• 인지과학
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• 제27권4호
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• pp.541-572
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• 2016

인과 추론은 심리학에서는 물론 최근 베이스 접근법을 취하는 인지과학자들에 의해서도 활발히 연구되고 있다. 본 연구는 인과추론에 대한 대표적 심리학 이론인 힘-확률대비이론(a power probabilistic contrast theory of causality)을 중심으로 인과 추론의 최근 동향을 개관하고자 한다. 힘-확률대비이론에서는, 원인은 결과를 일으키거나 억제하는 힘(power)인데, 이 힘은 특정한 조건하에서 통계적 상관을 통해 파악될 수 있다고 가정한다. 본 논문에서는 이 이론에 대한 초기의 경험적 지지 증거를 먼저 살펴본 다음, 베이스 접근에 기반을 둔 이론과의 쟁점을 명확히 하고, 원인은 맥락에 무관하게 동일하게 작동한다는 인과적 불변성 가정(causal invariance hypothesis)을 중심으로 한 보다 최근의 연구 결과를 소개하고자 한다. 이 연구들은 종래의 통계적 접근법으로는 잘 설명되지 않는 결과를 제시함으로써, 철학, 통계학, 그리고 인공 지능 등과 같은 인접 분야에 인과성에 대한 힘 이론을 진지하게 고려할 것을 촉구하고 있다.

• 산업경영시스템학회지
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• 제23권60호
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• pp.23-36
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• 2000

A probabilistic model of burr formation in face milling of gray cast iron is proposed. During a face milling operation, an irregular pattern of the edge profile consisting of burrs and edge breakouts is observed at the end of cut. Based on the metal cutting theory, we derive a probabilistic model. The operational bayesian modeling approach is adopted to include the relevant theory in the model.

• 한국수학사학회지
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• 제18권4호
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• pp.101-112
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• 2005

에르디쉬(Erdos)는 수학 연구에 자신의 삶 자체를 모두 바친 20세기를 대표하는 세계적인 수학자이다. 그는 딴은 분야에 걸쳐 1500여 편에 이르는 수학 논문을 발표하였을 뿐만 아니라, 수학의 새로운 지평을 연 영향력 있는 수학자였다. 그는 확률이론을 적용하는 독창적인 방법을 제시하여 확률론적 방법론을 창안하였고, 그러한 방법론은 결국 랜덤 그래프 이론의 모태가 되었다. 본 논문은 천재 수학자, 하지만 다른 한편으로는 바보 같은 순수함을 지녔던 헝가리 출신의 수학자 에르디쉬의 삶을 살퍼보며, 21기에서의 그의 삶과 그의 학문적 업적이 지니는 의미와 가치를 생각하여 보고자 한다.

• 논리연구
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• 제5권2호
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• pp.115-131
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• 2002

The problem of disjunctive causal factors is generalized as follows. Suppose that there are no factors of the kind considered so far that need to be held fixed in background contexts. Nevertheless, it is still possible that within the background contexts, each disjunct of a disjunctive causal factor X v W confers a different probability on an effect factor in Question. So a problem arises of how we identify a single causally significant probability of the effect factor in the presence of the disjunctive causal factor, assuming that each disjunct of the disjunctive causal factor confers a different probability on the effect factor. In this paper, I first introduce an experiment in which disjunctive causal factors seem to pose a problem for the theory of probabilistic causation. Second, I show how Eells' solution to the problem of disjunctive causal factors meets the problem that arises in the experiment. Third, I examine Hitchcock's arguments against Eells' solution, arguing that Hitchcock misconstrues Eells' solution, and disregards the feature of the theory of probabilistic causation such that a factor is a causal factor for another factor relative to a population P of a population type Q.

• 한국농공학회논문집
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• 제56권1호
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• pp.11-20
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• 2014

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1047-1050
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• 1993

In radiation protection and nuclear safety, there are many uncertainties or fuzziness due to subjective human judgement. It is desirable to have a theory by which both non-probabilistic uncertainties, or fuzziness, of human factors and the probabilistic properties of machines can be treated consistently. Fuzzy set theory seems to be an effective tool for analyzing the risk and safety of complex man-machine systems such as nuclear power plants.

• Structural Engineering and Mechanics
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• 제74권4호
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• pp.547-557
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• 2020

In this paper, an automatic adaptive mesh refinement procedure is presented for two-dimensional problems on the basis of a new probabilistic error estimator. First-order perturbation theory is employed to determine the lower and upper bounds of the structural displacements and stresses considering uncertainties in geometric sizes, material properties and loading conditions. A new probabilistic error estimator is proposed to reduce the mesh dependency of the responses dispersion. The suggested error estimator neglects the refinement at the critical points with stress concentration. Therefore, the proposed strategy is combined with the classic adaptive mesh refinement to achieve an optimal mesh refined properly in regions with either high gradients or high dispersion of the responses. Several numerical examples are illustrated to demonstrate the efficiency, accuracy and robustness of the proposed computational algorithm and the results are compared with the classic adaptive mesh refinement strategy described in the literature.

• 한국해양공학회지
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• 제19권1호
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• pp.26-32
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• 2005

Recent earthquakes, measuring over a magnitude of 5.0, on the eastern coast of Korea, have aroused interest in earthquake analyses and the seismic design of caisson-type breakwaters. Most earthquake analysis methods, such as equivalent static analysis, response spectrum analysis, nonlinear analysis, and capacity analysis, are deterministic and have been used for seismic design and performance evaluation of coastal structures. However, deterministic methods are difficult for reflecting on one of the most important characteristics of earthquakes, i.e. the uncertainty of earthquakes. This paper presents results of probabilistic seismic hazard assessment(PSHA) of an actual caisson-type breakwater, considering uncertainties of earthquake occurrences and soil properties. First, the seismic vulnerability of a structure and the seismic hazard of the site are evaluated, using earthquake sets and a seismic hazard map; then, the seismic risk of the structure is assessed.

• Korean Journal of Mathematics
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• 제27권3호
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• pp.629-643
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• 2019

In common fixed point problems in metric spaces several versions of weak commutativity have been considered. Mappings which are not compatible have also been discussed in common fixed point problems. Here we consider common fixed point problems of non-compatible and R-weakly commuting mappings in probabilistic semimetric spaces with the help of a control function. This work is in line with research in probabilistic fixed point theory using control functions. Further we support our results by examples.

• Nuclear Engineering and Technology
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• 제46권1호
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• pp.11-26
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• 2014

The analyses carried out within the Seismic Probabilistic Risk Assessments (SPRAs) of Nuclear Power Plants (NPPs) are affected by significant aleatory and epistemic uncertainties. These uncertainties have to be represented and quantified coherently with the data, information and knowledge available, to provide reasonable assurance that related decisions can be taken robustly and with confidence. The amount of data, information and knowledge available for seismic risk assessment is typically limited, so that the analysis must strongly rely on expert judgments. In this paper, a Dempster-Shafer Theory (DST) framework for handling uncertainties in NPP SPRAs is proposed and applied to an example case study. The main contributions of this paper are two: (i) applying the complete DST framework to SPRA models, showing how to build the Dempster-Shafer structures of the uncertainty parameters based on industry generic data, and (ii) embedding Bayesian updating based on plant specific data into the framework. The results of the application to a case study show that the approach is feasible and effective in (i) describing and jointly propagating aleatory and epistemic uncertainties in SPRA models and (ii) providing 'conservative' bounds on the safety quantities of interest (i.e. Core Damage Frequency, CDF) that reflect the (limited) state of knowledge of the experts about the system of interest.