• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• Safety and Health at Work
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• v.13 no.1
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• pp.117-125
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• 2022

Background: External-cause mortality is an important public health issue worldwide. Considering its significance to workers' health and inequalities across industries, we aimed to describe the state of external-cause mortality and investigate its difference by industry in Republic of Korea based on data for 2018. Methods: Data obtained from the Statistics Korea and Korean Employment Information System were used. External causes of death were divided into three categories (suicide, transport accident, and others), and death occurred during employment period or within 90 days after unemployment was regarded as workers' death. We calculated age- and sex-standardized mortalities per 100,000, standardized mortality ratios (SMRs) compared to the general population and total workers, and mortality rate ratios (RRs) across industries using information and communication as a reference. Correlation analyses between income, education, and mortality were conducted. Results: Age- and sex-standardized external-cause mortality per 100,000 in all workers was 29.4 (suicide: 16.2, transport accident: 6.6, others: 6.6). Compared to the general population, all external-cause and suicide SMRs were significantly lower; however, there was no significant difference in transport accidents. When compared to total workers, wholesale, transportation, and business facilities management showed higher SMR for suicide, and agriculture, forestry, and fishing, mining and quarrying, construction, transportation and storage, and public administration and defense showed higher SMR for transport accidents. A moderate to strong negative correlation was observed between education level and mortality (both age- and sex-standardized mortality rates and SMR compared to the general population). Conclusion: Inequalities in external-cause mortalities from suicide, transport accidents, and other causes were found. For reducing the differences, improved policies are needed for industries with higher mortalities.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.31-42
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• 2021

Recently, virtual reality (VR) and augmented reality (AR) technologies are attracting attention as core technologies in the era of the 4th industrial revolution. These virtual and augmented reality technologies are being used in a variety of industries, including the construction industry, healthcare industry, and manufacturing industry, to innovate in communication and collaboration, education and simulation, customer service and reinvention of the customer experience. In this paper, VR-based experiential safety education was conducted for workers of shipbuilding companies in Ulsan city, and for them, the educational effectiveness such as immersion, site applicability, safety accident prevention, education satisfaction, overall performance, and safety behavior in VR-based safety experience education were measured. In addition, we examined whether the immersion of VR-based safety experience education affects site applicability, safety accident prevention, educational satisfaction, overall performance, and safety behavior. Furthermore, it was analyzed whether site applicability plays a mediating role in the relationship between immersion and safety accident prevention. As a result, it was found that the immersion of VR-based safety experience education affects site applicability, safety accident prevention effect, education satisfaction, overall performance, and safety behavior, and that site applicability mediates between immersion and safety accident prevention. Based on these results, we suggests a direction for the development of VR-based contents in the field of safety and health and the transformation of safety and health education in the future.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.365-371
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• 2021

In this study, The Virtual Power Plant (VPP) solution platform considered in this study minimizes the cost and investment risk associated with the construction of power generation and transmission facilities. In addition, it includes a Demand Response (DR) program operation function to meet consumers' electricity demand. With the introduction of VPP, it is possible to provide more eco-friendly and efficient power by responding to changes in consumer load in real time through existing generators and DR programs without large-scale facility investment in power generation and transmission/distribution sectors. In order to link the communication device to the solar power and ESS linkage device, it is necessary to transmit data in the control/state between the device device and the edge system and develop an IoT device and interworking platform (OneM2M).

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.157-166
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• 2021

With the rising of the middle-class in China, the garage reserve has become an important element considered by the high-end community in improving user's experience. Taking Oak Bay, China as an example, this study aims to present a new system of storage service that can help companies achieve sustainable development of storage services. In order to achieve this goal, a customized service process is made by investigating user's need with sample visits and in-depth interviews, and finding user's key points with journey maps. The final analysis shows that this storage service system helps to expand the space and improve user's satisfaction. Therefore, this paper provides theoretical support and practical basis of garage services for other construction companies, and greatly improves the service system of community.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.890-895
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• 2021

Recently, the "Act of Serious Disasters, etc" was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.226-234
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• 2021

Purpose: The accessibility of medical facilities for cancer patients affects both their comfort and survival. Patients in rural areas have a higher socioeconomic burden and are more vulnerable to emergency situations than urban dwellers. This study examined the feasibility and effectiveness of a cancer care model integrating a regional cancer center (RCC) and public health center (PHC). Methods: This study analyzed the construction of a safety care network for cancer patients that integrated an RCC and PHC. Two public health institutions (an RCC in Gyeongnam and a PHC in Geochang County) collaborated on the development of the community care model. The study lasted 13 months beginning in February 2019 to February 2020. Results: The RCC developed the protocol for evaluating and measuring 27 cancer-related symptoms, conducted education for PHC nurses, and administered case counseling. The staff at the PHC registered, evaluated, and routinely monitored patients through home visits. A smartphone application and regular video conferences were incorporated to facilitate mutual communication. In total, 177 patients (mean age: 70.9 years; men: 59%) were enrolled from February 2019 to February 2020. Patients' greatest unmet need was the presence of a nearby cancer treatment hospital (83%). In total, 28 (33%) and 44 (52%) participants answered that the care model was very helpful or helpful, respectively. Conclusion: We confirmed that a combined RCC-PHC program for cancer patients in rural areas is feasible and can bring satisfaction to patients as a safety care network. This program could mitigate health inequalities caused by accessibility issues.

• Journal of Drive and Control
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• v.19 no.1
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• pp.16-25
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• 2022

The purpose of this study was to calculate and analyze the engine load factor of major agricultural operations using a 78 kW class agricultural tractor for estimating the emission of air pollutants and greenhouse. Engine load data were collected using controller area network (CAN) communication. Main agricultural operations were selected as plow tillage (PT), rotary tillage (RT), baler operation (BO), loader operation (LO), driving on soil (DS), and driving on concrete (DC). The engine power was calculated using the measured engine load data. A weight factor was applied to load factor for considering usage ratio according to agricultural operations. Weight factors for different agricultural operations were calculated to be 27.4%, 32.9%, 17.5%, 7.7%, 4.5%, and 10.0% for PT, RT, BO, LO, DS, and DC, respectively. As a result of the field test, load factors were 0.74, 0.93, 0.41, 0.23, 0.27, and 0.21 for PT, RT, BO, LO, DS, and DC, respectively. The engine load factor was the highest for RT. Finally, as a result of applying the weight factor for usage ratio of agricultural operations, the integrated engine load factor was estimated to be 0.63, which was about 1.31 times higher than the conventional applied load factor of 0.48. In future studies, we plan to analyze the engine load factor by considering various horsepower and working conditions of the tractor.