• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.9-20
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• 2015

To derive a check list of a borrowed city phenomenon, this discussion, through two cities subway OOH advertising in door, compared for convergence by segment analysis. First, discussions considered to be close to the topic of this discussion were arranged and summarized. Seoul and London were selected, and both sides of subway train doors in two cities selected, their states pictured and the images analyzed. As a result of comparison, in case of Seoul subway, the boundary between public information and commercial information(outdoor advertising) was very vague, so the borrowed city phenomenon was very generally analyzed. However, In case of London subway, its result was very contrary to that of Seoul. Eventually, Subway train doors of Seoul city as a borrowed city extracted check points of height, size, color, interval, space and permanency. Therefore in order to study serious discussion this contribution is necessity to limit thoughtless OOH advertising by these checklists in Korean cities.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.63-70
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• 2008

This paper presents a method of PRI do-interleaving for LASER pulse signals. When the PRI of LASER pulse is periodically changed, the first deviation and the second deviation of TOA is used to calculate the PRI pattern of input LASER signals of receiver. If the standard deviation of the first difference of TOA is less than 5% of the average of the first difference of TOA, the PRI pattern of LASER signal is fixed or jittered type. If the standard deviation is larger than 5% of the average, those are triangular PRI patterns or sawtooth PRI patterns.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.187-196
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• 2014

In this paper, Minimize driving energy for operation within a defined distance yeokgan fixed time-resolved and determine the nature of the train is traveling, and to model mathematically. Urban rail car cruise in general by the PID controller is used instead of automatically tracking a target value while traveling in energy consumption to be minimized by using optimal control model railroad charyangreul was designed under real operating conditions the same. The actual track conditions apply to the minimum value or a separate listing of cars around the track facility without a driving energy of the automatic operation and to reduce the driving energy. Therefore, actual route chosen straight line 8 / gradient segment / curve for the measured data analysis, such as sections within the city-minute drive each section and presented how the trains to save energy, depending on the pattern of the train station in the region.

• Journal of the Korean Society for Railway
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• v.18 no.5
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• pp.426-438
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• 2015

Train speed controlling systems are now changing from wayside systems to onboard signaling systems. Locomotive engineers refer to wayside markers to decide on a braking point when the train speed appears to be lower than the current speed. However, in the onboard signaling systems that have been installed recently, the braking point is not determined by the wayside signal but by an onboard value. In this paper, we studied braking points and methods for deciding on such points by engineers using the onboard systems. An optimized braking point is proposed via simulation of decelerating velocity to control the velocity in the signaling system through a predefined point; Gaussian distributions are used to simulate the actual situation. We estimated and demonstrated how to obtain braking parameters in order to satisfy the interval of permitted error.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.110-116
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• 2013

Track irregularities is one of the key factors influencing the running behavior of trains. In order to ensure safety and ride comfort of train, the criteria for track irregularities should be adequately established regarding vehicle velocity, vehicle characteristics, characteristics of the track recording car, and measurement interval. Also, track maintenance should be carried out thoroughly according to the criteria for managing track irregularities. Numerical analysis was conducted to investigate the influence of track alignment on the running behavior of Korean high speed train(KTX). Various wavelengths and amplitudes of lateral alignment were considered as parameters for this study using the Vampire program, a vehicle dynamics modeling package in railway environment. Derailment, lateral load, bogie acceleration and body acceleration of numerical analysis results due to alignment were investigated. Finally, the influence of the alignment on safety and ride comfort for the KTX was evaluated. This study indicates that alignment irregularities have significant impacts on running safety, and that the criteria used to manage alignment irregularities should be restrictive to ensure the running safety of the KTX.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8187-8194
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• 2015

To accelerate a heavy roller coaster train with over 1G force, a lot of thrust is required and linear synchronous motor(LSM) as propulsion method is suitable for this kind of system. To increase the propulsion efficiency of LSM, precise and real-time position information of vehicle is required for accurate phase control. However, the discontinuous position information with relatively long time interval is usually transmitted from the hall-sensors on the track every magnet length. In this paper, the basic motor model based on traditional dq-axis equations is described and the motor dynamic model is produced by considering the cogging force and friction loss. To improve the position accuracy, the position estimator is also proposed for LSM control system. Simulations were performed to check the characteristics of the torque control system which includes the position estimator based on the motor model. Simulation results based on the linearized model show that this control system has an enough bandwidth and phase margin and the executed algorithm achieves an ideal effect to follow the real-time position signal. Therefore, the feasibility of position estimator is also confirmed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.