• Journal of the Korean Society of Safety
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• 제20권2호
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• pp.61-66
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• 2005

This paper presents an human reliability assessment(HRA) for a installation task of the temporary power cable in construction fields. HRA is evolved to ensure that the workers could reliably perform critical tasks such as a process of the temporary power cable. Human errors are extremely commonplace, with almost everyone committing at least some errors every day. The considerable parts of electric shock accidents in the construction field are caused by a series of human errors. Therefore it is required to analyze the human errors contained in the task causing electric shock event, the event tree analysis(ETA) is adopted in this paper, and particularly human reliability was estimated for a installation task of the temporary power cables. It was assumed that the error probabilities of the human actions may be obtained using the technique for human error rate prediction(THERP). The results show that the predominant task on reliability in the cable installation tasks is check-out tasks and the probability causing electric shock by human errors was calculated as $1.0\times10^{-9}$.

• Proceedings of the KSR Conference
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.1204-1208
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• 2007

In order to prevent railway accidents due to human errors which have been recognized to be the most important cause in the railway accidents, human errors should have been controlled based on systematical analysis of the human errors, and countermeasures should be derived to reduce human error probability. Among several factors inducing human errors, task load (or task complexity) is representative. In order to reduce the human error, a systematic analysis should be undertaken to evaluate task load. In this study, task load according to task types of railway worker who are a safety critical staff have been quantitatively analyzed based on NASA-TLX(Task Load Index).

• Journal of the Ergonomics Society of Korea
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• 제30권1호
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• pp.99-108
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• 2011

Almost all companies have paid much attention to the safety management ranging from maintenance to operation even at the stage of designing in order to prevent accidents, but fatal accidents continue to increase throughout the world. In particular, it is essential to systematically prevent such fatal accidents as fire, explosion or leakage of toxic gas at factories in order to not only protect the workers and neighbors but also prevent economic losses and environmental pollution. Though it is well known that accident probability is very low in NPP(Nuclear Power Plants), the reason why many researches are still being performed about the accidents is the results may be so severe. HRA is the main process to make preparation for possibility of human error in designing of the NPP. But those techniques have some problems and limitation as follows; the evaluation sensitivity of those techniques are out of date. And the evaluation of human error is not coupled with the design process. Additionally, the scope of the human error which has to be included in reliability assessment should be expanded. This work focuses on the coincidence of human error and mechanical failure for some important performance shaping factors to propose a method for improving safety effectively of the process industries. In order to apply in these purposes into the thesis, I found 63 critical Performance Shaping Factors of the eight dimensions throughout studies that I executed earlier. In this study, various analysis of opinion of specialists(Personal Factors, Training, Knowledge or Experience, Procedures and Documentation, Information, Communications, HMI, Workplace Design, Quality of Environment, Team Factors) and the guideline for construction of PSF were accomplished. The selected method was AHP which simplifies objective conclusions by maintaining consistency. This research focused on the implementation process of PSF to evaluate the process of PSF at each phase. As a result, we propose an evaluation model of PSF as a tool to find critical problem at each phase and improve on how to resolve the problems found at each phase. This evaluation model makes it possible to extraction of PSF succesfully by presenting the basis of assessment which will be used by enterprises to minimize the trial and error of construction process of PSF.

• Journal of the Korea Safety Management & Science
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• 제16권4호
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• pp.101-111
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• 2014

Aviation safety is increasingly important to secure the safety of the Republic of Korea Air Force (ROKAF). A critical activity for enhancing aviation safety is to analyze an accident throughly and to identify causes that can explain it reasonably. The results of such a systematic accident investigation can be effectively used for improving information displays, task procedures, and training systems as well as for reorganizing team structure and communication control system. However, the current practice of analyzing aviation accidents in ROKAF is too superficial and simple to diagnose them systematically. Additionally, the current practice does not give a full consideration to human factors that have been identified as main causes of most of the aviation accidents. With this issue in mind, this study aims to suggest a new approach to analyzing aviation accidents related to human factors.The proposed method is developed on the basis of several models and frameworks about system safety, human error, and human-system interaction. Its application to forty-two human factors-related accidents, which have occurred in ROKAF during the last ten years, showed that the proposed method could be a useful tool for analyzing aviation accidents caused by human factors.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제39권5호
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• pp.203-206
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• 2013

Statistics is the science of data. As the foundation of scientific knowledge, data refers to evidentiary facts from the nature of reality by human action, observation, or experiment. Clinicians should be aware of the conditions of good data to support the validity of clinical modalities in reading scientific articles, one of the resources to revise or update their clinical knowledge and skills. The cause-effect link between clinical modality and outcome is ascertained as pattern statistic. The uniformity of nature guarantees the recurrence of data as the basic scientific evidence. Variation statistics are examined for patterns of recurrence. This provides information on the probability of recurrence of the cause-effect phenomenon. Multiple causal factors of natural phenomenon need a counterproof of absence in terms of the control group. A pattern of relation between a causal factor and an effect becomes recognizable, and thus, should be estimated as relation statistic. The type and meaning of each relation statistic should be well-understood. A study regarding a sample from the population of wide variations require clinicians to be aware of error statistics due to random chance. Incomplete human sense, coarse measurement instrument, and preconceived idea as a hypothesis that tends to bias the research, which gives rise to the necessity of keen critical independent mind with regard to the reported data.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.896-908
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based on firm technical underpinnings including (but not limited to): (1) how to collect HRA data from available sources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because of these necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy Research Institute independently developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. These systems provide unique frameworks that can be used to secure HRA data from full-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability of these two systems, two papers have been prepared with distinct purposes. The first paper, entitled "SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data", deals with technical issues pertaining to the collection of HRA data. This second paper explains how the two systems are able to inform HRA practitioners. To this end, the process of estimating HEPs is demonstrated based on feed-and-bleed operations using HRA data from the two systems.

• Journal of Korea Multimedia Society
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• 제18권5호
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• pp.583-598
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• 2015

We need many assembly lines to produce industrial product such as automobiles that require a lot of composited parts. Big portion of such assembly line are still operated by manual works of human. Such manual works sometimes cause critical error that may produce artifacts. Also, once the assembly is completed, it is really hard to verify whether of not the product has some error. In this paper, for monitoring behaviors of manual human work in an assembly line automatically, we proposes a realtime hybrid monitoring system that combines 2D vision sensor tracking technique with 3D motion recognition sensors.

• Proceedings of the KSR Conference
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.2040-2044
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• 2008

Recently, many safety measures are developing for the prevention of human error, which is main factors of railway accident. For the efficient management of human factors, many expertise on design, conditions, safety culture and staffing are required. But current safety management activities on safety critical works are focused on training, due to the limited resource and information. In order to establish railway human factors management, a systematic review model is required. Based on system engineering and nuclear industry model, a program review model is proposed in this study. The model includes operating experience review, task analysis, staffing and qualification, human reliability analysis, huma-system interface design, procedure development, training program, verification and validation, implementation and monitoring. Results can be applied for the review of safety measures relating to human factors.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1686-1697
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. Accordingly, HRA community has emphasized the accumulation of HRA data to support HRA practitioners for many decades. To this end, it is critical to resolve practical problems including (but not limited to): (1) how to collect HRA data from available information sources, and (2) how to inform HRA practitioners with the collected HRA data. In this regard, the U.S. Nuclear Regulatory Commission (NRC) and Korea Atomic Energy Research Institute (KAERI) independently initiated two large projects to accumulate HRA data by using full-scale simulators (i.e., simulator data). In terms of resolving the first practical problem, the NRC and KAERI developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. In addition, to inform HRA practitioners, the NRC and KAERI proposed several ideas to extract useful information from simulator data. This paper is the first of two papers to discuss the technical underpinnings of the development of the SACADA and HuREX systems.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.