• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.129-143
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• 2016

Sometimes, we need or try to figure out somebody's thoughts from his or her behaviors such as eye movement, facial expression, gestures, and motions. In safety-critical and complex systems such as nuclear power plants, the inference of operators' thoughts (understanding or diagnosis of a current situation) might provide a lot of opportunities for useful applications, such as development of an improved operator training program, a new type of operator support system, and human performance measures for human factor validation. In this experimental study, a novel method for inference of an operator's thoughts from his or her eye movement data is proposed and evaluated with a nuclear power plant simulator. In the experiments, about 80% of operators' thoughts can be inferred correctly using the proposed method.

• Journal of KIBIM
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• v.12 no.1
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• pp.1-9
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• 2022

Concrete formwork is a crucial component for any construction project. Artificial intelligence offers great potential to automate formwork design by offering various design options and under different criteria depending on the requirements. This study applied image segmentation in 2D formwork drawings to extract sheathing, strut and pipe support formwork elements. The proposed artificial intelligence model can recognize, classify, and extract formwork elements from 2D CAD drawing image and training and test results confirmed the model performed very well at formwork element recognition with average precision and recall better than 80%. Recognition systems for each formwork element can be implemented later to generate 3D BIM models.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2037-2047
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• 2022

The contribution of degraded human performance (e.g., human errors) is significant for the safety of diverse social-technical systems. Therefore, it is crucial to understand when and why the performance of human operators could be degraded. In this study, the occurrence probability of human errors was empirically estimated based on the complexity of proceduralized tasks. To this end, Logistic regression analysis was conducted to correlate TACOM (Task Complexity) scores with human errors collected from the full-scope training simulator of nuclear power plants equipped with analog devices (analog environment). As a result, it was observed that the occurrence probability of both errors of commission and errors of omission can be soundly estimated by TACOM scores. Since the effect of diverse performance influencing factors on the occurrence probabilities of human errors could be soundly distinguished by TACOM scores, it is also expected that TACOM scores can be used as a tool to explain when and why the performance of human operators starts to be degraded.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.137-138
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• 2023

4.16 참사 이후 재난 발생 시 황금시간(Golden Time)에 중요성이 대두되어 초기대응 교육이 사회 전반적으로 확대되었고, 소방청과 지방자치단체들도 황금시간 안에 구조인력들이 빠르게 도착할 수 있도록, 인력과 지부를 확장하고 새로운 시스템들을 도입하고 있다. 이런 상황에 맞추어, 현재 진행되고 있는 초기대응 안전교육 또한 변화가 필요하다. 안전교육의 필요성을 가지는 안전의식을 토대로 하여, 위기상황판단과 표준행동요령등의 가장 기본적인 안전교육에 중심을 두고, 다양하게 발전되는 새로운 시스템을 이용할 수 있는 안전교육을 통해서 재난이 발생할 때 인적·물적 피해를 최소화할 수 있도록 해야 한다.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.101-113
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• 2015

This study aims to prepare countermeasures to prevent and minimize drowning accident in inland waters by examining the current status of inland water safety management in Korea and diagnosing the defects. The defects in current inland water safety management is analyzed in four aspects. First is the legal defect that includes the absence of legislation that directs the inland water safety management. Second is the instructional defect such as the absence of educational program for prevention of inland water accidents and lack of professional water rescue experts. Third is cooperation defect such as dispersed reporting system and lack of private-public partnership in accident response. Fourth is the defect of emergency response ability, professionalism and accident response skills due to the dispersion and overlaps of safety management systems. In order to improve these defects, this study finds the countermeasures based on the survey of water sports professions and users and its analysis as follows: legislation of '(tentatively named) special act for water safety management in inland waters' is suggested in the legal aspects. A development of inland waters safety education program and training of water accident experts are suggested in the instructional aspects. Integrated operational system for water accident management, activation of safety network and re-establishment of private-public partnership are suggested in the cooperation aspects. Systematic and efficient inland water safety management plans such as enhancement of accident response skills and expertise and integrated inland water safety management with fire department-centered system were suggested in the aspects of emergency response ability.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.4
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• pp.87-92
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• 2014

In spite of ICAO's effort to ensure the safety of flight operation by requiring crews, controllers, and other ground aviation staffs to use unified system for units, SI (System International of units), there are still many aircrafts designed, manufactured, and operated based on non-SI units, and many crew training in airline companies are also conducted based on non-SI. Due to this confusion of using different unit systems in international flight operation, many crew members and passengers are exposed to danger. International flights pilots may have confusion while flying different airspaces of different countries that use different unit systems, and this may cause human errors causing accidents and incidents. Due to these reasons, it is needed to establish the standards to reflect non-SI that many countries practically use to SI, which is international standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3608-3626
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• 2021

The detection of bolts is an important task in high-speed train inspection systems, and it is frequently performed to ensure the safety of trains. The difficulty of the vision-based bolt inspection system lies in small sample defect detection, which makes the end-to-end network ineffective. In this paper, the problem is resolved in two stages, which includes the detection network and cascaded classification networks. For small bolt detection, all bolts including defective bolts and normal bolts are put together for conducting annotation training, a new loss function and a new boundingbox selection based on the smallest axis-aligned convex set are proposed. These allow YOLOv3 network to obtain the accurate position and bounding box of the various bolts. The average precision has been greatly improved on PASCAL VOC, MS COCO and actual data set. After that, the Siamese network is employed for estimating the status of the bolts. Using the convolutional Siamese network, we are able to get strong results on few-shot classification. Extensive experiments and comparisons on actual data set show that the system outperforms state-of-the-art algorithms in bolt inspection.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.503-509
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• 2015

In this paper, we designed and tested an ankle joint mechanism for a gait rehabilitation robot. Gait rehabilitation programs are designed to improve the natural leg motion of patients who have lost their walking capabilities by accident or disease. Strengthening the muscles of the lower-limbs and stimulation of the nervous system corresponding to walking helps patients to walk again using gait assistive devices. It is an obvious requirement that the rehabilitation system's motion should be similar to and as natural as the normal gait. However, the system being used for gait rehabilitation does not pay much attention to ankle joints, which play an important role in correct walking as the motion of the ankle should reflect the movement of the center of gravity (COG) of the body. Consequently, we have designed an ankle mechanism that ensures the safety of the patient as well as efficient gait training. Also, even patients with low leg muscle strength are able to operate the ankle joint due to the direct-drive mechanism without a reducer. This safety feature prevents any possible adverse load on the human ankle. The additional degree of freedom for the roll motion achieves a gait pattern which is similar to the normal gait and with a greater degree of comfort.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.95-103
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• 2022

Quick and accurate understanding of the situation in a severe accident is essential for conducting the appropriate accident management and response using the accident diagnosis information. This study employed deep learning technology to diagnose severe accidents through the major safety parameters transferred from a nuclear power plant (NPP) to AtomCARE. After selecting the major accident scenarios to consider, a learning database was established for particular scenarios affiliated with major scenarios by performing a large number of severe accident analyses using MAAP5 code. The severe accident diagnosis technology, which classifies detailed accident scenarios using the major safety parameters from NPPs, was developed by training it with the established database . Verification and validation were conducted by blind test and principal component analysis. The technology developed in this study is expected to be extended and applied to all severe accident scenarios and be utilized as a base technology for quick and accurate severe accident diagnosis.