• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• International Journal of Contents
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• v.14 no.3
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• pp.7-16
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• 2018

The extent and depth of the event plan determines the scope of pedagogical experience in situations and consequently the quality of immersive learning based on our simulated world. In contrast to planning in conventional narrative-based systems mainly pursuing dramatic interests, planning in virtual world-based pedagogical systems strive to provide realistic experiences in immersed situations. Instead of story plot comprising predetermined situations, our inter-event planning method aims at simulating diverse situations that each involve multiple events coupled via their associated agents' conditions and meaningful associations between events occurring in a background world. The specific techniques to realize our planning method include, two-phase planning based on inter-event search and intra-event decomposition (down to the animated action level); autonomous and independent agents to behave proactively with their own belief and planning capability; full-blown background world to be used as the comprehensive stage for all events to occur in; coupling events via realistic association types including deontic associations as well as conventional causality; separation of agents from event roles; temporal scheduling; and parallel and concurrent event progression mechanism. Combining all these techniques, diverse exogenous events can be derived and seamlessly (i.e., semantically meaningfully) integrated with the original event to form a wide scope of situations providing chances of abundant pedagogical experiences. For effective implementation of plan execution, we devise an execution scheme based on multiple priority queues, particularly to realize concurrent progression of many simultaneous events to simulate its corresponding reality. Specific execution mechanisms include modeling an action in terms of its component motions, adjustability of priority for agent across different events, and concurrent and parallel execution method for multiple actions and its expansion for multiple events.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.167-174
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• 2019

Compared to a single nuclear power plant (NPP) risk, the commonalities existing in the multiple NPPs attribute the characteristics of the multi-unit risk. If there is no commonality among the multiple NPPs, there will be no dependency among the risks of multiple NPPs. Therefore, understanding the commonality causing multi-unit events is essential to assessing the multi-unit risk, and identifying the characteristics of the multi-unit risk is necessary not only to select the scope and method for the multi-unit risk assessment, but also to analyze the data of the multi-unit events. In order to develop Korea-specific multi-unit risk assessment technology, we analyze the multi-unit commonalities included in the operational experiences of domestic NPPs. We identified 58 cases of multi-unit events through detailed review of domestic nuclear power plant event reports over the past 10 years, and the multi-unit events were classified into six commonalities to identify Korea-specific characteristics of multi-unit events. The identified characteristics can be used to understand and manage domestic multi-unit risks. It can also be used as a basis for modeling multi-unit events for multi-unit risk assessment.

• Journal of The Korean Astronomical Society
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• v.41 no.6
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• pp.181-186
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• 2008

Recently, Choe & Cheng (2002) have demonstrated that multiple magnetic flux systems with closed configurations can have more magnetic energy than the corresponding open magnetic fields. In relation to this issue, we have addressed two questions: (1) how much fraction of eruptive solar active regions shows multiple flux system features, and (2) what winding angle could be an eruption threshold. For this investigation, we have taken a sample of 105 front-side halo CMEs, which occurred from 1996 to 2001, and whose source regions were located near the disk center, for which magnetic polarities in SOHO/MDI magnetograms are clearly discernible. Examining their soft X-ray images taken by Yohkoh SXT in pre-eruption stages, we have classified these events into two groups: multiple flux system events and single flux system events. It is found that 74% (78/105) of the sample events show multiple flux system features. Comparing the field configuration of an active region with a numerical model, we have also found that the winding angle of the eruptive flux system is slightly above $1.5{\pi}$.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.1
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• pp.71-80
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• 2012

Purpose: This study was conducted to assess stressful life events and quality of life in nursing students. Methods: We performed a cross-sectional research design. A structured questionnaire was designed to collect data on participants' socio-demographics, stressful life events (interpersonal relationship & task-related events) and quality of life (WHOQOL-Bref) in September 2011. Descriptive statistics, t-test, one-way ANOVA, Pearson correlation coefficient and hierarchical multiple regression were applied to data analysis. Results: A total of 135 nursing students were surveyed. The score of the task-related stressful life events was higher than that of the interpersonal stressful life events. Physical domain score ($13.53{\pm}2.33$) was highest but environmental domain score ($12.75{\pm}2.34$) was lowest in quality of life. Satisfaction with campus life affected stressful life events (F=11.82, p<.001) and quality of life (F=17.77, p<.001), and extracurricular activities affect quality of life (t=-2.51, p=.013). Quality of life was negatively associated with task-related stressful life events (r=.-51, p<.001). Multiple regression analyses showed that extracurricular activities, satisfaction with campus life and task-related stressful life events were statistically significant in predicting quality of life with the explanatory power of 40.6%. Conclusion: This study could be a reference to improve the quality of life of nursing students.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Proceedings of the KSR Conference
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• 2007.05a
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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.

• International Journal of Contents
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• v.14 no.4
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• pp.57-64
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• 2018

Among many UNESCO world heritage sites in Korea, "Historic Village: Hahoe" is adjacent to Nakdong River and it is imperative to monitor the water level near the village in a bid to forecast floods and prevent disasters resulting from floods.. In this paper, we propose a recurrent neural network with multiple hidden layers to predict the water level near the village. For training purposes on the proposed model, we adopt the sixth-order error function to improve learning for rare events as well as to prevent overspecialization to abundant events. Multiple hidden layers with recurrent and crosstalk links are helpful in acquiring the time dynamics of the relationship between rainfalls and water levels. In addition, we chose hidden nodes with linear rectifier activation functions for training on multiple hidden layers. Through simulations, we verified that the proposed model precisely predicts the water level with high peaks during the rainy season and attains better performance than the conventional multi-layer perceptron.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.67-78
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• 2020

Typical security solutions such as intrusion detection system are not suitable for detecting advanced persistent attack(APT), because they cannot draw the big picture from trivial events of security solutions. Researches on techniques for detecting multiple stage attacks by analyzing the correlations between security events or alerts are being actively conducted in academic field. However, these studies still use events from existing security system, and there is insufficient research on the structure of the entire security system suitable for advanced persistent attacks. In this paper, we propose an attack path and intention recognition system suitable for multiple stage attacks like advanced persistent attack detection. The proposed system defines the trace format and overall structure of the system that detects APT attacks based on the correlation and behavior analysis, and is designed with a structure of detection system using deep learning and big data technology, etc.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.191-197
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• 2010

In this paper, we propose a novel methodology to identify border gateway protocol (BGP) updates associated with major events - affecting network reachability to multiple ASes - and separate them (statistically) from those attributable to minor events, which individually generate few updates, but collectively form the persistent background noise observed at BGP vantage points. Our methodology is based on principal component analysis, which enables us to transform and reduce the BGP updates into different AS clusters that are likely affected by distinct major events. We demonstrate the accuracy and effectiveness of our methodology through simulations and real BGP data.