• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.726-734
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• 2020

Objectives: The aim of this study was to analyze and classify the characteristics of potential damage from hazardous chemical accidents in 229 administrative units in South Korea by reflecting the social and environmental characteristics of areas where chemical accidents can occur. Methods: A number of indicators were selected through preceding studies. Factor analysis was performed on selected indicators to derive factors, and cluster analysis was performed based on the factor scores. Results: As a result of the cluster analysis, 229 administrative units were divided into three clusters, and it was confirmed that each cluster had its own characteristics. Conclusions: The first cluster, "areas at risk of accident occurrence and spread of damage" was a type with a high potential for accident damage and a high density of hazardous facilities. The second cluster, "Urban infrastructure damage hazard areas" appeared to be a cluster with high urban development characteristics. Finally, the third cluster 'Urban and environmental damage hazard areas' appeared to be a cluster with an excellent natural environment. This study went further from the qualitative discussion related to existing chemical accidents to identify and respond to accident damage by reflecting the social and environmental characteristics of the region. Distinct from the previous studies related to the causes of accidents and the response system, it is meaningful to conduct empirical research focusing on the affected areas by analyzing the possibility of accident damage in reflection of the social and environmental characteristics of the community.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1371-1376
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• 2019

In case of fire in a structure, it is difficult to suppress fire because it can not accurately grasp the location of fire in case of fire. In this paper, we propose a system algorithm that can guide the optimal evacuation route in case of deep learning-based (RNN) structure disaster. The present invention provides a service to transmit data detected by sensors to a server in real time by using installed sensor, to transmit and analyze information such as temperature, heat, smoke, toxic gas around the sensor, to identify the safest moving path within a set threshold, to transmit information to LED guide lights and direction indicators in a structure in real time to avoid risk factors. This is because the information of temperature, heat, smoke, and toxic gas in each area of the structure can be grasped, and it is considered that the optimal evacuation route can be guided in case of structure disaster.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.595-602
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• 2021

Marine accidents are mostly caused by medium and small ships, and are continuously increasing. In this paper, we propose an architecture of the speech synthesis based automatic safety navigation support service providing system for small ships that equiped onboard systems compared with vessels. The main purpose of the system is to prevent marine accidents by providing synthesized voice safety messages to nearby ships. The safety navigation support service is operated by connecting GPS and AIS to synthesize voice safety messages, automatically broadcast through VHF. Therefore, we developed a data processing module, a staged risk analysis module, a voice synthesis safety message generation module, and a VHF broadcasting equipment control module, which are components of the system. In addition, we conducted laboratory-level and sea-trial demonstration tests using the developed the system, which verified usefulness of the proposed service.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.667-676
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• 2021

Recently, adolescents in Korea are exposed to the risk of postural imbalance due to overuse of smartphones and lack of physical activity due to the amount of learning. In addition, the need for effective non-face-to-face exercise services is increasing due to Corona 19. With this in mind, this study proposes an exercise service using an EMG sensor to overcome the limitations of non-face-to-face services while providing the effect of improving round shoulders for adolescents. An exercise program that can improve round shoulders was constructed, and an application in conjunction with an EMG sensor was implemented to exercise effectively. The exercise program was configured to alternately exercise the target muscle area for 4 weeks, and the function to provide feedback was added by measuring the EMG values that change accordingly. Through this study, we intend to provide the basis for exercise-based posture correction digital service, and improve the unbalanced body through this, thereby promoting the possibility of health promotion.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.128-137
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• 2021

The purpose of this study is to examine the effect of sympathetic reactions to public service advertisement video messages produced for health campaigns. To this end, based on the empathy response scale proposed by Campbell & Babrow (2004), the empathy response to the images of nine health campaigns with themes of smoking cessation, tuberculosis, and suicide triggered fear of health risks and health behaviors (information seeking, preventive actions). As a result of the analysis, among the factors of empathy reaction, the reality of the message creative, the match of emotions, and the identification of the characters in the video each played a role in raising fear, and it is rather fear that logically understanding the situation that causes health problems through the health campaign video It was found that it played a role in reducing health information seeking behavior. On the other hand, it was found that the higher the degree of interest, such as sympathy for the characters in the video, among the factors of the sympathetic response to the health campaign, the higher the intention of preventive action to reduce the health risk.

• The Journal of Bigdata
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• v.5 no.2
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• pp.69-76
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• 2020

This study was conducted with the support of the Information and Communication Technology Promotion Center, funded by the government (Ministry of Science and ICT) in 2019. Highly pathogenic avian influenza (HPAI) is an acute infectious disease of birds caused by highly pathogenic avian influenza virus infection, causing serious damage to poultry such as chickens and ducks. High pathogenic avian influenza (HPAI) is caused by focusing on winter rather than year-round, and sometimes does not occur at all during a certain period of time. Due to these characteristics of HPAI, there is a problem that does not accumulate enough actual data. In this paper study, GAN network was utilized to generate actual similar data containing missing values and the process is introduced. The results of this study can be used to measure risk by generating realistic simulation data for certain times when HPAI did not occur.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.146-151
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• 2021

Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1500-1505
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• 2019

Currently, as part of the ICT Smart City, the company is building C-ITS(Cooperative-Intelligent Transport Systems) for solving urban traffic problems. In order to realize autonomous driving service with C-ITS, the role of advanced road infrastructure is important. In addition to the study of mid- to long-term C-ITS and autonomous driving services, it is necessary to present more realistic solutions for road traffic safety in the short term. Therefore, in this paper, we propose a highway accident detection alarm system that can detect and analyze traffic flow and risk information, which are essential information of C-ITS, based on IoT guard rail and provide immediate alarm and remote control. Intelligent IoT guard rail is expected to be used as an intelligent advanced road infrastructure that provides data at actual road sites that are required by C-ITS and self-driving services in the long term.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.467-473
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• 2020

Recently, the number of traffic accidents has also increased with the increase in the penetration rate of cars in Korea. In particular, not only inter-vehicle accidents but also human accidents near crosswalks are increasing, so that more attention to traffic safety around crosswalks are required. In this paper, we propose a system for predicting the safety level around the crosswalk by recognizing an approaching vehicle and estimating the speed of the vehicle using NVIDIA Jetson Nano-class edge devices. To this end, various machine learning models are trained with the information obtained from deep learning-based vehicle detection to predict the degree of risk according to the speed of an approaching vehicle. Finally, based on experiments using actual driving images and web simulation, the performance and the feasibility of the proposed system are validated.

• BMB Reports
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• v.54 no.3
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• pp.164-169
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• 2021

Neuronal growth regulator 1 (NEGR1) is a GPI-anchored membrane protein that is involved in neural cell adhesion and communication. Multiple genome wide association studies have found that NEGR1 is a generic risk factor for multiple human diseases, including obesity, autism, and depression. Recently, we reported that Negr1-/- mice showed a highly increased fat mass and affective behavior. In the present study, we identified Na/K-ATPase, beta1-subunit (ATP1B1) as an NEGR1 binding partner by yeast two-hybrid screening. NEGR1 and ATP1B1 were found to form a relatively stable complex in cells, at least partially co-localizing in membrane lipid rafts. We found that NEGR1 binds with ATP1B1 at its C-terminus, away from the binding site for the alpha subunit, and may contribute to intercellular interactions. Collectively, we report ATP1B1 as a novel NEGR1-interacting protein, which may help deciphering molecular networks underlying NEGR1-associated human diseases.