• Korean Journal of Logic
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• v.17 no.3
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• pp.461-484
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• 2014

This essay suggests a constructive interpretation of probabilities by diagnosing problems of the objective and the epistemic interpretations of probability. According to this interpretation, attributions of the mathematical structure of probability to a given system can be understood as positing constructive theoretical hypotheses showing the relationship among empirical data. The constructive interpretation is applied to comprehend probability claims in the explanation of temporal asymmetrical behaviour of our universe. A new approach interpreting probabilities as constructive theoretical terms enables us to circumvent shortcomings of both objective and subjective interpretation of probability, and appreciate why these interpretations nevertheless appear to be convincing in our case.

• Proceedings of the KSEEG Conference
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• 2003.04a
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• pp.165-168
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• 2003

최근 사면의 안정해석분야에서 자주 사용되고 있는 확률론적 해석 방법은 현장에서 획득 되는 자료들의 분산이 심하고 충분한 양의 자료가 획득되지 못할 경우 자료 내에 포함되는 불확실성과 가변성을 효과적으로 다룰 수 있는 방법 중의 하나로 인식되어 왔다. 그러나 대개 확률론적 해석 방법에서 이용되는 몬테카를로 시뮬레이션기법(Monte Carlo simulation method)은 파괴확률을 산정하기 위하여 수 많은 반복적인 계산과정이 요구되며 따라서 많은 시간과 노력이 필요하다는 단점을 가지고 있다. (중략)

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.309-314
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• 1998

언어학적 단위인 형태소의 특성을 유지하면서 음성인식 과정에 적합한 분리 기준의 새로운 디코딩 단위인 의사형태소를 정의하였다. 이러한 필요성을 확인하기 위해 새로이 정의된 37개의 품사 태그를 갖는 의사 형태소를 표제어 단위로 삼아 발음사전 생성과 형태소 해석에 초점을 두고 한국어 연속음성 인식 시스템을 구성하였다. 각 음성신호 구간에 해당되는 의사 형태소가 인식되면 언어모델을 사용하여 구성된 의사 형태소 단위의 상위 5개 문장을 기반으로 시작 시점과 끝 시점, 그리고 확률 값을 가진 의사 형태소 격자를 생성하고, 음성 사전으로부터 태그 정보를 격자에 추가하였다. Tree-trellis 탐색 알고리즘 기반에 의사 형태소 접속정보를 사용하여 음성언어 형태소 해석을 수행하였다. 본 논문에서 제안한 의사 형태소를 문장의디코딩 단위로 사용하였을 경우, 사전의 크기면에서 어절 기반의 사전 entry 수를 현저히 줄일 수 있었으며, 문장 인식률면에서 문자기반 형태소 단위보다 약 20% 이상의 인식률 향상을 얻을 수있었다. 뿐만 아니라 형태소 해석을 수행하기 위해 별도의 분석과정 없이 입력값으로 사용되며, 전반적으로 문자을 구성하는 디코딩 수를 안정화 시킬 수 있었다. 이 결과값은 상위레벨 언어처리를 위한 입력？으로 사용될 뿐만 아니라, 언어 정보를 이용한 후처리 과정을 거쳐 더 나은 인식률 향상을 꾀할 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• Computational Structural Engineering
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• v.2 no.2
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• pp.93-99
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• 1989

Since 1960's, structural engineers have recognized that the inherent random nature of loadings and materials as well as the imperfect structural analysis may be important factors in the structural safety evaluation. Based on the successful developments of the reliability-based structural analysis and design, the design criteria of the standards are recently developed(or modified) in the light of the probabilistic concepts. To develop the probability - based criteria for the domestic buildings, the probabilistic characteristic of loadings acting on structures should be defined first. In this study, therefore, live load data on apartment buildings have been collected and analyzed in systematic manner, and their probabilistic characteristics have been studied. Based on the results, the lifetime extreme values are computed and compared with current design loads. More rational design loads are suggested, which are more consistent in the probabilistic concepts.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.275-280
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• 2008

A technique for reliability-based design optimization(RBDO) is developed based on the Bayesian approach, which can deal with the epistemic uncertainty arising due to the limited number of data. Until recently, the conventional REDO was implemented mostly by assuming the uncertainty as aleatory which means the statistical properties are completely known. In practice, however, this is not the case due to the insufficient data for estimating the statistical information, which makes the existing RBDO methods less useful. In this study, a Bayesian reliability is introduced to take account of the epistemic uncertainty, which is defined as the lower confidence bound of the probability distribution of the original reliability. In this case, the Bayesian reliability requires double loop of the conventional reliability analyses, which can be computationally expensive. Kriging based dimension reduction method(KDRM), which is a new efficient tool for the reliability analysis, is employed to this end. The proposed method is illustrated using a couple of numerical examples.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.154-158
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• 1989

본 연구는 통제적 모델에 의한 연속 숫자음의 인식에 관한 것으로 4 연속 숫자음을 인식 대상으로하여 실험한다. 시스템은 크게 음향 음성 처리부 및 어휘 해석부 두 부분으로 나뉜다. 음향 음성 처리부에서는 입력 음성으로부터 특정 벡터인 12차의 LPC cepstrum 계수를 구하여, 프레임 레이블링과 소음소 레이블링 (phone labelling)을 한다. 프레임 레이블링인 베이스 분류법을 이용하였으며, 소음소 레이블링은 프레임 레이블과 사후확률 (posteriori probability)로 부터 이루어 졌다. 어휘 해석부분에서는 소음소 단위를 입력으로 받아 음운규칙을 통해 작성된 소음소 망을 거쳐 연속 숫자음 출력을 얻도록 했다. 본실험은 화자 3 명이 발음한 35 개의 4 연속 숫자음을 인식 대상으로 하였으며, 4 연속 숫자음을 평가단위로 80%의 인식율을 얻었고, 각 숫자음의 음절을 단위로 95%의 인식율을 얻어 제시한 알고리즘의 유효성을 입증하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.605-613
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• 2014

Epistemic uncertainty, the lack of knowledge, is often more important than aleatory uncertainty, variability, in estimating reliability of a system. While the probability theory is widely used for modeling aleatory uncertainty, there is no dominant approach to model epistemic uncertainty. Different approaches have been developed to handle epistemic uncertainties using various theories, such as probability theory, fuzzy sets, evidence theory and possibility theory. However, since these methods are developed from different statistics theories, it is difficult to interpret the result from one method to the other. The goal of this paper is to compare different methods in handling epistemic uncertainty in the view point of calculating the probability of failure. In particular, four different methods are compared; the probability method, the combined distribution method, interval analysis method, and the evidence theory. Characteristics of individual methods are compared in the view point of reliability analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.351-359
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• 2015

In order to exactly evaluate the seismic collapse capacity of a structure, probabilistic approach is required by considering uncertainties related to its structural properties and ground motion. Regardless of the types of uncertainties, they influence on the seismic response of a structures and their effects are required to be estimated. An incremental dynamic analysis(IDA) is useful to investigate uncertainty-propagation due to ground motion. In this study, a 3-story steel moment-resisting frame is selected for a prototype frame and analyzed using the IDA. The uncertainty-propagation is assessed with categorized parameters representing epistemic uncertainties, such as the seismic weight, the inherent damping, the yield strength, and the elastic modulus. To do this, the influence of the uncertainty-propagation to the seismic collapse capacity of the prototype frame is probabilistically evaluated using the incremental dynamic analyses based on the Monte-Carlo simulation sampling with the Latin hypercube method. Of various parameters related to epistemic uncertainty-propagation, the inherent damping is investigated to be the most influential parameter on the seismic collapse capacity of the prototype frame.