvenience to passengers. The railway operation plan is a promise to passengers and must have reliability. In the case of high frequency urban operation, dwell time have considerable influence on operational headway, so it is very important to efficiently plan and control the dwell time at stations. In this paper, the survey of the research on dwell time for urban railways is presented. The practical estimation model for minimum required dwell time, and the numerical example and validity are proposed.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.1
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• pp.85-93
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• 2000

In this paper, the predischarge propagation processes of SF\ulcorner gas stressed by impulse voltages under nonuniform electric field perturbed by a needle protrusion are described. The statistical and formative time-lags and the time interval between leader pulses were investigated on the basis of the predischarge current measured in the gas pressure range of 0.1~0.5 MPa. The predischarge current is closely related to the waveform, amplitude and polarity of applied votages, the gas pressure and the gap geometry. Both the positive and negative predischarge processes in nonuniform electric field develop in a regime of stepwise leader propagation leading to electrical breakdown. The mean of the time interval between leader pulses gives about a factor of 10 higher for the negative than for the positive leader current puls-es. According as the gas pressure increases, the statistical time-lag was almost unchangeable, but the formative time-lag was gradually decreased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.50-58
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• 2003

We have developed a locally variable time-step scheme matching with discontinuous grids in the flute-difference method for the efficient simulation of seismic wave propagation. The first-order velocity-stress formulations are used to obtain the spatial derivatives using finite-difference operators on a staggered grid. A three-times coarser grid in the high-velocity region compared with the grid in the low-velocity region is used to avoid spatial oversampling. Temporal steps corresponding to the spatial sampling ratio between both regions are determined based on proper stability criteria. The wavefield in the margin of the region with smaller time-step are linearly interpolated in time using the values calculated in the region with larger one. The accuracy of the proposed scheme is tested through comparisons with analytic solutions and conventional finite-difference scheme with constant grid spacing and time step. The use of the locally variable time-step scheme with discontinuous grids results in remarkable saving of the computation time and memory requirement with dependency of the efficiency on the simulation model. This implies that ground motion for a realistic velocity structures including near-surface sediments can be modeled to high frequency (several Hz) without requiring severe computer memory

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.2
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• pp.23-28
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• 2005

This study was to suggest the propagation path loss and predictive model of propagation path analysis in order to apply the frequency in the millimeter-wave band to the real time inter-vehicle communication system. This study was to suppose the case of inter-vehicle communication on the one-way two-lanes road in the big cites with a lot of traffic jams in order to analyze the effect by the reflected wave of multipath. As a simulation of suggested model, it found out that the propagation path by the reflected wave was about 0.1[m]$\sim$5.1[m] longer than the one by the direct wave during the transmission of 100[m] wave direct path. Also, as a result of comparing the propagation path loss, the loss would be about -0.8[dB]$\sim$-4.2[dB] larger in case of wall reflection and -0.8[dB]$\sim$-1[dB] vehicle reflection. From the result above, this researcher found out that the path loss of reflected wave produced by the walls was about -3.2[dB] larger than the path loss produced by the adjacent vehicles.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.

• Smart Structures and Systems
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• v.16 no.2
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• pp.281-294
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• 2015

Bolted joint connection is the most commonly used connection element in structures and devices. The loosening due to external dynamic loads cannot be observed and measured easily and may cause catastrophic loss especially in an extreme requirement and/or environment. In this paper, an innovative Real-time Cross-Correlation Method (RCCM) for monitoring of the bolted joint loosening was proposed. We apply time reversal process on stress wave propagation to obtain correlation signal. The correlation signal's peak amplitude represents the cross-correlation between the loosening state and the baseline working state; therefore, it can detect the state of loosening. Since the bolt states are uncorrelated with noise, the peak amplitude will not be affected by noise and disturbance while it increases SNR level and increases the measured signals' reliability. The correlation process is carried out online through physical wave propagation without any other post offline complicated analyses and calculations. We implemented the proposed RCCM on a single bolt/nut joint experimental device to quantitatively detect the loosening states successfully. After that we implemented the proposed method on a real large structure (reaction wall) with multiple bolted joint connections. Loosening indexes were built for both experiments to indicate the loosening states. Finally, we demonstrated the proposed method's great anti-noise and/or disturbance ability. In the instrumentation, we simply mounted Lead Zirconium Titanate (PZT) patches on the device/structure surface without any modifications of the bolted connection. The low-cost PZTs used as actuators and sensors for active sensing are easily extended to a sensing network for large scale bolted joint network monitoring.

• Geophysics and Geophysical Exploration
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• v.19 no.3
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• pp.131-135
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• 2016

Coarray is a parallel processing technique introduced in the Fortran 2008 standard. Coarray can implement parallel processing using simple syntax. In this research, we examined applicability of Coarray to seismic parallel processing by comparing performance of seismic data processing programs using Coarray and MPI. We compared calculation time using seismic wave propagation modeling and one to one communication time using domain decomposition technique. We also compared performance of parallel reverse-time migration programs using Coarray and MPI. Test results show that the computing speed of Coarray method is similar to that of MPI. On the other hand, MPI has superior communication speed to that of Coarray.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.215-218
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• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.444-446
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• 1994

This paper describes the design of a digital multifunction controller for the intelligent high voltage customer switchboard and proposes a relaying algorithm for high impedance faults using back-propagation neural network. The hardware design uses the three microprocessors and global memory architecture to achive real time operation and control 4 feeders. The controller uses a 64-point radix-4 DIF FFT algorithm to measure the harmonic and relay parameters. Synthesized fault current waveforms are used to train and test the back - propagation network.