• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4170-4180
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• 2022

The task complexity (TACOM) measure was previously developed to quantify the complexity of proceduralized tasks conducted by nuclear power plant operators. Following the development of the TACOM measure, its appropriateness has been validated by investigating the relationship between TACOM scores and three kinds of human performance data, namely response times, human error probabilities, and subjective workload scores. However, the information reflected in quantified TACOM scores is still insufficient to determine the levels of complexity of proceduralized tasks for human reliability analysis (HRA) applications. In this regard, the objective of this study is to suggest criteria for determining the levels of task complexity based on logistic regression between human error occurrences in digitalized main control rooms and TACOM scores. Analysis results confirmed that the likelihood of human error occurrence according to the TACOM score is secured. This result strongly implies that the TACOM measure can be used to identify the levels of task complexity, which could be applicable to various research domains including HRA.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.376-385
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• 2021

Probabilistic risk assessment (PRA) has improved its elemental technologies used for assessing external events since the Fukushima Daiichi Nuclear Power Station Accident in 2011. HRA needs to be improved for analyzing tasks performed under extreme conditions (e.g., different actors responding to external events or performing operations using portable mitigation equipment). To make these improvements, it is essential to understand plant-specific and scenario-specific conditions that affect human performance. The Nuclear Risk Research Center (NRRC) of the Central Research Institute of Electric Power Industry (CRIEPI) has developed an HRA guide that compiles qualitative analysis methods for collecting plant-specific and scenario-specific conditions that affect human performance into "narratives," reflecting the latest research trends, and models for analysis of tasks under extreme conditions.

• Research in Mathematical Education
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• v.26 no.3
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• pp.253-270
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• 2023

This study aims to investigate the extent to which Korean high school textbooks incorporate opportunities for students to engage in the mathematical modeling process through tasks related to exponential and logarithmic functions. The tasks in three textbooks were analyzed based on the actions required for each stage in the mathematical modeling process, which includes identifying essential variables, formulating models, performing operations, interpreting results, and validating the outcomes. The study identified 324 units across the three textbooks, and the reliability coefficient was 0.869, indicating a high level of agreement in the coding process. The analysis revealed that the distribution of tasks requiring engagement in each of the five stages was similar in all three textbooks, reflecting the 2015 revised curriculum and national curriculum system. Among the 324 analyzed tasks, the highest proportion of the units required performing operations found in the mathematical modeling process. The findings suggest a need to include high-quality tasks that allow students to experience the entire process of mathematical modeling and to acknowledge the limitations of textbooks in providing appropriate opportunities for mathematical modeling with a heavy emphasis on performing operations. These results provide implications for the development of mathematical modeling activities and the reconstruction of textbook tasks in school mathematics, emphasizing the need to enhance opportunities for students to engage in mathematical modeling tasks and for teachers to provide support for students in the tasks.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• Proceedings of the ESK Conference
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• 1994.04a
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• pp.7-18
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• 1994

Many office tasks have been automated by using computers to increase task productivity. The productivity of performing office tasks is dependent not only upon automating the task procedures, but also upon the usability of user interfaces. However, the literature lacks human factors research on evaluating the usability of user interface styles when they are used for performing different office tasks. This study evalu- ated the usability of user interfaces styles when performing various office tasks. User interface styles considered include menu-selection, command language, form fill-in, iconic styles, etc. A task analysis was conducted toclassify representative office tasks. A variety of analysis techniques such as brainstorming, analytic hierarchy process, prototyping, and expert opinions were used to evaluate the usability of the interfaces. The analysis procedures and results are described along with guidelines for selecting user interfaces in terms of subjective preferences and performance measures.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.273-282
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• 2022

MPEG-VCM(Video Coding for Machine) aims to standardize video codec for machines. VCM provides data sets and anchors, which provide reference data for comparison, for several machine vision tasks including object detection, object segmentation, and object tracking. The evaluation template can be used to compare compression and machine vision task performance between anchor data and various proposed video codecs. However, performance comparison is carried out separately for each machine vision task, and information related to performance evaluation of multiple machine vision tasks on a single bitstream is not provided currently. In this paper, we propose a performance evaluation method of a video codec for AI-based multi-tasks. Based on bits per pixel (BPP), which is the measure of a single bitstream size, and mean average precision(mAP), which is the accuracy measure of each task, we define three criteria for multi-task performance evaluation such as arithmetic average, weighted average, and harmonic average, and to calculate the multi-tasks performance results based on the mAP values. In addition, as the dynamic range of mAP may very different from task to task, performance results for multi-tasks are calculated and evaluated based on the normalized mAP in order to prevent a problem that would be happened because of the dynamic range.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.596-605
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• 1994

An efficient measurement and evaluation system for hand tool tasks will provide a practical solution to the problem of designing and evaluating manual tool tasks in the workplace. Few studies on the biomechanical analysis of hand postures and tool handling tasks exist because of the lack of appropriate measurement techniques for hand force. A measurement system for the finger forces and joint angles for analysis of manual tool handling tasks was developed in this study. The measurement system consists of a force sensing glove made from twelve Force Sensitive Resistors and an angle-measuring glove (Cyberglove$\^$TM/, Virtual technologies) with eighteen joint angle sensors. A biomechanical model of the hand using the data from the measurement system was also developed. Systems of computerized procedures were implemented integrating the hand posture measurement system, biomechanical analysis system, and the task analysis system for manual tool handling tasks. The measurement system was useful in providing the hand force data needed for an existing task analysis system used in CTD risk evaluation. It is expected that the hand posture measurement developed in this study will provide an, efficient and cost-effective solution to task analysis of manual tool handling tasks. These tasks are becoming increasingly important areas of occupational health and safety of the country.

• Journal of Digital Contents Society
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• v.9 no.4
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• pp.561-568
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• 2008

Research on the flow shop model has mainly been centered around periodic tasks scheduling. In this paper, we present an algorithm using synthetic utilization that can check the schedulability of aperiodic local tasks and aperiodic end-to-end tasks with precedence relation in the flow shop model. If the scheduling algorithm for aperiodic end-to-end tasks executed in the multiple stage pipeline is applied to the flow shop model, sometimes the actually schedulable tasks are decided to be not schedulable because of the fact that the actually unschedulable tasks are decided to be schedulable. The algorithm presented in this paper solves the problem, and the simulation shows that the schedulability increases 10%.

• Journal of Korean Academy of Nursing Administration
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• v.15 no.2
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• pp.203-215
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• 2009

Purpose: In order to improve resuscitation rate of CPR through providing qualitative nursing while performing CPR in hospital ward and to enhance quality of emergency nursing in the ward, the author embarked on the research as a way of developing ventricular fibrillation protocol about CPR. Method: Collected data were analyzed by using the SPSS/PC14.0 program while the routes of final protocol developed in this research are as follows. Result: Based on analysis results of literature study and CPR electronic hospital records, a total of 22 items and 53 specific contents were confirmed through the gathering of opinions from panels, and the allotment of roles and tasks with a standard of 2 nurses was designated. As the result of specialists' verification on validity, the final protocol composed of a total of 23 items and 45 specific contents was confirmed. At the result of the pertinency evaluation of confirmed protocol, it was evaluated as relatively pertinent with its average score 2.89-3.32. Conclusion: The protocol developed in this research is seen as to contribute to nurses participating in CPR to avoid the overlapping of tasks and to develop CPR through effective teamwork of medical teams by presenting clear roles and tasks.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1068-1074
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• 2009

The progress is widely used as a critical index for successful construction project management. In spite of the importance of progress measurement, the excessive management effort to collect and maintain detailed data has been highlighted as a major barrier to measurement of highly accurate progress. In order to reduce the required workload and to enhance accuracy, several researches have been conducted. These researches can be categorized into two groups. First group focuses on automated data collection utilizing advanced technologies only for limited construction tasks. The second group is a research area where the standard progress measurement methodologies encompassing entire construction tasks are investigated. Topics include the adjusting the level of details, standardizing work processes, and applying flexible WBS. However, the techniques for automated data collection are not fully investigated yet in the second group. Combining these two research areas can provide a solution for more effective progress management in terms of enhancing accuracy and optimizing workload. However, there has been no comprehensive research addressing these two research groups in an integrated manner. In this context, the purpose of this paper is to propose a methodology that identifies the most suitable measurement method and data acquisition technology (e.g., GPS, RFID, etc.) for entire construction tasks of a project. The proposed methodology in this paper will be able to facilitate the selection process of data acquisition technologies for entire construction tasks of a project and to support the overall enhancement of automated progress management.