• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.111-115
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• 2009

In Korea, the probabilistically developed seismic hazard maps are used with deterministically derived seismic site coefficients in developing the design response spectrum of a specific site. Even though the seismic hazard maps and seismic site coefficients are incompatible, the current design code ignores such incompatibility. If the seismic hazard map and seismic coefficients are both developed in identical probabilistic framework, such problems can be solved. Unfortunately, the available method cannot be use to derive "true" probabilistic site coefficients. This study uses the ground motion time histories, which were developed as the result of a new probabilistic seismic hazard analysis in the companion paper, as input motions in performing one-dimensional equivalent linear site response analyses, from which the uniform hazard response spectra are generated. Another important characteristic of the hazard response spectra are that the uncertainties and randomness of the ground properties are accounted for. The uniform hazard spectra are then used to derive probabilistic site coefficients. Comparison of probabilistic and deterministically site coefficients demonstrate that there is a distinct discrepancy between two coefficients.

• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.534-547
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• 2014

Large-scale failures resulting from natural disasters or intentional attacks are now causing serious concerns for communication network infrastructure, as the impact of large-scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we propose a new framework for the analysis and prevention of network service disruptions in large-scale failure scenarios. We build dynamic deterministic and probabilistic models to capture the impact of regional failures as they evolve with time. A probabilistic failure model is proposed based on wave energy behaviour. Then, we develop a novel approach for preventive protection of the network in such probabilistic large-scale failure scenarios. We show that our method significantly improves uninterrupted delivery of data in the network and reduces service disruption times in large-scale regional failure scenarios.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.262-269
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• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.29-33
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• 2001

사용자에게 적합한 프로그램을 추천하거나 필터링을 수행하는 지능형 방송 단말 응용 소프트웨어 에이전트에서 필수적으로 사용되는 사용자 선호도 (User Preference)를 추출하는 알고리즘을 연구하였다. 시청자 선호도 추출 알고리즘으로는 시청자의 프로그램 장르나 출연 배우 등에 대한 선호도, 프로그램 시청 시간 등에 대한 선호도나 시청 프로그램에 대한 사용자의 인터랙션 습성 분석에 의한 프로그램 선호도 등을 probabilistic framework과 rule-based framework을 근간으로 추출하는 알고리즘 연구에 대한 결과를 본 논문에서 제시한다.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.285-291
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• 2002

This paper proposes the concept and the general framework of the risk-based seismic design. Because earthquakes and the behaviors of structures are very unpredictable, probabilistic seismic design methods have been proposed after deterministic design methods. Considering these changes, we can find that the important point of seismic design is not the structural behavior itself, but the consequence of structural behavior under possible earthquakes. Risk-based seismic design can tell these consequences under any earthquakes. In this paper, structural confidences are considered by using fragility curve, and risk is modeled by failure probability and consequence-property damage cost, casualty cost.

• Steel and Composite Structures
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• v.21 no.1
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• pp.93-107
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• 2016

Seismic fragility analysis is a probabilistic decision-making framework which is widely implemented for evaluating vulnerability of a building under earthquake loading. It requires ingredient named probabilistic model and commonly developed using statistics requiring collecting data in large quantities. Preparation of such a data-base is often costly and time-consuming. Therefore, in this paper, by developing generic seismic drift demand model for regular-multi-story steel moment resisting frames is tried to present a novel application of the probabilistic decision-making analysis to practical purposes. To this end, a demand model which is a linear function of intensity measure in logarithmic space is developed to predict overall maximum inter-story drift. Next, the model is coupled with a set of regression-based equations which are capable of directly estimating unknown statistical characteristics of the model parameters.To explicitly address uncertainties arise from randomness and lack of knowledge, the Bayesian regression inference is employed, when these relations are developed. The developed demand model is then employed in a Seismic Fragility Analysis (SFA) for two designed building. The accuracy of the results is also assessed by comparison with the results directly obtained from Incremental Dynamic analysis.

• Earthquakes and Structures
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• v.15 no.5
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• pp.487-498
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• 2018

Buckling-restrained braces are passive control devices with high level of energy dissipation ability. However, they suffer from low post-yield stiffness which makes them vulnerable to severe ground motions, especially near-field earthquakes. Among the several methods proposed to improve resistance of BRB frames, mega-brace configuration can be a solution to increase frame lateral strength and stiffness and improve distribution of forces to prevent large displacement in braces. Due to the limited number of research regarding the performance of such systems, the current paper aims to assess seismic performance of BRB frames with mega-bracing arrangement under near-field earthquakes via a detailed probabilistic framework. For this purpose, a group of multi-story mega-BRB frames were modelled by OpenSEES software platform. In the first part of the paper, simplified procedures including nonlinear pushover and Incremental Dynamic Analysis were conducted for performance evaluation. Two groups of near-fault seismic ground motions (Non-pulse and Pulse-like records) were considered for analyses to take into account the effects of record-to-record uncertainties, as well as forward directivity on the results. In the second part, seismic reliability analyses are conducted in the context of performance based earthquake engineering. Two widely-known EDP-based and IM-based probabilistic frameworks are employed to estimate collapse potential of the structures. Results show that all the structures can successfully tolerate near-field earthquakes with a high level of confidence level. Therefore, mega-bracing configuration can be an effective alternative to conventional BRB bracing to withstand near-field earthquakes.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.5
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• pp.185-190
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• 2006

The security analysis relates to the ability of the electric systems to survive sudden disturbances such as electric short circuits or unanticipated loss of system elements. It is composed of both steady state and dynamic security analyses, which are not two separate issues but should be considered together. In steady state security analysis including voltage security analysis, the analysis checks that the system is operated within security limits by OPF (optimal power flow) after the transition of a new operating point. On the other hand, dynamic security analysis deals that the transition will lead to an acceptable operating condition. Transient stability, which is the ability of power systems to maintain synchronism when subjected to a large disturbance, is a principal component in dynamic security analysis. Usually any loss of synchronism will cause additional outages. They make the present steady state analysis of the post-contingency condition inadequate for unstable cases. This is the reason of the need for dynamics of systems. Probabilistic criterion can be used to recognize the probabilistic nature of system components and shows the possibility of system security. A comprehensive conceptual framework for probabilistic static and dynamic assessment is presented in this paper. The simulation results of the Western System Coordinating Council (WSCC) system compare an analytical method with Monte-Carlo simulation (MCS). Also, a case study of the extended IEEE Reliability Test System (RTS) shows the efficiency of this approach.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.627-638
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• 2018

This article describes an integrated Level 1-Level 2 probabilistic risk assessment (PRA) methodology to evaluate the radiological risk during postulated accident scenarios initiated during the decommissioning phase of a typical Mark I containment boiling water reactor. The fuel damage scenarios include those initiated while the reactor is permanently shut down, defueled, and the spent fuel is located into the spent fuel storage pool. This article focuses on the integrated Level 1-Level 2 PRA aspects of the analysis, from the beginning of the accident to the radiological release into the environment. The integrated Level 1-Level 2 decommissioning PRA uses event trees and fault trees that assess the accident progression until and after fuel damage. Detailed deterministic severe accident analyses are performed to support the fault tree/event tree development and to provide source term information for the various pieces of the Level 1-Level 2 model. Source terms information is collected from accidents occurring in both the reactor pressure vessel and the spent fuel pool, including simultaneous accidents. The Level 1-Level 2 PRA model evaluates the temporal and physical changes in plant conditions including consideration of major uncertainties. The goal of this article is to provide a methodology framework to perform a decommissioning Probabilistic Risk Assessment (PRA), and an application to a real case study is provided to show the use of the methodology. Results will be derived from the integrated Level 1-Level 2 decommissioning PSA event tree in terms of fuel damage frequency, large release frequency, and large early release frequency, including uncertainties.