• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.259-261
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• 2015

The model for ship maneuvering simulation is to enhance the competence of officer's ship maneuvering skills such as navigation equipment operation, collision avoidances, countermeasure an emergency situation so on. Despite using such ship maneuvering model, critical maritime accidents are occurred periodically in the world. To find adequate simulation model to evaluate competence abilities of deck officer who have maneuvering skills with some competence levels, we search the standard ship maneuvering method representing on the part 3 Person Overboard(POB) to onboard emergences of IAMSAR manual. Moreover we monitor the officer's human factors appearing during education and training and consider the use of human factors as fundamental data.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1897-1902
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• 2008

The computer system is used in many application domains and any system error in these domains may either cause critical loss or threaten environment or human life. Though examples of these domains can be found in many areas, the system, which is used in domains for carrying passengers including rolling stocks in particular, is expected to show satisfactory operation all the time. The relay control logic, which is used in rolling stocks, is complex in hardware and occupies considerably large volume. Nevertheless, it has been used for a long time, to let the system safely operate even in the occurrence of an error in the computer system. However, the relay control logic circuit is so complex that the analysis of proper circuit operation and interlocking tends to be dependent only on the designer's experiences instead of being systematically performed. Especially, the analysis following a change, addition and deletion of a previous circuit according to the requirements from a source of demand is significantly limited. In this paper, the accuracy of relay control logic is verified by the use of properties of Petri Net model. In addition, how main circuit breaker (MCB) control circuit is modeled and analyzed by the design methodology is shown.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.254-259
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• 1995

Large scale display panel(LSDP) is a main component in the next generation main control rooms. LSDP is located at the front of VDU-based operator's workstation and plays an important role in providing operators with overall information of plant status through mimic diagram, text/digit, graph, and so on. A critical matter determined at the first stage of LSDP design is how much information is displayed, because the information density of LSDP affects operator's performance. Many human factors guidelines recommend low information density of displays to avoid degrade of operator's performance, but doesn't provide a useful limit of information density. In this paper, we considered information density as the number of plant parameters and investigated the proper number of plant parameters through a human factors experiment. The experiment with 4 subjects was carried out and response time, error, and heart rate variation as criterion measures were recorded and analyzed. As the results, it is identified that the proper number of parameters in a LSDP is about thirty.

• Journal of Biomedical Engineering Research
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• v.37 no.1
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• pp.31-38
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• 2016

For the patients with bladder dysfunction, measurement of urine volume inside the bladder is very critical to avoid bladder failure. In measuring urine volume inside a bladder, low-resolution 3D ultrasound images are widely used. However, urine volume estimation from 3D ultrasound images is prone to big errors and inconsistency because of low spatial resolution and low signal-to-noise ratio of ultrasound images. We developed a new robust volume estimation algorithm which is not computationally expensive. We tested the algorithm on a lab-built ultrasound bladder phantom and volunteers. The average error rate of the human bladder volume estimation was 5.9% which was better than the commercial machine.

• Safety and Health at Work
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• v.5 no.3
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• pp.158-160
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• 2014

Investigation and analysis of accidents are critical elements of safety management. The over-riding purpose of an organization in carrying out an accident investigation is to prevent similar accidents, as well as seek a general improvement in the management of health and safety. Hundreds of workers have suffered injuries while installing, maintaining, or servicing machinery and equipment due to sudden re-energization of power lines. This study presents and analyzes two electrical accidents (1 fatal injury and 1 serious injury) that occurred because the power supply was reconnected inadvertently or by mistake.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of the Korean Society for Railway
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• v.16 no.4
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• pp.331-339
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• 2013

This study highlights the theme of human error emerging as a critical issue in the railroad industry, conducting exploratory research on the effects of health, cognition, and safety climates on safety behavior and accidents using an empirical method. The statistical results based on questionnaires received from 204 train drivers indicate that psychological fatigue, cognitive failure, and internal locus of control as individual variables and CEO philosophy and behavior of immediate boss as organizational variables have significant relationships with safety behavior, while cognitive failure, CEO philosophy, behavior of immediate boss, and education were found to be significant variables with respect to accidents. Furthermore, unsafe behavior such as mistakes and violations showed negative effects on near misses and responsibility accidents, respectively. Based on these results, effective alternatives and countermeasures needed to mitigate human error were posited.

• Journal of the Ergonomics Society of Korea
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• v.35 no.3
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• pp.143-153
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• 2016

Objective: The purposes of this study are to survey needs for changing accident investigation from blaming to systems approach and to briefly summarize systems-based accident analysis techniques. Background: In modern complex socio-technical systems, accidents are caused by a variety of contributing factors including human, technical, organizational, social factors, not by just a single violation or error of a specific actor, but accidents investigation used to be focused on the incorrect action of individuals. A new approach investigating causes of accidents as a symptom of a deficient system is required. Method: This study was mainly based on survey of literatures related to accidents, accidents investigation, which included academic journals, newspapers, etc. Results: This study showed that accidents investigation of Korea focusing on blaming is problematic. This was confirmed by two concepts of migration and hindsight bias frequently found in accident causation studies, and an attribute of accidents having varying causes. This was illustrated with an example of Sewol ferry capsizing accident. Representative systems-based accident analysis models including Swiss cheese model, AcciMap, HFACS, FRAM and STAMP were briefly introduced, which can be used in systematic accidents investigations. Finally, this study proposed a procedure for establishing preventive measures of accidents, which was composed of two steps: public inquiry and devising preventive measures. Conclusion: A new approach considering how safety-critical components such as technical and social elements, and their interactions lead to accidents is needed for preventing reoccurrence of similar accidents in complex socio-technical systems. Application: The results would be used as a reference or guideline when the safety relevant governmental organizations investigate accidents.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.