• Safety and Health at Work
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• v.7 no.1
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• pp.32-37
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• 2016

Background: Previous research has shown that paramedics are exposed to risks in the form of injuries to the musculoskeletal system. In addition, there are studies showing that they are also at increased risk of cardiovascular disease, cancer, and psychiatric diseases, which can partly be explained by their constant exposure to stress. The aim of this study is to evaluate whether the use of shoulder straps decreases physical effort in the form of decreased heart rate and cortisol concentration. Methods: A stretcher with a dummy was carried by 20 participants for 400 m on two occasions, one with and one without the shoulder straps. Heart rate was monitored continuously and cortisol samples were taken at intervals of 0 minutes, 15 minutes, 30 minutes, 45 minutes, and 60 minutes. Each participant was her or his own control. Results: A significant decrease in heart rate and cortisol concentration was seen when shoulder straps were used. The median values for men (with shoulder straps) at 0 minutes was 78 bpm/21.1 nmol/L (heart rate/cortisol concentration), at 15 minutes was 85 bpm/16.9 nmol/L, and at 60 minutes was 76 bpm/15.7 nmol/L; for men without shoulder straps, these values were 78 bpm/21.9 nmol/L, 93 bpm/21.9 nmol/L, and 73 bpm/20.5 nmol/L. For women, the values were 85 bpm/23.3 nmol/L, 92 bpm/20.8 nmol/L, and 70 bpm/18.4 nmol/L and 84 bpm/32.4 nmol/L, 100 bpm/32.5 nmol/L, and 75 bpm/25.2 nmol/L, respectively. Conclusion: The use of shoulder straps decreases measurable physical stress and should therefore be implemented when heavy equipment or a stretcher needs to be carried. An easy way to ensure that staff use these or similar lifting aids is to provide them with personalized, well-adapted shoulder straps. Another better option would be to routinely sewn these straps into the staff's personal alarm jackets so they are always in place and ready to be used.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.381-386
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• 2018

Objectives: This study aimed to describe the process of utilizing a mobile application for ecological momentary assessment (EMA) to collect data on stress and mood in daily life setting. Methods: A mobile application for the Android operating system was developed and installed with a set of questions regarding momentary mood and stress into a smartphone of a participant. The application sets alarms at semi-random intervals in 60-minute blocks, four times a day for 7 days. After obtaining all momentary affect and stress, the questions to assess the usability of the mobile EMA application were also administered. Results: The data were collected from 97 police officers working in Gyeonggi Province of South Korea. The mean completion rate was 60.0% ranging from 3.5% to 100%. The means of positive and negative affect were 18.34 of 28 and 19.09 of 63. The mean stress was 17.92 of 40. Participants responded that the mobile application correctly measured their affect ($4.34{\pm}0.83$) and stress ($4.48{\pm}0.62$) of 5-point Likert scale. Conclusions: Our study investigated the process of utilizing a mobile application to assess momentary affect and stress at repeated times. We found challenges regarding adherence to the research protocol, such as completion and delay of answering after alarm notification. Despite this inherent issue of adherence to the research protocol, the EMA still has advantages of reducing recall bias and assessing the actual moment of interest at multiple time points that improves ecological validity.

• Journal of Environmental Health Sciences
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• v.45 no.4
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• pp.340-357
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• 2019

The purpose of this paper is to help develop a comprehensive understanding of the humidifier disinfectant disaster from diverse perspectives based on a critical review of the relevant academic research papers published so far in the fields of both natural and social science. The authors reviewed pertinent articles in the six academic areas of law, social science, humanities, medicine, toxicology, and environmental health. A proper understanding of the issue of humidifier disinfectant is a challenging task because diverse aspects of it have become related over the more than two decades since such products were first released to the market in 1994. Technical and esoteric issues such as the complex system for relief and compensation for health damages and the approval of chemical toxicity are known to be major impediments to viewing the bigger picture regarding this tragedy. The authors believe that experts need to consider a comprehensive perspective going beyond their individual research arena to gain a better understanding of this issue, especially since it was an alarm signal on ethics and the role of experts and scholars in Korean society. Besides the two professors arrested by the prosecutor's office, it should be remembered that medical doctors recommended patients use humidifiers and disinfectants, and the media was inactive in reporting on this issue. Furthermore, the current paucity of examination of the social and political implications of this tragedy calls for more active engagement by researchers in the humanities and social sciences. In this regard, this paper is a work of self-examination and self-criticism by the authors that could resonate with the overall academic community.

• Journal of IKEEE
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• v.23 no.3
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• pp.1092-1095
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• 2019

In this paper, we propose the development of an embedded board for construction of smart factory. The proposed embedded board for construction of smart factory consists of main module, ADC module, I/O module. Main module is a main calculating device which includes communication pard that allows interface with external device with using industrial protocol and is ported operating system makes board operating into. ADC module takes part in transferring digital signal has converted from electrical signal to the main module from the external sensor which is installed on the field. I/O module is an input and output module which transfers to the main module about a status, alarm, command signal of field device and it has a function that blocks external noises from field device with isolation circuit into it. In order to evaluate the performance of the proposed embedded board for construction of smart factory, it has been tested by an authorized testing institute. As a result, quantity of interacting protocol was 5, speed of hardware clock synchronization was under 10us and operating time of battery without source power was over 8 hours. It produced the same result as the world's highest level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.123-124
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• 2019

우리나라 조선업은 건조분야에서 세계최고의 위상을 자랑하면서 급속도로 발전을 해왔다. 그러나 세계적으로 수주 불황과 조선 산업의 침체로 인해서 조선 산업은 직접적인 타격으로 국내의 중견 조선소 및 기자재 공급업체들이 도산되어0 최근부터 해양플랜트 사업이 우리나라 새로운 산업으로 부상하면서 해양 ICT 융합기술을 활용한 기자재 업체들이 많이 생겨났다. 하지만 실제 해양플랜트 선박 및 기자재의 국산화율은 현저히 낮다. 해양플랜트 외에 다른 용도로 사용이 가능한 Service and Access Basket의 국산화를 위해서 각 모듈별로 임베디드를 연계하여 설계하였다. 기존의 Service and Access Basket의 미비했던 안전 사양(기울기, 하중, 경보 등)을 추가로 설계하고 신뢰성이 높은 센서(자이로, 하중센서, 초음파 거리센서)들을 활용하여 통합 설계하였다. 이러한 통합 시스템이 완성이 되면 고소차, 해양/육상용 고공 작업을 할 수 있는 장비 등에 활용할 수 있을 예정이다.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.85-92
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• 2022

Demand for big data analysis and AI developers is increasing, but there is a lack of an education base to supply them. In this paper, by developing a cloud-based artificial intelligence education platform, the goal was to establish an environment in which practical practical training can be efficiently learned at low cost at educational institutions and IT companies. The development of the education platform was carried out by planning scenarios for each user, architecture design, screen design, implementation of development functions, and hardware construction. This training platform consists of a containerized workload, service management platform, lecture and development platform for instructors and students, and secured cloud stability through real-time alarm system and age test, CI/CD development environment, and reliability through docker image distribution. The development of this education platform is expected to expand opportunities to enter new businesses in the education field and contribute to fostering working-level human resources in the AI and big data fields.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.994-1003
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• 2020

In this paper, we investigated the current issues on the KPAS(Korean Public Alert Service) by News analysis. News articles, from May 15, 2005 to April 30, 2020, were collected with the key word of 'KPAS' through the News Big-Data System provided by the Korea Press Foundation. The results of the content analysis are as follows. First, the issues on alert presentation were categorized by alarm sound, message content, alert level, transmission frequency, delay, reception range, time of alert, and language. Issues on inability to receive KPAS messages were categorized into authority, mobile, sending standard, mobile communication infra, etc. For the last two to three years, news on the inability issues had decreased, while news on the presentation issues had increased. This tells us that the public demand for improvement in the KPAS lies in the presentation issues. The demand for societal resolutions to the presentation issues especially on message content, transmission frequency, and reception range has soared.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.150-156
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• 2022

As the 4th industrial revolution emerges, the implementation of smart factories are essential in the manufacturing industry. However, 80% of small and medium-sized enterprises that have introduced smart factories remain at the basic level. In addition, in root industries such as injection molding, PLC and HMI software are used to implement functions that simply show operation data aggregated by facilities in real time. This has limitations for managers to make decisions related to product production other than viewing data. This study presents a method for upgrading the level of smart factories to suit the reality of small and medium-sized enterprises. By monitoring the data collected from the facility, it is possible to determine whether there is an abnormal situation by proposing an appropriate algorithm for meaningful decision-making, and an alarm sounds when the process is out of control. In this study, the function of HMI has been expanded to check the failure frequency rate, facility time operation rate, average time between failures, and average time between failures based on facility operation signals. For the injection molding industry, an HMI prototype including the extended function proposed in this study was implemented. This is expected to provide a foundation for SMEs that do not have sufficient IT capabilities to advance to the middle level of smart factories without making large investments.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.