• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.15-30
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• 2023

Recently, various services using artificial intelligence(AI) are emerging in the media field as well However, most of the video editing, which involves finding an editing point and attaching the video, is carried out in a passive manner, requiring a lot of time and human resources. Therefore, this study proposes a methodology that can detect the edit points of video according to whether person in video are spoken by using Video Swin Transformer. First, facial keypoints are detected through face alignment. To this end, the proposed structure first detects facial keypoints through face alignment. Through this process, the temporal and spatial changes of the face are reflected from the input video data. And, through the Video Swin Transformer-based model proposed in this study, the behavior of the person in the video is classified. Specifically, after combining the feature map generated through Video Swin Transformer from video data and the facial keypoints detected through Face Alignment, utterance is classified through convolution layers. In conclusion, the performance of the image editing point detection model using facial keypoints proposed in this paper improved from 87.46% to 89.17% compared to the model without facial keypoints.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.