• Journal of Korean Academy of Nursing Administration
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• v.23 no.1
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• pp.76-89
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• 2017

Purpose: This study was conducted to examine the experience of nurse in comprehensive nursing care. Methods: Experiential data collected from 9 nurses through in-depth interviews. Participants were selected from nurses working in the comprehensive nursing care unit at general hospital. The main question was "Can you describe your experience in the comprehensive nursing care unit?" All interviews were recorded and transcribed, then analyzed using Colaizzi's method. Results: Nine themes were derived from the analysis: 'Practice nursing care', 'Feel thankful of the client', 'Difficulty in nursing due to absence of patients' guardian', 'Tired of over-demanding patient and distrust of guardian', 'Confusion regarding one's identity as a nurse', 'Not enough to support system', 'Insufficient pre-training for nurse and client', 'Requirement of work establishment for nurse and nurse aid', 'Concerns about low rewards and high safety accidents'. Conclusion: As a comprehensive nursing service, the nurses provided total patient care, and patient satisfaction and expression of appreciation increased. However, disadvantages were identified, such as patients' excessive needs, communication difficulties, lack of support systems, low compensation, and a high number of safety accidents. Therefore, systematic comprehensive nursing will be achieved if these shortcomings are addressed.

• Smart Structures and Systems
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• v.29 no.4
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.21-29
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• 2024

The effective management of damage in tunnels is crucial for ensuring their safety, longevity, and operational efficiency. In this paper, we propose an educational management model tailored specifically for addressing damage in tunnels, utilizing numerical solution techniques. By leveraging advanced computational methods, we aim to develop a comprehensive understanding of the factors contributing to tunnel damage and to establish proactive measures for mitigation and repair. The proposed model integrates principles of tunnel engineering, structural mechanics, and numerical analysis to facilitate a systematic approach to damage assessment, diagnosis, and management. Through the application of numerical solution techniques, such as finite element analysis, we demonstrate the efficacy of the proposed model in simulating various damage scenarios and predicting their impact on tunnel performance. Additionally, the educational component of the model provides valuable insights and training opportunities for tunnel management personnel, empowering them to make informed decisions and implement effective strategies for ensuring the structural integrity and safety of tunnel infrastructure. Overall, the proposed educational management model represents a significant advancement in tunnel management practices, offering a proactive and knowledge-driven approach to addressing damage and enhancing the resilience of tunnel systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.5
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• pp.1265-1272
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• 2015

This study aims at comparison and analyzing of marine officer license system for fishing vessels between South Korea and New Zealand. It is urgently required to establish Republic of Korea-New Zealand mutual certification system for marine officers who are on board ships within applicable area given that New Zealand will force foreign fishing vessels within New Zealand area to reflag from 2016 in accordance with the amendment of Fisheries Act. Secondly, to compare and analyze systems between two countries will contribute to the preparatory work related to ratification STCW-F convention as New Zealand already have completed law amendment to adapt the convention. Maritime law of New Zealand, Seafarers Act and Ship Personnel Act of Republic of Korea were compared and analyzed as references. The result showed that an improvement to corresponding level to the international convention and development of safety training by vessel type, and job descriptions according to the license class are needed to Republic of Korea system. Furthermore, it is suggested to prepare specialized training for deckhands as required in STCW-F convention and standard fishing vessel officer training record for designated institute of education. Therefore institutional complementarity and framework is required as it is expected that the nations of fishing in piscary demand to reflag Korean deep-sea fishing vessels or to ratify the STCW-F convention.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.87-94
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• 2016

Recently, as Korean industrial structure is moving to the service job, the number of workers engaged in the service job is increasing slowly. According to the statistics by Ministry of Employment and Labor announced in June, 2013, the number of service job workers in Korea was 7,477,135 which accounted for 48.4% of total workers. The trend of this service job is expected to increase continuously in the future. According to the 2013 statistics by Ministry of Employment and Labor, the number of industrial accidents victims of industrial accidents in the service job was 30,526 which was the biggest number among the entire businesses. The victims in the service job accounted for 33.2% among the total number of industrial accidents and represented more than those in the manufacture and construction industry. The service job had various works and employment patterns and most service jobs are petty and are small-sized establishments and it is difficult to try voluntarily to prevent the industrial accidents. However, Korean occupational safety and health act was enacted in accordance with the construction and manufacture in which industrial accidents occurred frequently in the past. The support of the government for the industrial accident prevention is focused on the construction and manufacture. Therefore, the current service job is placed on the blind spot of the safety management. Raising the safety awareness of workers through the safety education is the most important in order to prevent the industrial accidents of the service job with many conventional/repeated disasters such as the conduction by a simple mistake. Accordingly, this study analyzed the features and accidents of the domestic service jobs through the literature survey and analyzed the institutional devices for the safety management of the domestic service job, and the safety management cases of foreign service jobs and compared with domestic systems. Considering demands for the basic safety education for service job workers, a questionnaire was conducted targeting the service job workers and the execution plan of the basic safety & health education targeting the service job workers was carried out through the brainstorming of trainers of worker in the service job.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.232-238
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• 2011

Hazard analysis provides the basic foundation for system safety. Hazard analysis is performed to identify hazards, hazard effects, and hazard causal factors. Hazard analysis is used to determine system risk, to determine the significance of hazards, and to establish design measures that will eliminate or mitigate the identified hazards. Hazard analysis is used to systematically examine systems, subsystems, facilities, components, software, personnel, and their interrelationships, with consideration given to logistics, training, maintenance, test, modification, and operational environments. This paper present hazard analysis techniques which is commonly used in railway signalling, comparised their benefits and limitations.