• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.533-540
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• 2012

Objective: This paper targets a framework of a hand gesture based interface design. Background: While a modeling of contact-based interfaces has focused on users' ergonomic interface designs and real-time technologies, an implementation of a contactless interface needs error-free classifications as an essential prior condition. These trends made many research studies concentrate on the designs of feature vectors, learning models and their tests. Even though there have been remarkable advances in this field, the ignorance of ergonomics and users' cognitions result in several problems including a user's uneasy behaviors. Method: In order to incorporate compatibilities considering users' comfortable behaviors and device's classification abilities simultaneously, classification-oriented gestures are extracted using the suggested human-hand model and closed-loop classification procedures. Out of the extracted gestures, the compatibility-oriented gestures are acquired though human's ergonomic and cognitive experiments. Then, the obtained hand gestures are converted into a series of hand behaviors - Handycon - which is mapped into several functions in a mobile device. Results: This Handycon model guarantees users' easy behavior and helps fast understandings as well as the high classification rate. Conclusion and Application: The suggested framework contributes to develop a hand gesture-based contactless interface model considering compatibilities between human and device. The suggested procedures can be applied effectively into other contactless interface designs.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.9-16
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• 2013

Objective: The aim of this study is to investigate a trend of human error types observed in a series of verification and validation experiments for an Advanced Control Room(ACR) equipped with Lager Display Panel(LDP), Work Station Flat Panel Display(WS FPD), list type Alarm System(AS), Soft Control(SC) and Computerized Procedure System(CPS). Background: Operator behaviors in a fully computerized control room are quite different from those in a traditional hard-wired control room. Operators in an ACR all together monitor plant status and variables through their own interface system such as LDP and WS FPD, are notified of abnormal plant status through their own list type AS, control the plant through their own SC, and follow the structured procedure through their own CPS whereas operators in a traditional control room only separately do their duty directed by their supervisor. Especially the secondary task such as manipulating the user interface of ACR can be an extra burden to all the operators including the supervisor. Method: The Reason's human error classification method was applied to operators' behavioral data collected from a series of verification and validation experiments where operators showed their plant operational behaviors under a couple of harsh scenarios using the ACR simulator. Results: As operators accustomed to the new ACR system, knowledge or rule based mistakes appearing frequently in the early series of experiments decreased drastically in the latest stage of the series. Slip and lapse types of errors were observed throughout the series of experiments. Conclusion: Education and training can be one of the most important factors for the operators accustomed to the traditional control room to be adapted to the new system and to run the ACR successfully. Application: The results of this study implied that knowledge or rule based mistakes can be reduced by training and education but that lapse type errors might be reduced only through innovative improvement in human-system interface design or teamwork culture design including a new leadership style suitable for ACR.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.9-17
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• 2007

There is no universally agreed classification of human error, nor is there one in prospect. Thus, a taxonomy is usually made for a specific purpose. To seek the types of human errors in the environment of man-machine interface under the railway industry, we develop a cognitive information processing model incorporating the human's mental states. Using the model, this study investigates the types of human errors about the railway workers. Thus, a survey is conducted for railway safety personnel-locomotive engineers, station employees, and train commanders- in Korean railway company. Through the survey that is designed to investigate four types of human errors from the Questionnaires composed of thirty Questions, we analyze the types of human errors related to railway safety according to affiliated offices, operation shifts, age, and working years. Finally, from the insights of the results some guidelines for the railway safety management are presented.

• Korean Journal of Human Ecology
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• v.12 no.1
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• pp.63-73
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• 2003

The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

• Nuclear Engineering and Technology
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• v.55 no.10
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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.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.218.2-218
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• 2001

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.93-100
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• 2008

In this paper, we propose the application of the Human Factors Analysis and Classification System(HFACS) to analyze an aircraft accident data. HFACS is a general human error framework originally developed and tested within the U.S military as a tool for investigating and analyzing the human causes of aviation accidents. It was examined that HFACS reliably accommodate all human causal factors associated with the commercial accidents. We found that the HFACS could be used as a reliable tool for investigating aircraft accidents including a single accident analysis.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.195-200
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• 2014

Occupational fatal injury rate per 10,000 population of Korea is still higher among the OECD member countries. To prevent fatal injuries, the causes of accidents including human error should be analyzed and then appropriate countermeasures should be established. There was an severe converter furnace accident resulting in five people death by chocking in 2013. Although the accident type of the furnace accident was suffocation, many safety problems were included before reaching the death of suffocation. If the safety problems are reviewed throughly, the alternative measures based on the review would be very useful in preventing similar accidents. In this study, we investigated the converter furnace accident by using human error analysis and accident scenario analysis. As a result, it was found that the accident was caused by some human errors, inappropriate task sequence and lack of control in coordinating work by several subordinating companies. From the review of this case, the followings are suggested: First, systematic human error analysis should be included in the investigation of fatal injury accidents. Second, multi man-machine accident scenario analyis is useful in most of coordinating work. Third, the more provision of information on system state will lessen human errors. Fourth, the coordinating control in safety should be performed in the work conducting by several different companies.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.65-74
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• 2021

Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.