• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.302-313
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• 2010

In this paper, we propose an enhanced AGC (Automatic Gain Control) structure and P-SCH detection method for initial cell search in 3GPP (3rdGenerationPartnershipProject) LTE (Long Term Evolution) FDD(Frequency Division Duplex) / TDD (Time Division Duplex) dual mode system. Since TDD frame structure consists of uplink subframe and downlink subframe, conventional AGC structure causes P-SCH detection performance degradation by increase of AGC variation due to signal power difference between uplink and downlink subframe. Also, P-SCH detection performance is degraded by distortion of P-SCH correlation characteristic in frequency offset and multipath fading channel environments. Therefore, we propose an AGC structure which can minimize P-SCH detection performance degradation with stable operation in 3GPP LTE TDD mode as well as FDD mode. Also we propose a P-SCH detection method which can reduce distortion of correlation chareteristics in frequency offset and multipath fading environments and obtain good P-SCH detection performance. Simulation results show that the proposed AGC structure and P-SCH detection method have stable AGC operation and excellent P-SCH detection performance for 3GPP LTE TDD / FDD dual mode downlink receiver in various channel environments.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1456-1463
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• 2010

The safety critical systems in nuclear power plants should be designed to have a high level of fault tolerant capability because those systems are used for protection or mitigation of the postulated accidents of nuclear reactor. Due to increasing of the system complexity of the digital based system in nuclear fields, the reliability of the digital based systems without an auto-test or a self-diagnostic feature is generally lower than those of analog system. To overcome this problem, additional redundant architectures in each redundant channel and self-diagnostic features are commonly integrated into the digital safety systems. The self diagnostic function is a key factor for increasing fault tolerant capabilities in the digital based safety system. This paper presents an availability and safety evaluation model to analyze the effect to the system's fault tolerant capabilities depending on self-diagnostic features when the loss or erroneous behaviors of self-diagnostic function are expected to occur. The analysis result of the proposed model on the several modules of a safety platform shows that the improvement effect on unavailability of each module has generally become smaller than the result of usage of conventional models and the unavailability itself has changed significantly depending on the characteristics of failures or errors of self-diagnostic function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.977-985
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• 2021

The LiDAR sensor is attracting attention as a key sensor for autonomous driving vehicle. LiDAR sensor provides measured three-dimensional lengths within range using LASER. However, as much data is provided to the external system, it is difficult to process such data in an external system or processor of the vehicle. To resolve these issues, we develop integrated processing system for LiDAR sensor. The system is configured that client receives data from LiDAR sensor and processes data, server gathers data from clients and transmits integrated data in real-time. The test was carried out to ensure real-time processing of the system by changing the data acquisition, processing method and process driving method of process. As a result of the experiment, when receiving data from four LiDAR sensors, client and server process was operated using background or multi-core processing, the system response time of each client was about 13.2 ms and the server was about 12.6 ms.

• Journal of the Korean Society of Safety
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• v.36 no.4
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• pp.1-11
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• 2021

Piping systems comprising pumps and valves are essential in the power plant, oil, and defense industry. Their purpose includes a stable supply of the working fluid or ensuring the target system's safe operation. However, piping system accidents due to leakage of toxic substances, explosions, and natural disasters are prevalent In addition, with the limited maintenance personnel, it becomes difficult to detect, isolate, and reconfigure the damage of the piping system and recover the unaffected area. An autonomous recovery piping system can play a vital role under such circumstances. The autonomous recovery algorithms for the piping system can be divided into low-pressure control algorithms, hydraulic resistance control algorithms, and flow inventory control algorithms. All three methods include autonomous opening/closing logic to isolate damaged areas and recovery the unaffected area of piping systems. However, because each algorithm has its strength and weakness, appropriate application considering the overall design, vital components, and operating conditions is crucial. In this regard, preliminary research on algorithm's working principle, its design procedures, and expected damage scenarios should be accomplished. This study examines the characteristics of algorithms, the design procedure, and working logic. Advantages and disadvantages are also analyzed through simulation results for a simplified piping system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.60-70
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• 2011

Vehicle location detection and wireless communication method have be designed along the same lines as GPS, CDMA and WLAN, which is based upon the selecting factors such as state-of-art technology trend, accuracy, stability, and economic feasibility, in order to select the optimum method of information communication networks for integrated "Transportation-Power-Facilities" on the electric bus. In addition, the key features of each alternative for an efficient linkage have been review and the integration methodology for linking among Transportation Charging Center, Transportation(ITS, BIS) Center and smart Grid Center has been drawn up based on a technical level of difficulty of each alternative, political and administrative difficulties, and expense justification.

• Progress in Superconductivity and Cryogenics
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• v.6 no.2
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• pp.25-28
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• 2004

Three-phase inductive superconducting fault current limiter (SFCL) with DC reactor rated on 6.6 $KV_{rms}/200 A_{rms}$ has been developed in Korea. This system consists of one DC reactor, AC/DC power converter, and a three-phase transformer, which is called magnetic core reactor (MCR). This paper deals with the short-circuit analysis of the SFCL. The DC reactor was the HTS solenoid coil whose inductance was 84mH. The power converter was performed as the dual-mode operation for dividing voltage between the rectifying devices. The short-term normal operation (1 see) and short-circuit tests (2∼3 cycles) of this SFCL were performed successfully. In regular short-circuit test, the fault current was limited as 30% of rated short-circuit current at 2 cycles after the fault. The experimental results have a very similar tendency to the simulation results. Using the technique for the fault detection and SCR firing control, the fault current limiting rate of the SFCL was improved. From this research, the parameters for design and manufacture of large-scale SFCL were obtained.

• Nuclear Engineering and Technology
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• v.30 no.5
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• pp.435-443
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• 1998

Leak-before-break(LBB) approach has been shown to be both cost effective and risk reductive when applied to high energy Piping in nuclear Power Plants. For the Korean Next Generation Reactor (KNGR) development, LBB application is considered for the Main Steam Line(MSL) piping inside containment. Unlike the primary system leakages, the MSL leak detection systems must be based on principles other than radioactivity measurements. Among humidity, heat and acoustic noise currently being considered as indicators of leakage, we explored humidity as an effective one and developed ceramic-based humidity sensor which can be qualified for LBB applications. The ceramic material, sintered and annealed MgCr$_2$O$_4$-TiO$_2$, is shown to increase its electrical conductivity drastically upon water vapor adsorption over the entire temperature range of interest. With this ceramic sensor specimen, we suggested installation-inside-the-piping method by which we can detect leakage more rapidly and sensitively. In this paper, we describe the progress in the development and characterization of ceramic humidity sensor for the LBB application to the MSL of KNGR.

• Nuclear Engineering and Technology
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• v.47 no.3
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• pp.332-339
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• 2015

The leak before break (LBB) concept is widely used in designing pipe lines in nuclear power plants. According to the concept, the amount of leaking liquid from a pipe should be more than the minimum detectable leak rate of a leak detection system before catastrophic failure occurs. Therefore, accurate estimation of the leak rate is important to evaluate the validity of the LBB concept in pipe line design. In this paper, a program was developed to estimate the leak rate through circumferential cracks in pipes in nuclear power plants using the Henry-Fauske flow model and modified Henry-Fauske flow model. By using the developed program, the leak rate was calculated for a circumferential crack in a sample pipe, and the effect of the flow model on the leak rate was examined. Treating the crack morphology parameters as random variables, the statistical behavior of the leak rate was also examined. As a result, it was found that the crack morphology parameters have a strong effect on the leak rate and the statistical behavior of the leak rate can be simulated using normally distributed crack morphology parameters.