• 대한산업공학회지
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• 제22권1호
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• pp.129-139
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• 1996

Fault tree analysis is NP-hard problem. In this paper, we present a method which reduces size of the fault tree by eliminating the irrelevant events. Irrelevant event is the event which has no contribution to the system failure. In a fault tree, the irrelevant events occur due to the existence of the replicated events. By investigating the structure of the replicated events we establish the conditions which characterize the irrelevant events. Based on these conditions we present the computational algorithm which eliminate the irrelevant events. Complexity of the algorithm is shown to be polynomial and so, this algorithm can be utilized efficiently in FTA.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 춘계 학술발표회논문집
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• pp.38-44
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• 2003

Artificial blasts, defined as seismo-acoustic events, were discriminated from natural earthquakes in the Korean Peninsula by the seismo-acoustic analysis. Total 197 seismo-acoustic events corresponding to 9 percent of seismic events in 2002 were analyzed and classified as artificial surface blasts. Events distribution pattern of the seismo-acoustic events in 2002 was similar to the previous result in 1999-2001, except for two regions. Newly determined seismo-acoustic events were added to the previous artificial blast database. To extend infrasound detection capability, new small-scale infrasound array(TJIAR) was installed in KIGAM. Preliminary analysis for the small array was conducted to discriminate artificial blasts in the southwestern part of the Korean Peninsula. The small array discriminated S seismo-acoustic events during short period analysis. And two infrasound arrays(TJIAR and CHNAR) were used to determine approximate sound source location by cross bearing method.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 춘계 학술발표회논문집
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• pp.12-15
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• 2003

Korea Institute of Geoscience and Mineral Resources(KIGAM) is operating Wonju Korea Seismic Research Station(KSRS), 29 regional seismic research stations and 5 Korea-China joint seismic stations in China. Also KIGAM is operating Korea Earthquake Monitoring System (KEMS) to archive the real-time data stream and to determine event parameters (epicenter, origin time, and magnitude) by the automatic processing and analyst review. To do this, KEMS used KIGAM's regional seismic network and other institute's network in a near real-time base. From Dec. 1, 2001 to Nov. 30, 2002, 3,827 seismic events were analyzed in a automatic processing procedure and finally 3,437 events were analyzed by analyst and archived. But problem is this event catalog includes not only natural earthquake, but also artificial events produced by the blast. More than 80 % events were concentrated in daytime and many events were concentrated in the confirmed blast sites, Pyeongyang, Pocheon, Yeongjong-do, Donghae city, etc. Because these artificial events are a major potential cause of error when estimating the seismicity of a specific region, discrimination procedure has to be developed in the first place.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.1174-1179
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• 2007

Train collision and derailment are types of accident that happen with low probability of occurrence but could lead to disastrous consequences including multiple life losses. Risk assessment of the accidents are typically performed per their hazardous events, which are defined as events that cause accidents. This study classifies the train collision and derailment based on the relevant hazardous event, and investigates the causes related to the hazardous events. Finally, the relation of the causes, hazardous events, and the accidents are defined.

• Nuclear Engineering and Technology
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• 제49권2호
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• pp.342-348
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• 2017

Precursor analysis is widely used in the nuclear industry to judge the significance of events relevant to safety. However, in case of events that may damage equipment through effects that are not ordinary functional dependencies, the analysis may not always fully appreciate the potential for further evolution of the event. For fires, which are one class of such events, this paper discusses modelling challenges that need to be overcome when performing a probabilistic precursor analysis. The events used to analyze are selected from the Organisation for Economic Cooperation and Development (OECD) Fire Incidents Records Exchange (FIRE) Database.

• 한국재난정보학회 논문집
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• 제18권2호
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• pp.252-260
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• 2022

연구목적: 기후변화 등으로 인해 자연재해의 빈도 및 강도가 증가하고 있어, 원자력발전소에 영향을 주는 외부사건이 발생할 수 있다. 원자력발전소는 자연재해, 인공재해 등 외부사건으로부터 안전해야 한다. 따라서 원자력발전소에 발생할 수 있는 외부사건을 식별하여야 하며, 원자력발전소에 큰 영향을 줄 수 있는 외부사건을 선별하여야 한다. 본 연구에서는 외부사건을 식별 및 선별하는 방법의 연구동향을 소개한다. 연구방법: 원자력발전소의 확률론적 안전성 평가를 위한 외부사건의 식별 및 선별에 관한 문헌조사를 실시하였다. 또한 국내외 외부사건의 식별 및 선별에 관한 규정을 조사하였다. 연구결과: 원자력발전소의 외부사건 영향분석의 비용을 최소화하고자 외부사건을 식별 및 선별하는 연구가 이루어지고 있다. 각 연구는 관점에 따라 차이를 보이지만 공통적으로 식별과정에서는 원자력발전소 부지에서 발생할 수 있는 모든 사건을 식별하고자 하며, 선별과정에서는 정성적 기준과 정량적 기준을 바탕으로 외부사건을 선별한다. 결론:기후변화 등으로 인하여 자연재해의 강도가 변화하고 있어, 원자력발전소에 영향을 주는 외부사건을 식별 및 선별하는 과정이 중요해지고 있다. 따라서 본 논문에서는 외부사건을 식별과 선별하는 방법에 대해 조사하여 정리하였다. 국내의 경우 원자력발전소의 확률론적 안전성 평가를 위한 외부사건의 식별과 선별에 관한 연구가 미미한 실정이다. 또한 외부사건 정량적 선별과정에서 수행하는 '경계분석'과 '명백히 보수적인 분석' 방법에 관한 연구가 필요할 것으로 보인다.

• The Journal of the Acoustical Society of Korea
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• 제23권4E호
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• pp.112-119
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• 2004

This paper presents a method for the automatic recognition of pitch accents with no prior knowledge about the phonetic content of the signal (no knowledge of word or phoneme boundaries or of phoneme labels). The recognition algorithm used in this paper is a time-delay recurrent neural network (TDRNN). A TDRNN is a neural network classier with two different representations of dynamic context: delayed input nodes allow the representation of an explicit trajectory F0(t), while recurrent nodes provide long-term context information that can be used to normalize the input F0 trajectory. Performance of the TDRNN is compared to the performance of a MLP (multi-layer perceptron) and an HMM (Hidden Markov Model) on the same task. The TDRNN shows the correct recognition of $91.9{\%}\;of\;pitch\;events\;and\;91.0{\%}$ of pitch non-events, for an average accuracy of $91.5{\%}$ over both pitch events and non-events. The MLP with contextual input exhibits $85.8{\%},\;85.5{\%},\;and\;85.6{\%}$ recognition accuracy respectively, while the HMM shows the correct recognition of $36.8{\%}\;of\;pitch\;events\;and\;87.3{\%}$ of pitch non-events, for an average accuracy of $62.2{\%}$ over both pitch events and non-events. These results suggest that the TDRNN architecture is useful for the automatic recognition of pitch accents.

• 천문학회보
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• 제38권2호
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• pp.72.1-72.1
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• 2013

Even though current microlensing follow-up observations focus on high-magnification events due to the high efficiency of planet detection, it is very difficult to do a confident detection of planets in high-magnification events with extremely weak central perturbations (i.e., the fractional deviation is ${\delta}{\leq}0.02$). For the confident detection of planets in the extremely weak central perturbation events, it is needed both the high cadence monitoring and the high photometric accuracy. A next-generation ground-based observation project, KMTNet (Korea Microlensing Telescope Network), satisfies both the conditions. Here we investigate how well planets in high-magnification events with extremely weak central perturbations are detected by KMTNet. First, we determine the probability of occurrence of events with ${\delta}{\leq}0.02$. From this, we find that for ${\leq}100M_E$ planets in the separation of $0.2AU{\leq}d{\leq}20AU$, events with ${\delta}{\leq}0.02$ occur with a frequency of more than 70%, in which d is the projected planet-star separation. Second, we estimate the efficiency of detecting planetary signals in the events with ${\delta}{\leq}0.02$ via KMTNet. We find that for main-sequence and subgiant source stars, ${\geq}1M_E$ planets can be detected more than 50% in a certain range that has the efficiency of ${\geq}10%$ and changes with the planet mass.

• Nuclear Engineering and Technology
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• 제47권2호
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• pp.187-195
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• 2015

Human-induced initiating events, also called Category B actions in human reliability analysis, are operator actions that may lead directly to initiating events. Most conventional probabilistic safety analyses typically assume that the frequency of initiating events also includes the probability of human-induced initiating events. However, some regulatory documents require Category B actions to be specifically analyzed and quantified in probabilistic safety analysis. An explicit modeling of Category B actions could also potentially lead to important insights into human performance in terms of safety. However, there is no standard procedure to identify Category B actions. This paper describes a systematic procedure to identify Category B actions for low power and shutdown conditions. The procedure includes several steps to determine operator actions that may lead to initiating events in the low power and shutdown stages. These steps are the selection of initiating events, the selection of systems or components, the screening of unlikely operating actions, and the quantification of initiating events. The procedure also provides the detailed instruction for each step, such as operator's action, information required, screening rules, and the outputs. Finally, the applicability of the suggested approach is also investigated by application to a plant example.

• 한국보건간호학회지
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• 제31권3호
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• pp.518-527
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• 2017

Purpose: This study examined the relationship among stressful life events, mindfulness and depression, and explored the mediating role of mindfulness between stressful life events and depression in nursing students. Methods: A cross-sectional survey was undertaken. A sample of 120 nursing students participated in the study. The self-reported measures of stressful life events (revised life stress scale), depression (DASS-D) and mindfulness (MAAS) were adopted in April, 2017. The data were analyzed using descriptive statistics, frequency, Pearson correlation coefficient and multiple regression. Mediation analysis was performed using the Baron & Kenny method and Sobel test. Results: The mean scores of stressful life events, mindfulness and depression were 39.50, 77.03, and 8.18, respectively. Higher levels of stressful life events were associated with lower mindfulness (r = -46, p < .001) and higher depression (r = -44, p < .001), and mindfulness was negatively associated with depression (r = -72, p < .001). Mindfulness showed full mediation in the relationship between stressful life events and depression. Conclusion: Stressful life events influenced the nursing students' depression via the full mediating effect of mindfulness. Promoting mindfulness plays an important role in reducing depression in nursing students. Therefore, it is important to develop a mindfulness program or use a mindfulness-based stress reduction program (MBSR) for nursing students.