• 제목/요약/키워드: Latent factor

검색결과 279건 처리시간 0.026초

SEM-based study on the impact of safety culture on unsafe behaviors in Chinese nuclear power plants

  • Licao Dai;Li Ma;Meihui Zhang;Ziyi Liang
    • Nuclear Engineering and Technology
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    • 제55권10호
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    • pp.3628-3638
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    • 2023
  • This paper uses 135 Licensed Operator Event Reports (LOER) from Chinese nuclear plants to analyze how safety culture affects unsafe behaviors in nuclear power plants. On the basis of a modified human factors analysis and classification system (HFACS) framework, structural equation model (SEM) is used to explore the relationship between latent variables at various levels. Correlation tests such as chi-square test are used to analyze the path from safety culture to unsafe behaviors. The role of latent error is clarified. The results show that the ratio of latent errors to active errors is 3.4:1. The key path linking safety culture weaknesses to unsafe behaviors is Organizational Processes → Inadequate Supervision → Physical/Technical Environment → Skill-based Errors. The most influential factors on the latent variables at each level in the HFACS framework are Organizational Processes, Inadequate Supervision, Physical Environment, and Skill-based Errors.

A Study of Validity in Tripartite Model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses (탐색적 확인적 요인 분석을 통한 "과학에 대한 태도" 3요소 모델의 타당도 연구)

  • Lee, Kyung-Hoon
    • Journal of The Korean Association For Science Education
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    • 제17권4호
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    • pp.481-492
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    • 1997
  • The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.

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Collaborative Tag-Based Recommendation Methods Using the Principle of Latent Factor Models (잠재 요인 모델의 원리를 이용한 협업 태그 기반 추천 방법)

  • Kim, Hyoung-Do
    • The Journal of Society for e-Business Studies
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    • 제14권4호
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    • pp.47-57
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    • 2009
  • Collaborative tagging systems allow users to attach tags to diverse sharable contents in social networks. These tags provide usefulness in reusing the contents for all community members as well as their creators. Three-dimensional data composed of users, items, and tags are used in the collaborative tag-based recommendation. They are generally more voluminous and sparse than two-dimensional data composed of users and items. Therefore, there are many difficulties in applying existing collaborative filtering methods directly to them. Latent factor models, which are also successful in the area of collaborative filtering recently, discover latent features(factors) for explaining observed values and solve problems based on the features. However, establishing the models require much time and efforts. In order to apply the latent factor models to three-dimensional collaborative filtering data, we have to overcome the difficulty of establishing them. This paper proposes various methods for determining preferences of users to items via establishing an intuitive model by assuming tags used for items as latent factors to users and items respectively. They are compared using real data for concluding desirable directions.

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Combined RP/SP Model with Latent Variables (잠재변수를 이용한 RP/SP 결합모형에 관한 연구)

  • Kim, Jin-Hui;Jeong, Jin-Hyeok;Son, Gi-Min
    • Journal of Korean Society of Transportation
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    • 제28권4호
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    • pp.119-128
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    • 2010
  • Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.

Bayesian analysis of latent factor regression model (내재된 인자회귀모형의 베이지안 분석법)

  • Kyung, Minjung
    • The Korean Journal of Applied Statistics
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    • 제33권4호
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    • pp.365-377
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    • 2020
  • We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

Calculation of DSM's Latent capacity for arc-furnace Considered with the Same Time Load Factor (동시부하율을 고려한 전기로의 수요관리 잠재량 산출)

  • Son Hak Sig;Kim In Su;Im Sang Kug;Kim Hyeung Jung;Hur Dong Ryol;Kim Jae Chul
    • Proceedings of the KIEE Conference
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    • 대한전기학회 2004년도 하계학술대회 논문집 A
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    • pp.82-84
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    • 2004
  • This paper has calculated DSM's latent capacity through a survey investigating electric arc furnaces in over 2,000 toe companies and related organizations. The latent capacity of DSM calculated with the same time load factor is considered in this paper. The time load factor depends on the probability of each electric arc furnace of the value to work and the consideration of experts and technician's experience. Also, this paper verifies the reliability and application of unposed capacity which compared the old latent capacity of Load Management with KEMCO and KEPCO's Direct Load Control gathering capacity.

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Discovery of Association Rules Using Latent Variables

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2005년도 추계학술대회
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    • pp.177-188
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    • 2005
  • Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

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Discovery of Association Rules Using Latent Variables

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • Journal of the Korean Data and Information Science Society
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    • 제17권1호
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    • pp.149-160
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    • 2006
  • Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

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An Analysis of Traffic Accident Injury Severity for Elderly Driver on Goyang-Si using Structural Equation Model (구조방정식을 이용한 고령운전자 교통사고 인적 피해 심각도 분석 (고양시를 중심으로))

  • Kim, Soullam;Yun, Duk Geun
    • International Journal of Highway Engineering
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    • 제17권3호
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    • pp.117-124
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    • 2015
  • PURPOSES : The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS : To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS : This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including ${\chi}^2/df$, GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS : Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.