• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.490-504
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• 2021

In order to analyze the factors influencing career maturity of general high school students, this study verified the moderated mediating effect of career exploration efficacy by the frequency of conversation with parents in the process of self-awareness and class attitude affecting career maturity. For the study of the second year of general high school, the results of verifying the moderated mediating effect using SPSS 22 and Model 7 of PROCESS macro from the results of the first year survey of the Korea Education and Employment Panel (KEEP) II are as follows. First, positive self-awareness and class attitude had a positive effect on career maturity. Second, career exploration efficacy mediates self-awareness, class attitude, and career maturity. Third, in the relationship between self-awareness and career maturity, there is no moderated mediating effect on career exploration efficacy by the frequency of conversation with parents. Fourth, in the relationship between class attitude and career maturity, a confrontation-moderated mediating effect on career exploration efficacy by conversation frequency with parents appears. Based on these studies, implications and future tasks for career maturity of general high school students were proposed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• The Korea Journal of Herbology
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• v.36 no.6
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• pp.1-8
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• 2021

Objectives : Atractylodis rhizoma Alba has been traditionally used as a medicinal resource that is used for enhancing Qi (氣) in traditional medicine in Korea, China, and Japan. This study investigated the protective effects of Atractylodis rhizoma Alba extract (ARE) against trimethyltin (TMT), a neurotoxin that causes selective hippocampal injury, using both in vitro and in vivo models. Methods : We investigated the effects of ARE on TMT- (5mM) induced cytotoxicity in primary cultures of mouse hippocampal cells (7 days in vitro ) and on hippocampal injury in C57BL/6 mice injected with TMT (2.6 mg/kg). Results : We observed that ARE treatment (0 - 50 ㎍/mL) significantly reduced TMT-induced cytotoxicity in cultured hippocampal neurons in a dose-dependent manner, based on results of lactate dehydrogenase and 3-4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide assays. Additionally, this study showed that orally administered ARE (5 mg/kg; between -6 and 0 days before TMT injection) significantly attenuated seizures in adult mice. Furthermore, quantitative analysis of allograft inflammatory factor-1 (Iba-1)- and glial fibrillary acidic protein (GFAP)- positive cells showed significantly reduced levels of Iba-1- and GFAP-positive cell bodies in the dentate gyrus of mice treated with ARE prior to TMT injection. These findings indicate the significant protective effects of ARE against the TMT-induced massive activation of microglia and astrocytes in the hippocampus. Conclusions : We conclude that ARE minimizes the detrimental effects of TMT-induced hippocampal neurotoxicity, both in vitro and in vivo . Our findings may serve as useful guidelines to support ARE administration as a promising pharmacotherapeutic approach to hippocampal degeneration.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Journal of Korean Home Economics Education Association
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• v.34 no.2
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• pp.129-152
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• 2022

The purpose of this study was to develop a sustainable dietary education program for middle school home economics subject using a teaching strategy to improve the empowerment of adolescents and to verify and evaluate the effectiveness of the program. To achieve the purpose of this study, the program was developed and evaluated according to the ADDIE teaching design model. The contents related to the dietary area were extracted from the technical & home economics curriculum of the 2015 revised middle school and SDGs, and their relevance was analyzed to select the contents of dietary education. The program developed based on the analysis results is 'dietary life together' and consists of five learning topics: 'living together in the global village', 'maintaining healthy diet', 'creating a dietary culture together', 'living with nature and people', and 'maintaining a safe diet'. As a strategy for improving empowerment, we presented four situations, each of which represents value judgment, prediction of results, responsible behavior choice, and decision making. The developed program was reviewed by experts and applied to 17 unit classes for 17 weeks (1 unit hour per week) to the third graders of middle schools in Gyeonggi-do. Significant differences were found between before and after the class measurements of the personal empowerment and the political and social empowerment, which shows the classes were effective in improving empowerment. However, since there was no significant difference in interpersonal empowerment before and after the program, suggestions were made to utilize strategies to facilitate discussion and cooperative learning when implementing the program. The students who participated in the class evaluated the program positively as a whole. The program was evaluated to have helped the students believe they could change society through solving dietary problems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.3
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• pp.49-53
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• 2022

Ultra-thin glass (UTG) has been widely used in foldable display as a cover window for the protection of display and has a great potential for rollable display and various flexible electronics. The foldable display is under impact loading by bending and touch pen and exposed to other external impact loads such as drop while people are using it. These external impact loads can cause cracks or fracture to UTG because it is very thin under 100 ㎛ as well as brittle. Cracking and fracture lead to severe reliability problems for foldable smartphone. Thus, this study constructs finite element analysis (FEA) model for the pen drop test which can measure the impact resistance of UTG and conducts mechanical modeling to improve the reliability of UTG under impact loading. When a protective layer is placed to an upper layer or lower layer of UTG layer, stress mechanism which is applied to the UTG layer by pen drop is analyzed and an optimized structure is suggested for reliability improvement of UTG layer. Furthermore, maximum principal stress values applied at the UTG layer are analyzed according to pen drop height to obtain maximum pen drop height based on the strength of UTG.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.