• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2002년도 춘계 학술발표회 논문집
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• pp.28-35
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• 2002

The probabilistic seismic hazard analysis for engineering needs several active fault parameters as input data. Fault slip rates, the segmentation model for each fault, and the date of the most recent large earthquake in seismic hazard analysis are the critical pieces of information required to characterize behavior of the faults. Slip rates provide a basis for calculating earthquake recurrence intervals. Segmentation models define potential rupture lengths and are inputs to earthquake magnitude. The most recent event is used in time-dependent probability calculations. These data were assembled by expert source-characterization groups consisting of geologists, geophysicists, and seismologists evaluating the information available for earth fault. The procedures to prepare inputs for seismic hazard are illustrated with possible segmentation scenarios of capable fault models and the seismic hazards are evaluated to see the implication of considering capable faults models.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• 한국경영과학회지
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• 제8권2호
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• pp.9-25
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• 1983

A new approach to cross impact analysis using probabilistic system dynamics(PSD) is presented in this article. The previous models using PSD consist of system dynamics models as a basis which are interacting with cross impact analysis (CIA) sectors. In this model, the policy impact analysis part is separated from the CIA sectors and is constituted an independent subsectors of the model. The policy subsector is designed to separate the policy impact and provide feedback both to the system dynamics base model and cross impact analysis sectors. The new technique is applied to the forecasting, assessment and policy formulation of air pollution in Seoul metropolitan area in 2,000. The results show that the new tool consider policy effects more effectively than the previous PSD models.

• Nuclear Engineering and Technology
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• 제41권8호
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• pp.1065-1072
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• 2009

As the use of software increases at nuclear power plants (NPPs), the necessity for including software reliability and/or safety into the NPP Probabilistic Safety Assessment (PSA) rises. This work proposes an application procedure of software reliability growth models (RGMs), which are most widely used to quantify software reliability, to NPP PSA. Through the proposed procedure, it can be determined if a software reliability growth model can be applied to the NPP PSA before its real application. The procedure proposed in this work is expected to be very helpful for incorporating software into NPP PSA.

• 대한기계학회논문집A
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• 제29권1호
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Information Processing Systems
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• 제10권1호
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• pp.145-161
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• 2014

Recently, many large organizations have multiple data sources (MDS') distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done). When the pattern is present in the site, but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore, this process can lose some interesting patterns, which can help the decider to make the right decision. In such situations we propose the application of a probabilistic model in the synthesizing process. An adequate choice for a probabilistic model can improve the quality of patterns that have been discovered. In this paper, we perform a comprehensive study on various probabilistic models that can be applied in the synthesizing process and we choose and improve one of them that works to ameliorate the synthesizing results. Finally, some experiments are presented in public database in order to improve the efficiency of our proposed synthesizing method.

• 한국산학기술학회논문지
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• 제18권4호
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• pp.8-14
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• 2017

전기 장비와 같은 비구조적 요소는 다양한 제반 시설에서 적절한 기능을 수행하는 중요한 역할을 한다. 특정 시설에서 이러한 비구조적 요소 중 일부는 강한 지진 발생이 발생한다고 하더라고 계속적으로 작동해야 한다. 그러나 다양한 이유 중 지진 진동의 불확실성과 전기 장비와 같은 비구조적 요소의 다양성 때문에 지진 진동의 영향으로 인한 각 기계적 손상과 작동 상의 손상을 정의하는 것과 시스템 손상 확률을 결정하는 것은 어려운 일이다. 따라서 비구조적 요소의 특성과 지진의 변화를 고려한 전기 장비의 성능 평가를 위한, 실용이고 효과적인 확률 모델을 개발할 필요가 있다. 이 연구는 비구조적 요소의 동적 거동과 비구조적 요소를 구조물에 구속 시키는 구속 장치의 선형 거동 및 비선형 거동에 대한 이해를 향상 시킬 것이다. 또한, 이 연구는 폭넓고 새로운 지진 강도를 위한 구속된 비구조적 요소의 확률론적 내진 응답 모델을 생성할 것이다.

• 한국방재학회 논문집
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• 제9권2호
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• pp.45-51
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• 2009

지구기후모델을 이용하여 예측된 (1) 물성치와 (2) 현재 및 미래의 표면 에너지 입력상수의 가변성을 고려한 동결 및 융해깊이를 예측하기 위하여 확률론적 접근법이 도입되었다. 확률론적 접근법을 예시하기 위하여 극지방에서의 융해깊이 예측을 고려해보았다. 특히 확률론적 융해깊이 예측을 위하여 몬테카를로 시뮬레이션과 함께 Stefan 공식이 사용되었다. 시뮬레이션 결과는 물성치의 가변성을 보여주었다. 표면 에너지 입력상수와 온도 데이터는 융해깊이를 예측하는데 상당한 불확실성을 야기시킬 수 있다.

• 한국경영과학회지
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• 제3권1호
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• pp.81-91
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• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• Structural Engineering and Mechanics
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• 제71권6호
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• pp.739-749
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• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.