• 한국산업보건학회지
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• 제33권2호
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• pp.230-246
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• 2023

Objectives: The climate crisis has arrived and heat-related illnesses are increasing. It is necessary to discover new high-heat risk industries and understand the environment . It is also necessary to prioritize risks of industries that have not been included in the management target to date. The study was intended to monitor and evaluate the thermal risk of high-priority workplaces. Methods: A prioritization method was developed based on five factors: occurrence of and death due to heat-related illnesses, work environment monitoring, indoor work rate, small heat source, and limited heat dissipation. it, was applied to industrial accidents caused by heat-related illnesses. Wet bulb temperature index and apparent temperature were measured in July and August at 24 workplaces in seven industries and assessed for thermal risk. Results: The wet bulb temperature index was in the range of 23.8~31.9℃, and exposure limits were exceeded in the growing of crops, food services activities and accommodation, and building construction. The apparent temperature was in the range of 26.8~36.7℃, and exceeded the temperature standard for issuing heatwave warnings in growing of crops, food services activities and accommodation, warehousing, welding, and building construction. Both temperature index in growing of crops and building construction were higher than the outside air temperature. Conclusions: In the workplace, risks in industries that have not be controlled and recognized through existing systems was identified. it is necessary to provide break times according to the work-rest time ratio required during dangerous time period.

• 아동학회지
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• 제21권4호
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• pp.177-196
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• 2000

A large sample (1090) of randomly selected early childhood education professionals and government officials rated each of the 133 standards of "A Model for Institutional Accreditation for Early Childhood Education and Care"(Yang, 1999) on a scale of 1 (least important) to 5 (most important). Findings were that all kindergartens and child care centers should be evaluated for accreditation every 3 years with 3-6 months for self-study and on-site validation visits by representatives of the appropritates agencies for 1-2 days. Evaluation results are should be used by institution personnel as a guide to self-supervision, by government officials as a funding standard and by parents as criteria of program quality. Essential accreditation standards included: facilities and equipment; curriculum; nutrition, health and safety; administration and management; and support systems. Safety and teacher-child interactions were most highly rated while parent involvement was not highly rated.

• Smart Structures and Systems
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• 제20권6호
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• pp.759-767
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• 2017

Wear and aging associated damage is a severe problem for safety and maintenance of engineering structures. To acquire structural operational state and provide warning about different types of damage, research on damage identification has gained increasing popularity in recent years. Among various damage identification methods, the Lamb wave-based methods have shown promising suitability and potential for damage identification of plate-type structures. In this paper, a comprehensive study was presented to elaborate four remarkable aspects regarding the Lamb wave-based damage identification method for plate-type structures, including wave velocity, signal denoising, image reconstruction, and sensor layout. Conclusions and path forward were summarized and classified serving as a starting point for research and application in this area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• 한국가스학회지
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• 제21권4호
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• pp.6-15
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• 2017

화학사고의 발생을 감소시키고, 예방하기 위한 공정안전관리(PSM) 제도는 우리나라의 경우 1996년부터 시행되었다. 그러나 PSM 제출대상인 기존 21종 물질에 대한 규정량과 새로이 추가된 브롬화수소 등의 독성물질의 규정량에 대한 타당성 검토가 미흡하여 많은 문제점이 발생되고 있다. 본 연구에서는 25종의 PSM 제출대상독성물질의 규정량을 국내 외 공정안전관리제도와 관련된 규정량과 비교 검토하였다. 그리고 흡입독성, NFPA 지수 등으로 구성된 독성 유해 위험성 식을 제안하여 고위험, 중위험, 저위험의 3등급으로 독성물질을 분류하고, 규정량의 조정에 반영하였다. 본 연구결과의 규정량 개선안은 유사 공정안전관리제도의 규정량 차이로 인한 사업장의 혼란과 부담 완화 및 합리적 개선에 도움을 줄 것으로 기대된다.

• Smart Structures and Systems
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• 제33권2호
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• 한국산업보건학회지
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• 제24권3호
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• pp.310-320
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• 2014

Backgrounds: Endotoxin, which found in the outer membrane of the gram-negative bacteria cell wall, makes up almost all of the lipopolysaccharide(LPS). When people are exposed to endotoxin,it can result in diverse health effects such as an airway irritation and inflammation, fever, malaise, bronchitis, allergic asthma, toxic pneumonitis, hypersensitivity lung disease. Cases among the elderly, children or pregnant can occur more frequently than a healthy adult if they are repeatedly exposed to the existing endotoxin. Therefore, we investigated and assessed the environmental characteristics associated with the airborne endotoxin concentration level in six hospital lobbies. Method: Endotoxin from indoor air in six hospital lobbies was measured by an area sampling method and analyzed according to American Society for Testing and Materials International(ASTM international) E2144-01. Total suspended particulate(TSP), carbon dioxide($CO_2$), temperature and humidity were also measured by using direct reading measurements or airborne sampling equipment at the same time. Environmental characteristics were appropriately divided into two or three groups for a statistics analysis. One-way analysis variable(one-way ANOVA) was used to examine a difference of the endotoxin concentration, depending on the environmental characteristics. In addition, only variables with p-value(p<0.25) were eventually designed to the best model by using multiple regression analysis. Results: The correlation analysis result indicated that TSP(p=0.003) and $CO_2$(p<0.0001) levels were significantly associated with endotoxin concentration levels. In contrast, temperature(p<0.068) and humidity(p<0.365) were not associated with endotoxin concentration. Levels of endotoxin concentration were statistically different among the environmental characteristics of Service time(p=0.01), Establishment of hospital(p<0.001), Scale of hospital(p=0.01), Day average people using hospital(p=0.03), Cleaning time of lobby(p=0.05), Season(p<0.001), and Cleaning of ventilation system(p<0.001) according to ANOVA. Finally, the best model(Adjusted R-square=72%) that we designed through a multiple regression test included environmental characteristics related to Service time, Area of lobby, Season, Cleaning of ventilation system, and Temperature. Conclusions: According to this study, our result showed a normal level of endotoxin concentration in the hospital lobbies and found environmental management methods to reduce the level of endotoxin concentration to a minimum. Consequently, this study recognized to be requirement for the management of ventilation systems and an indoor temperature in order to reduce the level of endotoxin concentration in the hospital lobbies.

• 대한인간공학회지
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• 제32권5호
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• pp.429-435
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• 2013

Objective: This study aims to analyze job satisfaction and accident prevention according to job rotation system types in a motor company. Background: The study of a motor company's job rotation system has come to attention with enhancing productivity, preventing musculoskeletal disorders, and improving quality. Method: In this study, a survey was conducted to show job satisfaction rates according to job rotation systems. Also an investigation was done regarding industrial accidents and previous workers who are receiving treatment for musculoskeletal disorder over the last five years. Results: The job rotation system in this study has been carried out by voluntary decision of workers. Out of the job rotation types, the medium rotation complexity type had high job satisfaction whereas in a high or low rotation complexity type, which many workers prefer, led to less number of accidents and days of sick leave. Application: The results of this study are expected to be a fundamental data to job design.

• 한국전기전자재료학회논문지
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• 제32권1호
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• pp.58-63
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• 2019

Lithium-ion batteries used for IT, automobiles, and industrial energy-storage devices have battery management systems (BMS) to protect the battery from abnormal voltage, current, and temperature environments, as well as safety devices like, current interruption device (CID), fuse, and vent to obtain positive temperature coefficient (PTC). Nonetheless, there are harmful to human health and property and damage the brand image of the manufacturer because of smoke, fire, and explosion of lithium battery packs. In this paper, we propose a systematic protection algorithm combining battery temperature, over-current, and interconnection between protection elements to prevent copper deposition, internal short circuit, and separator shrinkage due to frequent and instantaneous over-current discharges. The parameters of the proposed algorithm are suggested to utilize the experimental data in consideration of battery pack operating conditions and malicious conditions.

• Safety and Health at Work
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• 제9권1호
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• pp.42-52
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• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.