• 한국지능시스템학회논문지
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• 제17권5호
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• pp.684-690
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• 2007

본 논문은 확률변수들로 이루어진 클러스터의 집합과 확률변수들에 대해 관찰된 데이터가 주어진 상황에서, 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성(directional tendency of conditional dependence in the Bayesian probabilistic graphical model)을 결정하는 방법을 기술한다. 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성을 추정하기 위해 한 클러스터에서 다른 각 클러스터에 가장 가까운 확률변수를 해당 클러스터의 외부연결변수로 결정한다. 외부연결변수들 사이에서의 가장 확률이 높은 조건부 확률적 의존성을 나타내는 방향성 비순환 그래프(directed acyclic graph(DAG))를 찾음으로써, 주어진 클러스터들 사이에 존재하는 조건부 확률적 의존의 방향성을 결정한다. 사용된 방법이 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성을 유의미하게 추정할 수 있음을 실험적으로 보인다.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1107-1116
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• 2014

In this paper, we review recent developments in network analysis using the graph theory, and introduce ongoing research area with relevant theoretical results. In specific, we introduce basic notations in graph, and conditional and marginal approach in constructing the adjacency matrix. Also, we introduce the Marcenko-Pastur law, the Tracy-Widom law, the white Wishart distribution, and the spiked distribution. Finally, we mention the relationship between degrees and eigenvalues for the detection of hubs in a network.

• 복식문화연구
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• 제30권6호
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• pp.842-860
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• 2022

There has been growing attention on the well-being of people with disabilities. The purpose of this study was twofold: (1) to investigate the associations between individuals' socio-demographic and psychological characteristics and clothing expenditure, and (2) to examine the moderated mediation effect of self-efficacy and acceptance of disability on the association between dependency on others and happiness among people with visual impairment. This study was based on secondary analysis of data from the second wave of the 6th Panel Survey of Employment for the Disabled collected by the Employment Development Institute. The results of this study showed that average monthly expenditure on clothing was positively associated with self-efficacy, happiness, and acceptance of disability, while being negatively associated with dependency on others. The results also confirmed that self-efficacy mediated the association between dependency on others and happiness. A conditional direct effect of dependency on others on happiness was found, in which negative associations were significant among people with visual impairment who had low and mean levels of acceptance of disability (but not high levels). In addition, there was a significant conditional indirect effect, in which the indirect and negative effect of dependency on others on happiness via self-efficacy was significant for those with low and average levels of acceptance of disability. These findings support the importance of enhancing the independence and acceptance of disability among people with visual impairment, which ultimately contributes to their happiness.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.110-116
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• 2022

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs. Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated. In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

• Nuclear Engineering and Technology
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• 제51권1호
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• pp.45-53
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• 2019

Thermal-hydraulic passive safety systems (PSSs) are incorporated into many advanced reactor designs on the bases of simplicity, economics and inherent safety nature. Several factors among which are the critical parameters (CPs) that influence failure and reliability of thermal-hydraulic (t-h) passive systems are now being explored. For simplicity, it is assumed in most reliability analyses that the CPs are independent whereas in practice this assumption is not always valid. There is need to critically examine the dependency influence of the CPs on reliability of the t-h passive systems at design stage and in operation to guarantee safety/better performance. In this paper, two multivariate analysis methods (covariance and conditional subjective probability density function) were presented and applied to a simple PSS. The methods followed a generalized procedure for evaluating t-h reliability based on dependency consideration. A passively water-cooled steam generator was used to demonstrate the dependency of the identified key CPs using the methods. The results obtained from the methods are in agreement and justified the need to consider the dependency of CPs in t-h reliability. For dependable t-h reliability, it is advisable to adopt all possible CPs and apply suitable multivariate method in dependency consideration of CPs among other factors.

• 전자공학회논문지A
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• 제33A권2호
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• pp.196-204
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• 1996

This paper presents a new scheduling algorithm, which is the most improtant subtask in the high level synthesis. The proposed algorithm performs scheduling in consideration of resource sharing concept based on characteristics of conditionsla bransches in the intermediate data structure. CDFG (control data flow graph) generated by a VHDL analyzer. This algorithm constructs a conditon graph based on time frame of each operation using both the ASAP and the ALAP scheduling algorithm. The conditon priority is obtained from the condition graph constructed from each conditional brance. The determined condition priority implies the sequential order of transforming the CDFG with conditonal branches into the CDFG without conditional branches. To minimize resource cost, the CDFG with conditional branches are transformed into the CDFG without conditonal brancehs according to the condition priority. Considering the data dependency, the hardware constraints, and the data execution time constraints, each operation in the transformed CDFG is assigned ot control steps. Such assigning of unscheduled operations into contorl steps implies the performance of the scheduling in the consecutive movement of operations. The effectiveness of this algorithm is hsown by the experiment for the benchmark circuits.

• 한국시뮬레이션학회논문지
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• 제19권1호
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• pp.103-111
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• 2010

한국어 처리에서 구문분석기에 대한 요구는 많은 반면 성능의 한계와 강건함의 부족으로 인해 채택되지 못하는 것이 현실이다. 본 연구는 구문분석을 레이블링 문제로 전환하여 성능, 속도, 강건함을 모두 실현한 시스템에 대해서 설명한다. 우리는 다단계 구 단위화(Cascaded Chunking)를 통해 한국어 구문분석을 시도한다. 각 단계에서는 어절별 품사 태그와 어절 구문표지를 자질로 사용하고 CRFs(Conditional Random Fields)를 이용하여 최적의 결과를 얻는다. 58,175문장 세종 구문 코퍼스로 10-fold Cross Validation(평균 10.97어절)으로 실험한 결과 평균 86.01%의 구문 정확도를 보였다. 이 결과는 기존에 제안되었던 구문분석기와 대등하거나 우수한 성능이며 기존 구문분석기가 처리하지 못하는 장문도 처리 가능하다.

• 한국정보처리학회논문지
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• 제1권1호
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• pp.38-49
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• 1994

기존 포트란 프로그램을 벡터 처리 가능한 코드인 벡트란으로 변환시키는데 있어 서 가장 문제가 되는 것은 루프내에서 제어의 분기가 발생하는 경우 조건적 전달이 일어난다는 것이다. 조건적 전단이란 어떤 문장의 실행이 다른 문장내의 변수 값에 의 해 이루어지는 제어 의존성으로, 본 논문은 루프내부의 조건적 제어를 제거하기 위한 알고리즘과 조건적 할당문을 이용하였을 때 그 내부의 복잡한 조건에 대한 단순화 알 고리즘을 제시한다. 이 때 조건적 할당문의 조건은 부울 변수(2-상태)뿐 아니라 3가지 이상의 상태를 나타내는 n-상태변수를 통하여 나타낸다.

• East Asian Economic Review
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• 제27권1호
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• pp.33-60
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• 2023

This paper analyzes the entire distribution of stock market returns/volatility in five emerging markets (ASEAN5) and figures out the conditional distribution of the CHI_EPU index. The aim is to examine the impact of CHI_EPU on the stock returns/volatility density of ASEAN5 markets. It also examined whether changes in CHI_EPU explain returns at higher or lower points (abnormal returns). This paper models the behaviour of stock returns from March 2011 to June 2018 using a non-parametric conditional density estimation approach. The results indicate that CHI_EPU diminishes stock returns and augments volatility in ASEAN5 markets, except for Malaysia, where it affects stock returns positively. The possible reason for this positive impact is that EPU is not the leading factor reducing Malaysian stock returns; but, other forces, such as dependency on other countries' stock markets and global factors, may have a positive impact on stock returns (Bachmann and Bayer, 2013). Thus, the risk of simultaneous investment in Chinese and ASEAN5 stock markets, except Malaysia, is high. Further, the degree of this influence intensifies at extreme high/low intervals (positive/negative tails). The findings of this study have significant implications for investors, policymakers, market agents, and analysts of ASEAN5.

• Journal of Computing Science and Engineering
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• 제9권4호
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.