• Journal of the Korean Society for Railway
• /
• v.18 no.3
• /
• pp.223-231
• /
• 2015

This paper proposes a new wireless backhaul technology based on MHN(Mobile Hotspot Network) which uses a wide frequency band of millimeter wave to provide wideband Wi-Fi services for subway passengers. Performance analysis of MHN up and down links, based on data: up and down links structure analysis of physical layer and simulation study of the MHN wireless backhaul link model, show that the proposed MHN-based wireless backhaul network can transmit data at a 1.2Gbps data rate and provide Internet service 100 times faster than that of conventional WiBro-based wireless backhaul networks. These results indicate that the proposed MHN technology is appropriate for subway mobile networks.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.3
• /
• pp.72-83
• /
• 2000

This paper presents the implementation of the position control system for 3 phase induction motor using reaching mode controller and neural networks. The reaching mode controller is used to bring the position error and speed error trajectories toward the sliding surface and to train neural networks at the first time. The structure of the reaching mode controller consists of the switch function of sliding surface. And feedforward neural networks approximates the equivalent control input using the reference speed and reference position and actual speed and actual position measured form an encoder and, are tuned on-line. The reaching mode controller and neural networks are applied to the position control system for 3 phase induction motor and, are compared with a PI controller through computer simulation and experiment respectively. The results are illustrated that the output of reaching mode controller is decreased and feedforward neural networks take charge of the main part for the control action, and the proposed controllers show better performance than the PI controller in abrupt load variation and the precise control is possible because the steady state error can be minimized by training neural networks.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.4
• /
• pp.259-266
• /
• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Korean Society for Railway
• /
• v.17 no.5
• /
• pp.336-341
• /
• 2014

The articulated structures seen in train or tram applications are being applied in road transportation systems, for use in mass passenger transit. When articulated vehicles are driven on public roads, they no longer follow a guided track. Therefore, there are a lot of control elements that need to be considered, such as turning radius, swept path width, off-tracking, and swing-out. Some of the currently available articulated vehicles on roads are equipped with an independent drive system; a system that has one motor at each wheel. Through this drive system, each wheel can be independently controlled, making precise and quick dynamic stability control possible. In this paper, we propose a torque distribution algorithm that can reduce the overall turning radius of the articulated vehicle, which has been verified through dynamic simulation.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.5
• /
• pp.237-242
• /
• 2023

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.17 no.3
• /
• pp.257-266
• /
• 2015

This study introduce that we studied optimization and possibility of smoke spread prevention with air-curtain system in undersea tunnel named from Ho-Nam to Jeju line in domestic if a fire break out in train. To verify performance, air-curtain system is installed between rescue station platform and each door of passenger car to provide safety route to evacuator and we studied simulation model of various cases about 15 MW fire severity considering domestic specifications. As a result we verified the fact that CASE1(air jet with 15degree toward passenger car) and CASE 5 (air jet with 15degree toward passenger car and pressure air blast from cross passage) is best Smoke Spread Prevention and less inflow carbon monoxide. Through above results, we expect that air-curtain system is one of the facilities for fire safety and provide us safety platform route in undersea tunnel.

• Structural Monitoring and Maintenance
• /
• v.2 no.1
• /
• pp.77-93
• /
• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of the Korean Society for Railway
• /
• v.19 no.5
• /
• pp.617-628
• /
• 2016

This paper proposes the structure of a smart transformer to improve the performance of the 60Hz main power transformer for rolling stock. The proposed smart transformer is a kind of solid state transformer that consists of semiconductor switching devices and high frequency transformers. This smart transformer would have smaller size than the conventional 60Hz main transformer for rolling stock, making it possible to operate AC electrified track efficiently by power factor control. The proposed structure employs a cascade H-Bridge converter to interface with the high voltage AC single phase grid as the rectifier part. Each H-Bridge converter in the rectifier part is connected by a Dual-Active-Bridge (DAB) converter to generate an isolated low voltage DC output source of the system. Because the AC voltage in the train system is a kind of medium voltage, the number of the modules would be several tens. To control the entire smart transformer, the inner DC voltage of the modules, the AC input current, and the output DC voltage must be controlled instantaneously. In this paper, a control algorithm to operate the proposed structure is suggested and confirmed through computer simulation.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.541-549
• /
• 2018

This paper deals with the braking dynamic behavior of diesel electric locomotive pulling domestic cargo and passenger vehicles. Friction coefficient, pneumatic pressure, and running resistance affecting the braking system were tested. For the friction coefficient, the Dynamo test was performed with reference to UIC 541-4. The results are analyzed by multivariate regression and the relationship between braking force and ititial velocity is presented. The pneumatic pressure were classified into service braking and emergency braking. In order to reflect the characteristics of the brake valve and piping, the pressure rising over time was measured in the vehicle. In order to reflect the external force acting on the vehicle, we carried out the test of EN 14067-4 and presented the second order polynomial formula on a running resistance. The running resistance test results were compared with other countries. The dynamic behavior of a diesel electric locomotive running on a straight flat track based on vehicle resources, friction coefficient, braking pressure, and running resistance is simulated using the time integration presented in EN 14531-1. The simulation results were compared and verified with the vehicle braking test results. The results of this study can be used to analyze the dynamic braking behavior of a train. Also, it is expected that various parameters affecting braking in vehicle design can be analyzed and used as basic data for braking performance improvement.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.