• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.306-312
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• 2017

The purpose of this research is to introduce the technical standard of accident sequence precursor (ASP) analysis, and to propose a case study using the dynamic-probabilistic safety assessment (D-PSA) approach. The D-PSA approach can aid in the determination of high-risk/low-frequency accident scenarios from all potential scenarios. It can also be used to investigate the dynamic interaction between the physical state and the actions of the operator in an accident situation for risk quantification. This approach lends significant potential for safety analysis. Furthermore, the D-PSA approach provides a more realistic risk assessment by minimizing assumptions used in the conventional PSA model so-called the static-PSA model, which are relatively static in comparison. We performed risk quantification of a steam generator tube rupture (SGTR) accident using the dynamic event tree (DET) methodology, which is the most widely used methodology in D-PSA. The risk quantification results of D-PSA and S-PSA are compared and evaluated. Suggestions and recommendations for using D-PSA are described in order to provide a technical perspective.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.141-146
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• 2024

Station blackout (SBO) risk is one of the most significant contributors to nuclear power plant risk. In this paper, the sequence probability formulas derived by the convolution approach are compared with those derived by the conventional event tree/fault tree (ET/FT) approach for the SBO situation in which emergency diesel generators fail to start. The comparison identifies what makes the ET/FT approach more conservative and raises the issue regarding the mission time of a turbine-driven auxiliary feedwater pump (TDP), which suggests a possible modeling improvement in the ET/FT approach. Monte Carlo simulations with up-to-date component reliability data validate the convolution approach. The sequence probability of an alternative alternating current diesel generator (AAC DG) failing to start and the TDP failing to operate owing to battery depletion contributes most to the SBO risk. The probability overestimation of the scenario in which the AAC DG fails to run and the TDP fails to operate owing to battery depletion contributes most to the SBO risk overestimation determined by the ET/FT approach. The modification of the TDP mission time renders the sequence probabilities determined by the ET/FT approach more consistent with those determined by the convolution approach.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.550-558
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• 2018

This paper presents the current limit strategy of voltage controller of delta-connected H-bridge static synchronous compensator (STATCOM) under an unbalanced voltage fault event. When phase to ground fault happens, the feasibility to heighten the magnitude of sagging phase voltage is considered by using symmetric transformation method in delta-structure STATCOM. And the efficiency to cover the maximum physical current limit of switching device is considered by using vector analysis method that calculate the zero sequence current for balancing the cluster energy in delta connected H-bridge STATCOM. The result is simple and obvious. Only positive sequence current has to be used to support the unbalanced voltage sag. Although the relationship between combination of the negative sequence voltage with current and zero sequence current is nonlinear, the more negative sequence current is supplying, the larger zero sequence current is required. From the full-model STATCOM system simulation, zero sequence current demand is identified according to a ratio of positive and negative sequence compensating current. When only positive sequence current support voltage sag, the least zero sequence current is needed.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1998.11a
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• pp.219-224
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• 1998

확률론적 안전성 평가(Probabilistic Safety Assessment PSA)나 정량적인 위험도 평가(Quantitative Risk Assessment: QRA)에서 인간신뢰도분석(human reliability analysis)은 인간행위를 기기처럼 생각하여 전체 시스템의 안전성에 중요한 초기사건(initiating event) 이전이나 초기사건 이후 또는 초기사건을 유발하는 인간행위를 파악하고 정량화하여, 확률론적 평가의 논리구조인 사건 및 고장수목(event tree 및 fault tree)이나 사고경위 단절집합 (accident sequence outsets)에 포함시키는 것이다. (중략)

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.565-570
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• 2013

Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.1
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• pp.83-101
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• 2016

This paper suggests an event-based scenario manager capable of creating and editing a scenario for shipbuilding process simulation based on multibody dynamics. To configure various situation in shipyards and easily connect with multibody dynamics, the proposed method has two main concepts: an Actor and an Action List. The Actor represents the anatomic unit of action in the multibody dynamics and can be connected to a specific component of the dynamics kernel such as the body and joint. The user can make a scenario up by combining the actors. The Action List contains information for arranging and executing the actors. Since the shipbuilding process is a kind of event-based sequence, all simulation models were configured using Discrete EVent System Specification (DEVS) formalism. The proposed method was applied to simulations of various operations in shipyards such as lifting and erection of a block and heavy load lifting operation using multiple cranes.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.34-41
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• 2016

The purpose of this paper is to conduct ETA on six items of ESS: the whole system, battery, BMS, PCS, ESS and cable. To achieve that, ESS work flow and its components are categorized. Based on performance, human, environmental, management, and safety, this paper drew initiation events (IE) and end states (ES). ETA is applied to the main functions of each item, and the end states that may occur in one initiation event are suggested. In addition, detailed classification was performed to induce various end states on the basis of the suggested initiation events ; loss of grid electricity of ESS, loss of battery electricity(DC) of battery, impairment of electric function of BMS, loss of grid electricity(AC) of PCS, loss of data of EMS, Mechanical damage of cable, event sequence analysis conducted on the basis of event trees. If the suggested IEs and ESs are applied on the basis of ESS event cases, it is expected to prevent the same kinds of accident and operate ESS safely.

• 한국연소학회:학술대회논문집
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• 2004.06a
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• pp.133-137
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• 2004

The present study is focused on the small scale turbulent mixing processes in the scalar field. In order to deal with molecular mixing in turbulent flow, the linear eddy model is addressed. In each realization, the molecular mixing term is implemented deterministically, and turbulent stirring is represented by a sequence of instantaneous, statistically independent rearrangement event called by triplet map. The LEM approach is applied with relatively simple conditions. The characteristics of scalar mixing and PDF profiles are addressed in detail.

• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.187-194
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• 2016

A single fire event within a fire area can cause multiple initiating events considered in internal events probabilistic safety assessment (PSA). For an example, a fire event in turbine building fire area can cause a loss of the main feed-water and loss of off-site power initiating events. This fire initiating event could result in special plant responses beyond the scope of the internal events PSA model. One approach to address a fire initiating event is to develop a specific fire event tree. However, the development of a specific fire event tree is difficult since the number of fire event trees may be several hundreds or more. Thus, internal fire events PSA model has been generally constructed by modifications of the pre-developed internal events PSA model. New accident sequence logics not covered in the internal events PSA model are separately developed to incorporate them into the fire PSA model. Recently, many fire PSA models have fire induced initiating event fault trees not shown in an internal event PSA model. Up to now, there has been no analytical comparative study on the constructions of fire events PSA model using internal events PSA model with and without fault trees of initiating events. In this study, the changing process of internal events PSA model to fire events PSA model is analytically presented and discussed.

• Journal of KIISE
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• v.43 no.11
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• pp.1281-1297
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• 2016

In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.