• 제목/요약/키워드: Causal Relations

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전자장비 고장진단 질의응답을 위한 인과관계 정의 및 추출 (Definition and Extraction of Causal Relations for Question-Answering on Fault-Diagnosis of Electronic Devices)

  • 이신목;신지애
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제35권5호
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    • pp.335-346
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    • 2008
  • 온톨로지의 인과관계는 특정 응용을 위한 추론에서 중요한 역할을 하므로, 인과관계는 응용에서 쓰이는 추론의 형태에 근거하여 정의되어야 한다. 본 논문에서는, 전자장비의 고장진단 질의응답을 위한 온톨로지에서의 인과관계를 정의하고 추출하는 모델을 제시한다. 질의응답의 패턴을 분석하여 인과범주를 정의하고, 질의응답에서 나타나는 개념들 사이의 관계들 중 인과범주에 속하는 경우를 인과관계로 정의한다. 인과관계 인스턴스는 응용분야의 정의문으로부터 어휘 패턴을 이용하여 추출되고 시소러스 정보를 이용하여 점진적으로 확장된다. 분야 전문가들의 평가 결과, 본 모델은 관계분류에 있어서 92.3%의 평균 정확률과 추출 단계의 인과관계 인식에 있어서 80.7%의 정확률을 보인다.

BSC 성과측정지표간의 인과관계에 관한 연구 - C대학병원 사례 중심으로 - (A Study on Causal Relations among BSC Performance Measurement Indexes - Focused on the case of C University Hospital -)

  • 신승권
    • 산학경영연구
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    • 제20권2호
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    • pp.119-133
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    • 2007
  • 본 연구는 경영혁신의 한 기법으로 BSC를 비영리기업인 대학병원에 도입할 경우 영리기업에서와 같이 그 효과가 있는지를 검증하고자 한다. 대학병원 사례에 의한 BSC 성과측정지표간의 인과관계를 알아보기 위하여 구조방정식 모델을 이용하여 분석하였다. BSC 성과측정지표간의 인과관계를 분석한 결과 모두 통계적으로 유의한 것으로 나타나 연구가설이 모두 채택되었다. 향후에는 실제 재무자료를 학습 및 성장 관점, 내부프로세스 관점, 고객 관점과 연계하여 BSC 성과측정지표간의 인과관계를 연구할 필요가 있다.

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범주기반 속성추론: 인과관계 강도의 검증 (Category-Based Feature Inference: Testing Causal Strength )

  • 조준형;이형철;김신우
    • 감성과학
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    • 제26권1호
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    • pp.55-64
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    • 2023
  • 본 연구는 범주속성들이 공통원인 혹은 공통효과 인과 네트워크로 연결되었을 때 인과강도에 따른 속성추론을 검증했다. 인과범주에서 속성추론을 검증한 기존 연구들은 인과관계의 방향, 연결된 속성의 개수, 원인 혹은 결과의 여부 등에 따라 고유한 추론 패턴이 나타남을 보여주었다. 다만 기존 연구들은 인과관계에 따른 추론패턴을 주로 탐색했으며 인과관계의 효과가 인과강도에 따라 어떤 변화를 보이는지 확인한 연구는 찾아보기 어렵다. 본 연구에서는 공통원인(실험 1), 공통효과(실험 2) 네트워크에서 인과강도에 따른 속성추론을 검증했다. 이를 위해 참가자들에게 속성들이 인과적 관련성을 가지는 범주를 학습하게 한 다음 속성추론 과제를 실시하도록 했다. 실험 결과 인과관계 뿐만 아니라 인과강도 역시 속성추론에 중요한 영향을 미쳤다. 인과강도가 강할 떄 공통원인 속성에 대해서는 추론이 약해진 반면 공통효과 속성에 대해서는 추론이 강해졌다. 또한 인과강도가 강할 때 공통원인이 존재하는 경우 결과속성들에 대한 추론이 강해진 반면 공통효과에서는 반대의 결과가 나타났다. 특히 공통효과에서는 인과강도가 강할 때 인과적 절감이 더 뚜렷하게 나타났다. 이 결과들은 인과적 범주에서의 속성추론에서 참가자들은 인과관계 뿐만 아니라 인과강도를 고려한다는 것을 일관성있게 보여준다.

해양사고 예방을 위한 사전학습 언어모델의 순차적 레이블링 기반 복수 인과관계 추출 (Sequence Labeling-based Multiple Causal Relations Extraction using Pre-trained Language Model for Maritime Accident Prevention)

  • 문기영;김도현;양태훈;이상덕
    • 한국안전학회지
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    • 제38권5호
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    • pp.51-57
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    • 2023
  • Numerous studies have been conducted to analyze the causal relationships of maritime accidents using natural language processing techniques. However, when multiple causes and effects are associated with a single accident, the effectiveness of extracting these causal relations diminishes. To address this challenge, we compiled a dataset using verdicts from maritime accident cases in this study, analyzed their causal relations, and applied labeling considering the association information of various causes and effects. In addition, to validate the efficacy of our proposed methodology, we fine-tuned the KoELECTRA Korean language model. The results of our validation process demonstrated the ability of our approach to successfully extract multiple causal relationships from maritime accident cases.

가정외보호 아동의 양육자 관계와 교우관계의 상호 영향: 자기회귀교차지연모형을 활용한 종단연구 (The Reciprocal Relationship between Caregiver Relations and Peer Relations of Children in Out-of-home Care: Longitudinal Study Using Autoregressive Cross-lagged Modeling)

  • 김담이;강현아
    • 아동복지연구
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    • 제16권2호
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    • pp.109-135
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    • 2018
  • The purpose of this study was to analyze the longitudinal causal relationship between caregiver relations and peer relations of children in out-of-home care. We analyzed the three years(2011-2013) of longitudinal data from the Panel Study on Korean Children in Out-of-Home Care. The autoregressive cross-lagged model (ARCL) was used to measure the longitudinal causal relationship between caregiver relations and peer relations. As a result, first, caregiver relations and peer relations showed stability over time. In other words, the results of the measurement at three time points showed that the caregiver relations and peer relations at the previous time had a significant effect on the caregiver relations and peer relations at the later time point. Second, the previous caregiver relations had a significant effect on the subsequent peer relations over time. Third, the previous peer relations had a significant effect on the subsequent caregiver relations over time. This study confirmed the interrelationships of caregiver relations and peer relations of children in care by examining the longitudinal data using the longitudinal analysis method.

기혼여성의 결혼 불만족과 혼외관계에 대한 탐색적 고찰 (An Exploratory Study on Marital Dissatisfaction and Extramarital Relations among married Women)

  • 공미혜
    • 대한가정학회지
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    • 제40권1호
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    • pp.195-208
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    • 2002
  • This study examines how marital dissatisfaction is connected with extramarital relations among married women. To explore this subject,1 am using in-depth interview techniques. The data from 16 married women who involved (and are involving) extramarital relations are collected in semistructured interviews. In this article, I describe four specific types of extramarital relations with particular relevance to marital dissatisfaction: (1) temporary extramarital relations caused by marital dissatisfaction, (2) positive extramarital relations as maintaining dissatisfied marriage, (3) unavoidable extramarital relations as breaking dissatisfied marriage, and (4) extramarital relations as a part of life with satisfied marriage. With these results, I believe that equity theory could be applicable in explaining the relationships between marital satisfaction and extramarital relations. There are limitations when the qualitative research is analyzed. One problem is measurement. It is difficult to measure equality (or equity), life dissatisfaction, and other concepts. furthermore, this study is not abbe to explain causal relationships among equality, life dissatisfaction, actual extramarital relations. The future study should perhaps be in quantitative research focused on the causal model in which all exchange variables are conceptualized and properly measured for the intimate relationship.

정량 추론과 정성 추론의 통합 메카니즘 : 주가예측의 적용 (A Mechanism for Combining Quantitative and Qualitative Reasoning)

  • 김명종
    • 지식경영연구
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    • 제10권2호
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    • pp.35-48
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    • 2009
  • The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

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구조방정식을 이용한 도시부 4지 신호교차로의 사고원인 분석 (A Causational Study for Urban 4-legged Signalized Intersections using Structural Equation Method)

  • 오주택;이상규;허태영;황정원
    • 한국도로학회논문집
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    • 제14권6호
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    • pp.121-129
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    • 2012
  • PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

이동통신 단말기의 감성만족 요소간 인과관계에 관한 연구 (A Study on Causal Relationships among Sensibility Satisfaction Factors for Mobile Phone)

  • 전영호;백인기;김정일;손기혁
    • 대한인간공학회지
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    • 제22권2호
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    • pp.1-13
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    • 2003
  • In general, causal relationship for theoretical concepts is hypothesized based on precedent studies and tested by a structural equation model. However, when theoretical backgrounds are scarce or absent, the causal relationship is hypothesized operatively by the purpose and scope of research and tested by overall goodness-of-fit indices such as GFI and RMR. Such a causal relationship can't be most appropriate statistically because it is selected as specific relationship from researcher's view among possible causal relationships. Therefore, this study is to propose a procedure for identifying the causal relationship that produces the best GFI among possible causal relationships for theoretical concepts.

동태적 분석 및 설계를 위한 인과지도 작성법의 한계와 개선방안에 관한 연구 (A Study on Theoretical Improvement of Causal Mapping for Dynamic Analysis and Design)

  • 정재운;김현수
    • 한국시스템다이내믹스연구
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    • 제10권1호
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    • pp.33-60
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    • 2009
  • This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

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