• Journal of the Korean Society of Safety
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• v.38 no.5
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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.

• International Journal of Highway Engineering
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• v.14 no.6
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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.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.335-346
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• 2008

Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• The Journal of Korean Academic Society of Nursing Education
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• v.11 no.2
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• pp.161-168
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• 2005

Purpose: The purposes are to identify the factors that influence work-based stressor experienced by clinical nurses and to provide a perceived causal structural model among these factors. Method: Data was collected and analyzed in 2 steps to apply a perceived causal structure : network analysis which was developed by Kelley(1983). Results: 1. The extracted causes from qualitative data were identified 10 categories ; over loaded work, relative feelings of deprived, inefficient duty schedule, negative attitudes of patient, burden of extra affair, inadequate administrative support, negative attitudes of physician, conflict with other personnels in hospital, lack of professional knowledge and skill, nursing service marketing burden. 2. Construction of the perceived causal structural model ; 1) The most central cause is over loaded work and the distal causes were inadequate administrative support, lack of professional knowledge and skill in the systems of causation. 2) The causes that have a number of outgoing link were over loaded work, inadequate administrative support, negative attitudes of physician. 3) The cause that have a number of incoming link was relative feelings of deprived. Conclusion: The network suggests that the first centre cause was related on over loaded work.

• Journal of Korean Society for Quality Management
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• v.39 no.4
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• pp.464-479
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• 2011

The purpose of this study is that analyzing the causal relationship between Infra, Process and Performance of companies which are executing the Supply Chain Quality Management(SCQM) with their subcontractors and partners. Korean Standards Association(KSA) provides the Supply Chain Quality Management Model and Quality Collaboration Index for 4 years, but a few study has investigated the critical variables and their causal relationship to organizational performance. Therefore we examine the SCQM model and related index and choose the quality, human resource and risk management processes for identifying the path to organizational performance. In addition, exploratory factor analysis is conducted for figuring out the major factors among the 3 processes. Structural Equation Model are successively used for determining which characteristics of the infra and processes are the most critical variables to performance. The data was collected from KSA and composed of 52 companies and 346 their partners. The result shows that risk management process has no significant effect on the organizational performance and pre-production process collaboration.

• Journal of the Korean Home Economics Association
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• v.32 no.4
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• pp.193-208
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• 1994

This study investigated the causality of the children's self-perceived competence and related variables(age, sex, socio-demographic variables, family structure, the number of brother, home environmental process variables and peer group environmental variables.) The subjects of this study were 842 children at age five, seven, nine, eleven and thirteen attending kindergartens. elementary schools and junior high schools and their mothers in Seoul. This study employed children's self-perceived competence scales(The Pictorial scales for 5, 7, Qestionnaire for 9, 11, 13) home environment scales and peer group enviornment scales(the Pictorial scales for 5, 7 Qestionnaire for 9, 11, 13) Freqencies one way-ANOVA Pearson's Cronbach's αmultiple regression and path analysis were used for data-analysis. Major findings were as follows: 1. The results of the analysis of causal model showed that the variables that affected cognitive self-perceived competence directly were age, sex, parent's education economic status of the home the number of brother and peer's emotional support 2. The results of the analysis of causal model showed that the variables that affected social self-perceived competence directly were sex, economic status of the home, peer's emotional support and common activity. 3. The results of the analysis of causal model showed that the variables that affected physical self-perceived competence directly were age, sex, peer's emotional support and common activity.

• Journal of The Korean Association For Science Education
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• v.22 no.5
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• pp.1010-1029
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• 2002

The purpose of this research was to survey middle school students' preference for science and its causal factors, so as to analyze the causal relationships between them. Preference for science and its causal factors were defined theoretically, and a theoretical model was constructed to measure them and analyze the causal relationship by structural equation modeling. According to the theoretical model and a pilot test, a questionnaire was developed with three parts; the background information of a respondent, the preference for science, and the causal factors of preference. The questionnaire was administered to one class per grade of randomly selected 8 middle schools from 4 areas across the country, and 819 students' data were collected. Preference for science was defined as a state of mind. It revealed to what extent, and how, one likes science. It consisted of 3 categories - 'emotional response', 'behavioral volition', 'valuational comprehension', and each category was divided into two subcategories. Causal factors affecting the preference for science consisted of three categories - personal, educational and social factors, and each was divided into 2 or 3 subcategories. Middle school students' preference for science was middling as a total. Curiosity about contents of science and valuation of science were high, comparatively, but behavioral volition about science was especially low. Students' responses to the causal factors were relatively high in every educational factor and sociocultural valuation of social factors, but relatively low in socioeconomic rewards of social factors, and especially low in personal factors. The causal relationship about the preference for science was investigated by multiple regression analysis and path analysis, using the structural equation model. Multiple regression analysis about the preference for science and its causal factors revealed important factors. The important factors were personal ability, the personal traits, rewards in school science, and contents of school science in order of magnitude of standardized regression coefficient ${\beta}$. Stepwise regression analysis with each of the subcategories of the preference for science as dependent variables showed what factors were important in each subcategory. According to the result of structural equation modeling, personal factors affected 'emotional response' and 'behavioral volition' directly, and social factors affected 'valuational comprehension' directly. Educational factors affected all categories of the preference for science by influencing not only 'emotional response' and 'valuational comprehension' directly, but also 'behavioral volition' indirectly. The way to promote middle school students' preference for science was suggested, based on the analysis result.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.225-266
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• 1997

A Causal Transition Model of the IT impact on organization is proposed. The model is based upon the premise that the IT impact is a multi-phase, multi-realm phenomenon, and that the IT impact in one organizational realm logically transpires to another realm, thus forming complex causal webs among them. Two exploratory research studies, the one qualitative and the other quantitative, were conductea to validate the model in a setting involving major structural reorganization of the organizations' IT function. The research results provide support for the general theory structure of the model. The findings include: ⅰ) the IT impact manifests on multiple organizational realms, with different degrees of strength, ⅱ) the impact on the realms follow a particular causal transition path among them, and ⅲ) the IT impact manifests on and through the information processing aspect of work. The results, however, indicate that people's perception of the IT impact is strongly mitigated by the IT relevance of work, and that the organization is affected as much by the structural arrangement surrounding and accompanying the IT as by the technology itself, suggesting that the IT impact is an organizational phenomenon as well as a technological phenomenon. The implications of the research results are discussed at the end.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.42-54
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• 2005

This paper has the purpose to find out the causal model between QCC activities and performance. This study consists of four hypotheses. First, QCC teamwork has positive influence on QCC performance. Second, QCC atmosphere has positive influence on QCC performance. Third, QCC autonomy has positive influence on QCC performance. Fourth, this causal model is appropriate for representing the relationship between QCC activities and performance. The results of hypothesis testing are as follows. The first and the fourth hypotheses are adopted. The second and the third hypotheses are rejected. Therefore, QCC teamwork will accelerate QCC activities more than atmosphere and autonomy of QCC.