• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.53-58
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• 2021

Nuclear power plant's safety against seismic events is evaluated as risk values by probabilistic seismic safety assessment. The risk values vary by the seismic failure correlation between the structures, systems, and components (SSCs). However, most probabilistic seismic safety assessments idealized the seismic failure correlation between the SSCs as entirely dependent or independent. Such a consideration results in an inaccurate assessment result not reflecting real physical phenomenon. A nuclear power plant's seismic risk should be calculated with the appropriate seismic failure correlation coefficient between the SSCs for a reasonable outcome. An accident scenario that has an enormous impact on a nuclear power plant's seismic risk was selected. Moreover, the probabilistic seismic response analyses of a nuclear power plant were performed to derive appropriate seismic failure correlations between SSCs. Based on the analysis results, the seismic failure correlation coefficient between SSCs was derived, and the seismic fragility curve and core damage frequency of the loss of essential power event were calculated. Results were compared with the seismic fragility and core damage frequency of assuming the seismic failure correlations between SSCs were independent and entirely dependent.

• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.69-76
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• 2019

This paper presents a method that identifies the unsafe behaviors at the level of near-misses using image analysis. The method establishes potential collision hazardous area in earthmoving operation. It is implemented using a game engine to reproduce the dangerous events that have been accepted as major difficulty in utilizing computer vision technology to support construction safety management. The method keeps realistically track of the ever-changing hazardous area by reflecting the volatile field conditions. The method opens a way to distinguish unsafe conditions and unsafe behaviors that have been overlooked in previous studies, and reflects the causal relationship which causes an accident. The case study demonstrate how to identify the unsafe behavior of a worker exposed to an unsafe area created by dump trucks at the level of near-misses and to determine the hazardous areas.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.317-325
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• 2018

With the aim of creating new economic values and strengthening national competitiveness, governments and major private companies around the world are continuing their interest in big data and making bold investments. In order to collect objective data, such as news, securing data integrity and quality should be a prerequisite. For researchers or practitioners who wish to make decisions or trend analyses based on objective and massive data, such as portal news, the problem of using the existing Crawler method is that data collection itself is blocked. In this study, we implemented a method of collecting web data by addressing existing crawler-style problems using the cloud service platform provided by Amazon Web Services (AWS). In addition, we collected 'gas safety' articles and analyzed issues related to gas safety. In order to ensure gas safety, the research confirmed that strategies for gas safety should be established and systematically operated based on five categories: accident/occurrence, prevention, maintenance/management, government/policy and target.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.236-250
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• 2019

Objectives: The purpose of this study was to investigate the effect of intensive rehabilitation programs on pain, range of motion (ROM), lumbar muscle strength, core muscle endurance, disability, and depression in patients with traumatic low back injuries and to compare the efficacy of this therapy with that of conventional rehabilitation therapy. Methods: The study was performed with a retrospective medical chart review of patients with traumatic low back injury referred to the rehabilitation center at the Daegu Hospital of the Korean Workers Compensation and Welfare Service. Forty-four patients were allocated to either the conventional rehabilitation group (CRG; n = 22) or the intensive rehabilitation group (IRG; n = 22). The CRG group patients, who received 30-min therapist-supervised physical therapy and modality therapy five times per week for four weeks, were compared with the IRG group patients, who received 60-min therapist-supervised physical therapy, 30-min therapist-patient 1:1 matching rehabilitation therapy, and modality therapy five times per week for four weeks. Outcome measures were a numerical rating scale, ROM, lumbar muscle strength, lumbar core muscle endurance, thickness of lumbar deep focal core muscle (transverse abdominis and lumbar multifidus), Oswestry disability index (ODI), and depression (Korean version patient health questionnaire-9). Results: There were statistically significant improvements after treatment in all outcome measures in both groups (p < 0.05). In the intergroup comparison, NRS scores on the activity and thickness of lumbar deep focal core muscles increased significantly more in the IRG than in the CRG (p < 0.05). There were no statistically significant intergroup differences in NRS scores on resting, ROM except left lateral bending, lumbar muscle strength, core muscle endurance, ODI, and depression. Conclusions: We could confirm the superior effectiveness of an intensive rehabilitation program compared to conventional rehabilitation therapy in patients with traumatic low back injuries.

• The Journal of Korean Obstetrics and Gynecology
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• v.32 no.3
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• pp.73-85
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• 2019

Objectives: The purpose of this study was to analyze efficacy and safety of Korean Medicine treatment for traffic accidents during pregnancy. Methods: We investigated the studies on Korean Medicine treatment for traffic accidents during pregnancy via searching through PUBMED, the Cochrane Library, CNKI, and domestic search engines and a total of 6 studies were selected. Results: The major complaints of traffic accidents during pregnancy were low back pain, neck pain and gastrointestinal symptoms. And there were vaginal bleeding and pruritus vulvae in the complaints. All of the studies were given acupuncture treatment for symptom relief, and chuna, herbal acupuncture and cupping were administered. Herbal medicine was also performed, and the most prescribed herbal medicine was Antaeeum-gamibang. All cases reported as traffic accidents during pregnancy showed a reduction in symptoms, normal pregnancy maintenance and delivery, and no miscarriage were reported. Conclusions: Korean Medicine treatment is an effective and safe treatment option for traffic accidents during pregnancy. Further systematic studies are needed to establish the basis for Korean Medicine treatment for traffic accidents during pregnancy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.77-83
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• 2019

Major causes of industrial accidents include falls and gas leak. The existing safety helmet and smart device combination products are focused on convenience, so the functions to prevent such accidents are insufficient. We developed a smart helmet focusing on fall accident detection and gas leak detection. We also developed management system to manage workers efficiently. Its core function is to detect dangerous conditions of employees, to communicate with managers and to confirm the situations of workers. The effectiveness of the combustible gas measurement capability was verified through experiments. However, since a significant amount of power consumption is founded due to continuous operation of the board and the sensor, countermeasures such as replacing with a large capacity battery are required.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.217-224
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• 2019

The intelligent robot industry is a complex which encompasses all fields of science and technology, and its marketability and industrial impact are remarkable. Major countries in the world have been strengthening their policies to foster the intelligent robot industry, but discussions on liability issues and legal actions that are accompanied by the related big or small accidents are still insufficient. In this study, therefore, the patent law by artificial intelligence robots and the legislation for relevant legal actions at the criminal law level are presented. Patent law legislation by artificial intelligence robots should comply with the followings. First, the electronic human being other than humans ought to be given legal personality, which is the subject of patent infringement. Even if artificial intelligence has legal personality, legal responsibility will be varied depending on the judgment of whether the accident has occurred due to the malfunction of the artificial intelligence itself or due to the human intervention with malicious intention. Second, artificial intelligence as a subject of actors and responsibility should be distinguished strictly; in other words, the injunction is the responsibility of the intelligent robot itself, but the financial repayment is the responsibility of the owner. In the criminal law legislation, regulations for legal punishment of intelligent robot manufacturing companies and manufacturers should be prepared promptly in case of legal violation, by amending the scope of application of Article 47 (Penal Provisions) of the Intelligent Robots Development and Distribution Promotion Act. In this way, joint penal provisions, which can clearly distinguish the responsibilities of the related parties, should be established to contribute to the development of the fourth industrial revolution.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3824-3836
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• 2021

Workers' safety from radiological exposure in a 1 ton/day capacity spent resin treatment facility was evaluated according to the operating times and outflow rate due to process related leakages. The conservative annual dose based on the operating times of the workers exceeded the dose limit by at least 7.38E+01 mSv for close work. The realistic dose range was derived as 1.62E+01 mSv-6.60E+01 mSv. The conservative and realistic annual doses for remote workers were 1.33E+01 mSv and 3.00E+00 mSv respectively, which were less than the dose limit. The MWR was identified as the major contributor to worker exposure within the 1 h period required for removal of radioactive materials. The dose considering both internal and external exposures without APF was derived to be 1.92E+01 mSv for conservative evaluation and 4.00E+00 mSv for realistic evaluation. Furthermore, the dose with APF was derived as 7.27E-01 mSv for conservative evaluation and 1.51E-01 mSv for realistic evaluation. Considering the APF for leakage from all parts, the dose range was derived as 1.25E+00 mSv-2.03E+00 mSv for conservative evaluation and 2.61E-01 mSv-4.23E-01 mSv for realistic evaluation. Hence, it was confirmed that radiological safety was secured in the event of a leakage accident.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.48-54
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• 2021

According to the Ministry of Employment and Labor's statistics on occupational fatalities in South Korea, more than half of the fatalities in the past five years have occurred in the construction industry. The stuck-by and caught-in-between accidents associated with construction equipment is the major source of fatalities from construction sites. In order to prevent such accidents in construction sites, the government has spent lots of efforts including proposing the "special law on construction safety" and encouraging the implementation of new technology for accident prevention. However, numerous accidents are still occurred at construction sites and further efforts are still required. In this manner, this study developed a collision prevention technique that can prevent collision between equipment and worker by recognizing location and type of the nearby objects through ultrasound scanning. The study conducted a pilot experiment and the analysis results demonstrate the feasibility of achieving high performance in both object recognition and location estimation. The developed technique will contribute to prevent collision accidents at construction sites and provide the supplemental knowledge on developing automated collision prevention system for construction equipment.