• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.93-97
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• 2007

As many as 111 reactor trips have occurred for recent 5 years('01-'05), and 26 cases of them have occurred due to human error. The trend of human error rate didn't decrease in 2004, so KHNP started to make efforts to decrease human errors. In 2006 KHNP bench marked excellent foreign nuclear power plants and introduced human error prevention tools. In addition, KHNP created as many as 40 posters for human performance improvement. The posters are based on the about 500 real incident reports collected through K-HPES from 1995. Therror preventive tols for the poster. This paper explains design of the posters and their application.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.19-28
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• 2011

Objective: The aim of this study is to reclassify human errors and to develop hands-on tools to apply the new classification for preventing human error accidents in highway construction site. Background: The main cause of accidents in highway construction was reported as the carelessness of workers. However, such diagnosis could not help us operationally prevent accidents in real workplace. Method: The accidents in highway construction were reanalyzed and the causes of human error were reclassified in order to educate and improve the awareness of human error in highway construction. Field survey and interview with safety managers and workers were conducted to find the causal relationship between the actual accidents and the human errors. Results: The most frequently observed human errors in highway construction were classified into six categories such as mis-perception, distraction, memory fail, slip, cognition error and mis-judgment. In order to provide hands-on tools to increase the awareness of human error in construction field, the human error checklist and card sorting diary were developed. Especially, the card sorting diary was designed to increase the ability in human error inspection of safety manager at construction site. Moreover, posters were developed based on actual accident cases. Conclusion: We suggested that the improved awareness and analytical report on checklist, card sorting diary and posters for construction field could collectively prevent the accident. Application: The classification of human error, hands-on tools and posters can be directly applicable on highway construction site. This analytical and collective approach preventing human error-related accident could be extended to other construction workplaces.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.918-927
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• 2022

The purpose of this study was to investigate the causes of collisions by examining 668 cases of merchant ship collision accidents that occurred during the past 12 years (2010-2021) and analyzed them statistically. Further, the analysis results were applied to propose a human error prevention collision avoidance (HEPCA) model. The statistical annual report of the Korea Maritime Safety Tribunal (KMST) and the collision investigation report were investigated to collect data on the causes of collisions of merchant ships, and frequency analysis was performed using the statistical analysis tool, SPSS Statistics. In the first-stage analysis, the causes of collisions were analyzed targeting 668 merchant ship collision accidents, and in the second-stage analysis, the identified maximum frequency cause factors were analyzed in detail. The analysis results identified that 98 % of the cause of the collision was the human error of the navigator, and the highest frequency was in the order of neglect of look-out > violation of navigation regulations > improper maneuvering. The cause of the neglect of look-out was mainly neglecting continuous monitoring after the first recognition of the target ship. The HEPCA model for human error prevention was proposed by applying the analysis results to the collision case of the investigation report. The results of this study are expected to be used as educational materials at marine navigator educational institutions and in practice for avoiding collisions caused by human errors of navigators.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.144-150
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• 2014

A man is a component of a large system how complex it may be so that human performance which can influence on the system safety should be included in system safety assessment. Meanwhile, human performance can vary over time due to lots of factors. Among them, stress is an indirect factor that may cause human error which can result in industrial accidents. To assess stress level of human workers, not a few assessment tools have been developed. However, it can be questionable to utilize them for human performance anticipation because they were mainly developed with the viewpoint of mental health, though stress assessment can be also required in the safety aspect. Therefore, this study aimed to survey the possibility of their application with safety purpose. About 10 kinds of work stress tools were collected and analyzed with reference to assessment items, assessment and analysis methods, and follow-up measures. The results showed that most tools focused their weights on Demands, Supports, and Relationships, in sequence. However, they, except only one tool developed by the Japanese researchers, merely advised to set up counterplans in PDCA cycle or risk management activities. In consequence, application of stress assessment tools mainly developed for mental health seemed impractical for safety purpose with respect to human performance anticipation so that it was concluded that development of a new assessment tools aimed to human performance variation and accident prevention would be inevitable.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.189-193
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• 2009

As large-scale buildings, skyscrapers, and multi-purpose buildings recently increase in numbers dramatically the internal space of such buildings becomes more and more large and complicated accordingly. The Multi-purpose buildings are increasing rapidly in number, and concerns over the evacuation process in case of fire emergency are also increasing. However, A controller who is responsible for visitor's safety makes a decision about measures for visitor safety in human based decision making process. Many potential accidents that are caused by human error lurk in results of the process. The accidents can be decreased by changing the decision making process from human-based into technology-based. Through this study, the way to improve current disasters prevention system is presented and it can also improve shelter inducement methods effectively. SICS(Spatial Information Control System) which is being studied in other research is used as the tool for improvement.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.7
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• pp.801-809
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• 2017

The purpose of this study is to contribute to the prevention of ship collisions by investigating real ship collision cases and statistically analyzing causes of human error for captains and Officers of the Watch (OOW). This study encompassed a total of 109 cases for 218 vessels, which were suitable for the analysis of ship accidents between merchant ships or merchant ships and fishing boats over the 7 years from 2010 to 2016. Data was collected while classifying vessels according to type, Give-way and Stand-on vessels, along with the cause of human error. Factors causing human error were identified after focusing on the cause of each collision given by the OOW ; frequency and cross tabulation analyses were conducted using SPSS, a statistical analysis tool. As a result, the main causes of human error by an OOW in a ship collision situation were that lookout was neglected in a Give-way vessel including radar surveillance (74.3 %) or continuous observation of an opponent vessel was carried out (17.4 %). A major factor for Stand-on vessels was failure to act to avoid collision with another vessel (63.3 %). In particular, most neglect for lookout type merchant ships occurred after the opponent ship was first observed, and a common cause of lookout neglect and neglect of duty was a focus on other tasks during navigational watch time.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.111-116
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• 2021

Normal Operation Safety Survey (NOSS) is a safety management tool used by air traffic control organizations to measure data related to threats and errors to proactively prevent accidents before they occur. NOSS was developed to be applied to the air traffic control field according to the concept of Line Operation Safety Audit (LOSA) conducted by airlines to reduce human errors in the cockpits. Safety management based on data is crucial for the prevention of control-related accidents, and the competency of observers for this is important for the successful implementation of NOSS. Based on LOSA implementation experiences and expert interviews, this paper introduces the international NOSS methods and discusses the key factors required for effective observation and the implementation methods and implications of NOSS observer training.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.