• Journal of Korean Public Health Nursing
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• v.35 no.2
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• pp.254-267
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• 2021

Purpose: This study aimed at identifying the factors affecting the service satisfaction of urban elderly, focusing on the outreach community health service in Seoul, and suggesting policy directions for the successful implementation of community care. Method: Individuals aged 65 and 70 who used the outreach community health service from July 2017 to June 2019 were eligible for the survey. A total of 2,028 people were sampled using a proportional allocation method for each autonomous district in the survey which covered 25 districts. A multi-level logistic regression analysis was conducted, taking into account the individual's socioeconomic level, health status, type of service provided, and the healthcare-related environment and service provision period of the autonomous district. Result: The results revealed that the health status of the urban elderly, the type of services provided (health screening, linkage to community health center and clinic/hospital, medical checkup results counseling, frailty evaluation), and personal experience of the service were the major factors associated with the satisfaction with the outreach health services. Conclusion: The development of customized health services based on the close relationship between visiting nurses and the elderly may be considered to promote a sustainable community health care model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.7-14
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• 2022

Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.521-528
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• 2021

Existing image analysis systems in use in the military field are carried out by readers analyzing and identifying images themselves, writing and disseminating related content, and in this process, repetitive tasks are frequent, resulting in workload. In this paper, to solve the previous problem, we proposed an algorithm that can operate the Seq2Seq model on a word basis, which operates on a sentence basis, and applied the Attention technique to improve accuracy. In addition, by applying the Beam Search technique, we would like to recommend various current identification sentences based on the past identification contents of a specific area. It was confirmed through experiments that the Beam Search technique recommends sentences more effectively than the existing greedy Search technique, and confirmed that the accuracy of recommendation increases when the size of Beam is large.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• Journal of Korean society of Dental Hygiene
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• v.21 no.6
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• pp.741-750
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• 2021

Objectives: To provide basic data for oral health policy by identifying the differences in oral health behaviors and conditions among middle-aged people in their 40s and 50s and relating these to their health-related quality of life, and to identify factors affecting health-related quality of life. Methods: In this study, secondary data from the National Health and Nutrition Examination Survey were used. Data were gathered from 8,127 participants, of which 2,353 were middle-aged (40-59 years old). Covariance analysis was performed using a complex sample general linear model to investigate the changes in the subjects' health-related quality of life (EQ-5D) according to their general characteristics, oral health behaviors, and oral health status. Results: Oral health behavior was not significantly related to health-related quality of life. In contrast, a higher oral health status (chewing function) was associated with better health-related quality of life. Conclusions: In conclusion, this study suggested a close relationship between oral health status (chewing function) and health-related quality of life. Therefore, it is necessary to recognize the importance of oral health in improving the health-related quality of life of middle-aged people in their 40s and 50s, as well as in informing oral health policies and preventive measures for individuals and communities.

• The Journal of Sustainable Design and Educational Environment Research
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• v.20 no.4
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• pp.1-12
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• 2021

This study aims to propose a planning direction based on the results of domestic case studies and expert advice as a basic research to establish a specific implementation model for kindergarten education space in response to changes in future society and future education by escaping from the issues and facility limitations of existing kindergarten education space. To this end, it is intended to contribute to the positive effect of future education policies by being used as basic data for building specific implementation models through review of the 'Nuri' course, domestic case analysis, and revision of specific standards for kindergarten facility planning. The main research results were case analysis and advisory consultations to derive facility standards and planning directions divided into layout plans, general classrooms, support facilities, public spaces, and external spaces, and a process for identifying and systematizing space-influencing factors based on the facility standards derived in this basic study is needed hereafter.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.14-22
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• 2022

This study was conducted to promote male nursing students' adaptation to college life by identifying life stress, grit, and degree of adaptation to college life, and the effect of grit mediating effect on the relationship between life stress and college life adjustment. became The subjects of this study were 209 male nursing college students enrolled in nursing departments in 5 major cities. The collected data were analyzed with SPSS/WIN 25.0. Life stress showed a significant negative correlation with college life adaptation and grit, but grit showed a positive correlation with college life adaptation. As a result of mediating effect verification, the final model explained 43.5% of college life adaptation. Therefore, in order to improve male nursing students' adaptation to college life, it is required to develop a program that can lower life stress and increase grit and college life adaptation level.

• Safety and Health at Work
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• v.13 no.2
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• pp.255-260
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• 2022

Background: The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the importance of implementing strategic management that prioritizes the safety of frontline nurse professionals. In this sense, this research was aimed at identifying factors associated with the illness of nursing professionals caused by COVID-19 according to socio-demographic, clinical, and labor variables. Methods: A cross-sectional study was conducted in three Brazilian university hospitals with 859 nursing professionals, which include nurses, technicians, and nursing assistants, between November 2020 and February 2021. We present data using absolute and relative frequency. We used Chi-square test for hypothesis testing and multiple logistic regression for predictive analysis and chances of occurrence. Results: The rate of nursing professionals affected by COVID-19 was 41.8%, and the factors associated with contamination were the number of people in the same household with COVID-19 and obesity. Being a nurse was a protective factor when the entire nursing team was considered. The model is significant, and its variables represent 56.61% of the occurrence of COVID-19 in nursing professionals. Conclusion: Obesity and living in the same household as other people affected by COVID-19 increases the risk of contamination by this new coronavirus.

• Journal of Animal Reproduction and Biotechnology
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• v.37 no.3
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• pp.193-201
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• 2022

Subgroups of sperm which share similar motility features documented in mammals indicate between-subject variations that might be related to fertilizing potential of the respective ejaculates. The objectives of this study were to define subpopulations of motile sperm in Gyr falcon semen using kinematic parameters driven by Computer Assisted Semen Analysis (CASA) and to investigate the subject-related variations in these subpopulations. A total of 24 fresh ejaculates from 6 falcons were used to assign each of the 20473 sperms into 3 subpopulations by a multivariate cluster analysis. The proportion of sperms in different sub-populations were compared among subjects by a generalized linear model and repeatability of sperm frequency in different subpopulations was investigated by corelation analysis. The resulting 3 categories of sperm indicated significant differences in all kinematic parameters (p < 0.05). Subpopulation 1 (15.91%) contained sperms with the highest velocity and progressiveness of movement trajectory while subpopulation 3 (6.4%) included the least progressively motile sperms. Proportion of rapid and medium progressive sperm were consistently higher in the ejaculate of three falcons compared to the two other birds which also had the highest proportion of slow non-progressive sperms (p < 0.05). Respective proportion of sperms in each subpopulations indicated significant repeatability over multiple measurements (p < 0.05). In conclusion, subpopulations of motile sperm in Gyr falcon can be identified using kinematic parameters generated by CASA. Individual differences in the proportion of these subpopulations might have potential application for identifying the males with higher fertilizing capacity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.