• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.4
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• pp.173-180
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• 2022

Most of the drinking water dams managed by the local governments in Korea are earthfill dams, and these dams have almost no geotechnical property information necessary for seismic performance evaluation. Nevertheless, in the rough planning stage for improving seismic safety for these dams, it is necessary to classify their relative seismic hazard against earthquakes and conduct an additional ground investigation. The zero seismic failure probability curve is a curve suggested in this study in which the probability of failure due to an earthquake becomes '0' regardless of the geotechnical properties of the earthfill dam. By examining the method and procedure for calculating failure probability due to an earthquake suggested in previous researches, the zero seismic failure probability curves for an earthquake in 1,000-year and 2,400-year return periods in Korea were presented in the form of a hyperbola on the plane of the dam height versus freeboard ratio (ratio of freeboard to dam height), respectively. The distribution characteristics of the dam height and the freeboard ratio of 81 Korean earthfill dams were presented. The two proposed zero seismic failure probability curves are shown on the plane of the dam height versus freeboard ratio, and the relative seismic hazard of 81 dams can be classified into three groups using these curves as boundaries. This study presented the method of classifying the relative seismic hazard and the classification result.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.993-999
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• 2007

With the introduction of the RAMS(Reliability, Availability, Maintainability, Safety), the interest of the system assurance has been increased. First of all, fast-growing electronic circuit requires analyzing the failure rates, by dividing the signalling system more specifically. Since 2005, the K.N.R (Korean National Railway) has incorporated ERP(Enterprise Resource Planning) in order to establish the complete status as the top international comprise, therefore while ordering the project, it has established the classification system and then has been applying to ERP system in 2007. Due to the complex of the classification system, the reliability analysis of the signalling system was assessed with the limit of IXL ATP with On-board and wayside equipment. This paper assumed MTBF(Mean Time Between Failure), MTTR((Mean Time Between Repair) of total signalling system, by using the classification of ERP program.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.195-202
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• 2011

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.219-226
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• 2013

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.703-706
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• 2003

Yield enhancement in semiconductor fabrication is important. It is ideal to prevent all the failures. However, when a failure occurs, it is important to quickly specify the cause stage and take countermeasure. The automatic method of failure pattern extraction from fail bit map provides reduced time to analysis and facilitates yield enhancement. This paper describes the techniques to automatically classifies a failure pattern using a fail bit map, a new simple schema which facilitates the failure analysis.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.31-37
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• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.923-932
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• 1994

The studies on the bearing capacity of shallow and deep foundations have been made in various fields and formulas for various failure mechanisms have been presented. But, for these models, the method of classification with foundation depth has been obscure and bearing capacity factors have not been uniformly applied. An experiment was performed, in plane strain conditions, with ground model made of carbon rods. The failure mechanism of foundation and ultimate bearing capacity with foundation depth were observed. Based on experimental results the classification between shallow and deep foundations by failure shape was tried. Various present failure mechanisms of foundation were verified through the experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5123-5129
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• 2013

In this paper, it is proposed that JTAG fault injection environment and the results of the classification techniques that the reliability of embedded systems can be tested. As applying these, this is possible to quantitative analysis of vulnerable factor for system. The quantitative analysis for the degree of vulnerability of system is evaluated by faults errors, and failures classification schemes. When applying these schemes, it is possible to verify process and classify for fault that might occur in the system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Korean Journal of Adult Nursing
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• v.27 no.2
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• pp.242-250
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• 2015

Purpose: To evaluate heart failure knowledge and adherence to self-care behaviors, and to identify factors affecting adherence to self-care behaviors among Korean patients with heart failure. Methods: Correlational research using the European Heart Failure Self-care Behavior Scale, the Duke Activity Status Index, the Dutch Heart Failure Knowledge Scale, the New York Heart Association Functional Classification, and the Medical Outcomes Study Social Support Survey was conducted. A total of 280 outpatients with heart failure responded to the five questionnaires. Results: The mean scores for self-care adherence and heart failure knowledge were $31.98{\pm}6.81$ and $8.78{\pm}2.53$, respectively, indicating lower adherence and knowledge than those previously reported. Subjects with lower functional status, more social supports, and greater knowledge of heart failure are more likely to adhere to prescribed regimens. Conclusion: Nurses should focus on patient education and support to improve their adherence to self-care behaviors.