• Annual Conference of KIPS
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• 2014.04a
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• pp.749-752
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• 2014

본 논문에서는 저속으로 동작하는 구름 베어링의 다중 결함 조기 검출을 위해 결함 특징 추출, 효과적인 특징 선택, 선택된 특징을 이용한 결함 분류의 세 단계로 구성된 결함 진단 기법을 제안한다. 1단계에서 이산 웨이블릿 변환을 이용하여 미세성분으로부터 통계적 결함 특징을 추출하고, DET(distance evaluation technique)를 이용하여 추출한 결함 특징 가운데 베어링 다중 결함 검출에 효과적인 특징을 선택한다. 마지막으로 선택된 특징을 k-NN(k-Nearest Neighbors) 분류기 입력으로 사용함으로써 결함을 진단한다. 본 논문에서는 제안한 결함 진단 기법의 성능을 분류 정확도 측면에서 평가한 결과 95.14%의 높은 분류 정확도를 보였다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.37-44
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• 2023

In the future society, a means to verify the identity of the information owner is required at the beginning of most services that the information owner encounters, and the emergence and gradual spread of digital identification that proves the identity of the information owner is essential. In addition, as the utilization value of personal information increases, discussions on how to provide personal information are active. Therefore, there is a need for a personal information management method necessary for building a hyper-connected society that is safe from various hacking, forgery, alteration, and theft by allowing the owner to directly manage and provide personal information management. In this study, a decentralized identity information management model that overcomes the problems and limitations of the centralized identity management method of personal information and manages and selectively provides personal information by the information owner himself and implemented a smart personal information provision system(SPIPS: Smart Personal Information Provision System) using a smartphone.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.82-90
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• 1995

This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.2
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• pp.81-89
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• 2001

Space systems are operating in a changing and uncertain space environment and are desired to have autonomous capability for long periods of time without frequent telecommunications from the ground station At the same time. requirements for new set of projects/systems calling for ""autonomous"" operations for long unattended periods of time are emerging. Since, by the nature of space systems, it is desired that they perform their mission flawlessly and also it is of extreme importance to have fault-tolerant sensor/actuator sub-systems for the purpose of validating science measurement data for the mission success. Technology innovations attendant on autonomous data validation and health monitoring are articulated for a growing class of autonomous operations of space systems. The greatest need is on focus research effort to the development of a new class of fault-tolerant space systems such as attitude actuators and sensors as well as validation of measurement data from scientific instruments. The characterization for the next step in evolving the existing control processes to an autonomous posture is to embed intelligence into actively control. modify parameters and select sensor/actuator subsystems based on statistical parameters of the measurement errors in real-time. This research focuses on the identification/demonstration of critical technology innovations that will be applied to Autonomous Spacecraft Health Monitoring/Data Validation Control Systems (ASHMDVCS). Systems (ASHMDVCS).

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.605-613
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• 2007

In order to overcome the difficulties of quantitative risk analysis such as complexity of model, we propose a systematic methodology for risk quantification of railway system which consists of 6 steps: The identification of risk factors, the determination of major scenarios for each risk factor by using event tree, the development of supplementary fault trees for evaluating branch probabilities, the evaluation of event probabilities, the quantification of risk, and the analysis in consideration of accident situation. In this study, in order to address the feasibility of the propose methodology, this framework is applied to the prototype risk model of nation-wide railway level crossings. And the quantification result based on the data of 2005 in Korea will also be presented.

• Korean Journal of Materials Research
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• v.26 no.11
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• pp.656-661
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• 2016

4H- and 6H-SiC grown by physical vapor transport method were investigated by transmission electron microscopy (TEM). From the TEM diffraction patterns observed along the [11-20] zone axis, 4H- and 6H-SiC were identified due to their additional diffraction spots, indicating atomic stacking sequences. However, identification was not possible in the [10-10] zone axis due to the absence of additional diffraction spots. Basal plane dislocations (BPDs) were investigated in the TEM specimen prepared along the [10-10] zone axis using the two-beam technique. BPDs were two Shockley partial dislocations with a stacking fault (SF) between them. Shockley partial BPDs arrayed along the [0001] growth direction were observed in the investigated 4H-SiC. This arrayed configuration of Shockley partial BPDs cannot be recognized from the plan view TEM with the [0001] zone axis. The evaluated distances between the two Shockley partial dislocations for the investigated samples were similar to the equilibrium distance, with values of several hundreds of nanometers or even values as large as over a few micrometers.

• Nuclear Engineering and Technology
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• v.28 no.1
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• pp.36-43
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• 1996

In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. The system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the KORI-2 nuclear power unit is developed and examined in this work. The analysis and the fault identification of the thermal cycle of a nuclear power plant is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, this algorithm is shown to be able to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work.

• Journal of the Korean Geophysical Society
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• v.2 no.1
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• pp.57-64
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• 1999

To effectively identify near-surface faults with vertical slips from seismic refraction data, the GRM interpretation technique is tested and investigated in terms of various parameters through computer modeling. A characteristic change in shape of the velocity-analysis function near faults is noticed, and a new strategy of `Slope Variation Indicator (SVI)' is developed and tested in this study. The SVI is defined as a first horizontal derivative of the difference of velocity analysis functions for a large XY value and a small one, respectively. As the dip of refractor decreases and as the difference in XY value increases, the peak value of SVI increases and its duration decreases. Consequently, the SVI indicates accurately the location of buried fault in the test models. The SVI is believed to be an efficient tool in seismic refraction method to investigate location and distribution of shallowly buried faults.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.479-484
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• 2015

Network RTK is a precise positioning technique using carrier phase correction data from reference stations within the network, and is constantly being researched for improved performance. However, the study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy, because network RTK has been mainly used on surveying for static or kinematic positioning. In this paper, adequate monitoring system for network RTK is designed as basis research for integrity monitoring on network RTK. To this, fault tree on network RTK is analyzed, and a countermeasure is prepared to detect and identify the each fault items. Based these algorithms, monitoring system to use on central processing facility is designed for network RTK service.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.