• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.363-374
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• 2023

This study is a descriptive research study to confirm the care efficacy and empathy competency of nursing hospital nurses and analyze the factors to improve person-centered care behavior, provide theoretical and practical information accordingly, and prepare basic data. The study collected data from March 20, 2023 to April 20, 2023, targeting 146 nurses at a nursing hospital for the elderly in C region located in G province, and finally analyzed a total of 144 copies. Using IBM SPSS/25, descriptive statistics t-test, ANOVA, and correlation multiple regression analysis were analyzed. As a result of analyzing the variables that affect the subject's person-centered care by hierarchical multiple regression, the higher the caring efficacy and empathy capacity, the more the person-centered care was affected, and the explanatory power was 31.5%. As a result of this study, empathy competency and care efficacy were found to have an effect on person-centered care. It is thought that various measures should be prepared to improve the care efficacy and empathy competency for the efficient nursing performance of person-centered care of nursing hospital nurses.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Analytical Science and Technology
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• v.19 no.5
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• pp.433-440
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• 2006

A HPLC/UV method was developed and validated for the rapid and simultaneous determination of urinary metabolites of organic solvents, mandelic acid, hippuric acid, phenylglyoxylic acid, ortho-, meta- and para-methylhippuric acid, using a monolithic column. The mobile phase was composed of tetrabutylammonium bromide as ion-pairing reagent with a flow rate of 2.4 mL/min. The total run time was less than 2.5 min for all six analytes. Good linearities were obtained for all the metabolites with correlation coefficients above 0.9993. Intra-day precision, accuracy and inter-day precision was 0.01~7.32%, 83.9~116.3% and 0.01~7.16%, respectively. The method was validated and confirmed by quantification of the quality assurance samples of Industrial Safety and Health Research Institute, Korea Occupational Safety and Health Agency.

• 한국노년학
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• v.27 no.2
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• pp.487-502
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• 2007

An objective of this study is to verify factor of exercise adherence of the elder who have carried on exercise for many years at non-commercial sports center. To achieve the objective, I used ethnography, a method of qualitative study. The subjects for this study were 5 members of non-commercial sports center. They have been attending lesson which I have been running Taichi for more than 2 years and carried on exercise for more than 5 years. Main results of this study are as follows. First, the elder who have kept on with exercise showed the eagerness to participate and capacity of self-management through their exercise adherence for many years. Second, various factors such as social, environmental, and social psychological on had a strong effect on a continuous exercise performance. The most remarkable thing of results of this study is a close human connection among instructors, manager and companion is the most effectual factor of the elder's exercise adherence.

• 한국노년학
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• v.39 no.2
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• pp.285-304
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• 2019

The purpose of this study was to investigate the effects of leisure-based occupational therapy on leisure satisfaction, activities of daily living, and rehabilitation motivation in elderly patients with chronic diseases. In the study, Subjects were assigned to either the control or experimental group. The experimental group consisted of 10 and the control group of 14 patients. The experimental group receive a Leisure-based occupational therapy intervention, and the control group receive a conventional occupational therapy intervention. Leisure satisfaction, activities of daily living, and rehabilitation motivation were measured to Canadian occupational performance measure(COPM), Korean Activities of Daily Living(K-ADL·K-IADL), and Patient Questionnaire of Rehabilitation Motivation(PAREMO). Evaluation scores were compared before and after intervention to determine whether or not there were differences over time. And we examined the correlation between the variables. The results of this study were as follows: 1) The leisure-based occupational therapy has a significant effect on leisure satisfaction and rehabilitation motivation(p<.01). 2) The conventional occupational therapy has a significant effect on activities of daily living(p<.01). 3) There was a significant correlation between ADL and IADL(p<.01). 4) There was a significant correlation between leisure satisfaction and Rehabilitation Motivation(p<.01). Implications for occupational therapy include that we must offer appropriate approach with considering the interests and value of patients to them. And proposing studies to demonstrate the efficacy of occupational therapy approaches.

• Journal of Practical Agriculture & Fisheries Research
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• v.18 no.1
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• pp.101-112
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• 2016

Monitoring and control of the greenhouse environment play a decisive role in greenhouse crop production processes. The network system for greenhouse control was developed by using recent technologies of networking and wireless communications. In this paper, a remote monitoring and control system for greenhouse using a smartphone and a computer with internet has been developed. The system provides real-time remote greenhouse integrated management service which collects greenhouse environment information and controls greenhouse facilities based on sensors and equipments network. Graphical user interface for an integrated management system was designed with bases on the HMI and the experimental results showed that a sensor data and device status were collected by integrated management in real-time. Because the sensor data and device status can be displayed on a web page, transmitted using the server program to remote computer and mobile smartphone at the same time. The monitored-data can be downloaded, analyzed and saved from server program in real-time via mobile phone or internet at a remote place. Performance test results of the greenhouse control system has confirmed that all work successfully in accordance with the operating conditions. And data collections and display conditions, event actions, crops and equipments monitoring showed reliable results.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.557-570
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• 2023

The purpose of this study is to develop a digital-based non-face-to-face instructional design model for national public servants. For this purpose, we reviewed the direction of non-face-to-face instructional design based on previous research, and interviewed a total of 10 instructors and learners with experience in non-face-to-face education to identify the success factors of non-face-to-face instructional design for government employees. In addition, to ensure the validity of the instructional design model, a Delphi survey was conducted with experts in instructional design and educational management. As a result, the digital-based instructional design model for national public servants reflects the importance of learning objectives and content design to enhance learning motivation and improve educational effectiveness, and includes detailed implementation plans to support specific and clear performance activities for instructors in non-face-to-face education situations. The instructional design model developed through this study can be expanded as a standard for improving the quality of education in public human resource development, and can be used as a teaching and learning guide according to learner types and characteristics.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.883-896
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• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.