• Journal of the Korean Society of Safety
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• v.16 no.3
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• pp.117-124
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• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.585-586
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• 2004

• Journal of the Korean Society of Physical Medicine
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• v.2 no.1
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• pp.73-84
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• 2007

Purpose : The purpose of this study was to determine risk factors and methods in balance assessment associated with fall in older adults. Methods : This article describes many of the tools that can be used to evaluate the physical parameters associated with fall risk in older adults. Results : Composite ratings of performance(Tinetti balance assessment, Guralnik test battery, Berg balance scale, modified-physical performance test) measures the score compounding the balance measure to determine fall risk. Static balance instruments are composed of FICSIT-4 that measures the ability of maintaining foot positions and CTSIB that measures postural stability. Dynamic balance instrument is composed of functional reach test. To measure walking velocity and mobility, 8-foot up-and-go test and walking around two cones are used. We can use 1-RM and to measure muscular strength, isokinetic dynamometery, and 30-second chair stand to measure lower extremity muscle strength. Conclusion : The described instruments are easy to use and widespread. To select and use these tool kits carefully is considered to be helpful in identifying those who are most likely to fall. The final part of the article includes a brief discussion of the potential role of exercise training interventions to improve these physical parameters and prevent falls.

• Safety and Health at Work
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• v.12 no.4
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• pp.462-470
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• 2021

Background: Fatigue is pervasive, under-reported, and potentially deadly where flight operations are concerned. The aviation industry appears to lack a standardized, practical, and easily replicable protocol for fatigue risk assessment which can be consistently applied across operators. Aim: Our paper sought to present a framework, supported by real-world data with subjective and objective parameters, to monitor aircrew fatigue and performance, and to determine the safe crew configuration for commercial airline operations. Methods: Our protocol identified risk factors for fatigue-induced performance degradation as triggers for fatigue risk and performance assessment. Using both subjective and objective measurements of sleep, fatigue, and performance in the form of instruments such as the Karolinska Sleepiness Scale, Samn-Perelli Crew Status Check, Psychomotor Vigilance Task, sleep logs, and a wearable actigraph for sleep log correlation and sleep duration and quality charting, a workflow flagging fatigue-prone flight operations for risk mitigation was developed and trialed. Results: In an operational study aimed at occupational assessment of fatigue and performance in airline pilots on a three-men crew versus a four-men crew for a long-haul flight, we affirmed the technical feasibility of our proposed framework and approach, the validity of the battery of assessment instruments, and the meaningful interpretation of fatigue and work performance indicators to enable the formulation of safe work recommendations. Conclusion: A standardized occupational assessment protocol like ours is useful to achieve consistency and objectivity in the occupational assessment of fatigue and work performance.

• Journal of the Korean Society of Safety
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• v.26 no.1
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• pp.49-57
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• 2011

A questionnaire study was carried out to understand the status of performing the risk assessment of work-related musculoskeletal disorders(WMDSs), which is the employers' legal responsibility when the employees are involved in doing tasks with risk factors. Employers or managers from 340 companies and the representative employees from 250 companies participated in the survey. According to the participated employers and employees, 35.0~46.2% of companies had performed the first risk assessment before the end of 2005. However, it is presumed that the real condition might not be as much as the result, because most companies were very reluctant to took part in the survey. It was found that the type of business and size of the company are the main factors affecting the performance of the risk assessment in terms of the performing ratio, method, and so on. The participants were positive in the thought that the assessment would be helpful in preventing msuculoskeletal disorders, while there was a little difference between the employers and employees in the thought that the assessment would be helpful in finding the injuries in the early stage. It was found that it is necessary to modify and improve the definition and criteria of the tasks to be examined in the assessment.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.39-55
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• 2017

Preeminent performance of construction industry are unattainable with poor productivity resulting in time and cost over runs. Enhancement in productivity cannot be achieved without identifying and analyzing factors that adversely affect productivity. The objective therefore is to propose a productivity analysis model to quantify the probability of effect of factors influencing productivity by using fuzzy logic incorporated with relative importance index method, for various types of construction projects. To achieve this objective, a questionnaire survey was carried out targeting respondents of Indian construction industry, from four distinct projects, namely, residential, commercial, infrastructure and industrial projects. Based on questionnaire administered, the relative importance and ranks of factors demonstrated using relative importance index method. Probability assessment model to analyze productivity was then developed by using Fuzzy Logic Toolbox of MATLAB. The applicability of the proposed model was tested in seven construction projects and the probability of impact of factors on productivity evaluated. The results of application of model in the construction firms infers that the most contributing factor groups for most of the projects were discerned to be manpower, motivation and time group.

• Journal of the Korea Institute of Building Construction
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• v.14 no.4
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• pp.369-378
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• 2014

A system to analyze, assess and manage the health performance of resources and spaces throughout the project lifecycle shall be established to ensure sustainable healthy buildings. Decisions made in the planning, design, construction, and operation and management (O&M) phases must help sustain the health performance of buildings at the level specified by clients or the relevant laws. For this reason, it is necessary to develop a model to ensure the consistent management of performance, as such performance varies according to the decisions made by project participants in each phase. The purpose of this research is to develop a Lifecycle Health Assessment Model (LHA) for sustainable healthy buildings. The developed model consists of four different modules: the Health-friendly Resources Database (HRDB) module, which provides health performance data regarding resources and spatial elements; the Lifecycle Health-performance Tree (LHT) module, which analyzes the hierarchy of spatial and health impact factors; the Health Performance Evaluation (HPE) Module; and the Lifecycle Health Management Module, which analyzes and manages changes in health performances throughout the lifecycle. The model helps ensure sustainable health performances of buildings.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.2
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• pp.97-105
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• 2019

PURPOSE: This study examined the factors related to school-based activity performance in school-aged children with spastic cerebral palsy (CP). METHODS: The Gross Motor Function Systems (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS) as functional classifications, and the physical activity performance of the School Function Assessment (SFA) were measured in 79 children with spastic CP to assess the student's performance of specific school-related functional activities. RESULTS: All the function classification systems were correlated significantly with the physical activity performance of the SFA ($r_s=-.47$ to -.80) (p<.05). The MACS (${\beta}=-.59$), GMFCS (${\beta}=-.23$), CFCS (${\beta}=-.21$), and age (${\beta}=-.15$) in order were predictors of the physical activity performance of the SFA (84.8%)(p<.05). CONCLUSION: These functional classification systems can be used to predict the school-based activity performance in school-aged children with CP. In addition, they can contribute to the selection of areas for intensive interventions to improve the school-based activity performance.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.104-118
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• 2003

It has been criticized that conventional human reliability analysis (HRA) methodologies for probabilistic safety assessment (PSA) have been focused on the quantification of human error probability (HEP) without detailed analysis of human cognitive processes such as situation assessment or decision-making which are crticial to successful response to emergency situations. This paper introduces a new human reliability analysis (HRA) methodology, AGAPE-ET (A guidance And Procedure for Human Error Analysis for Emergency Tasks), focused on the qualitative error analysis of emergency tasks from the viewpoint of the performance of human cognitive function. The AGAPE-ET method is based on the simplified cognitive model and a taxonomy of influencing factors. By each cognitive function, error causes or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and influencing mechanism of PIFs on the cognitive function. Then, overall human error analysis process is designed considering the cognitive demand of the required task. The application to an emergency task shows that the proposed method is useful to identify task vulnerabilities associated with the performance of emergency tasks.