• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.728-737
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• 2021

In the meantime, broadcasting CG has gone through the process of dismantling, changing, and distorting, while broadcasting CG in broadcasting programs utilizes the expanded background of 'temporality' and 'formality'. This is to create an audiovisual language that appeals to human synesthesia by expressing the meaning to be conveyed in three dimensions. Broadcast CG goes beyond simple instructional and informational broadcast graphic operation, and increases the pure aesthetic value and sensibility of the video considering readability and formativeness, and through this, the audiovisual information perfection of the broadcast program is derived and acts as a very important factor. Therefore, this paper examines the results of broadcast CG production and utilization methods at existing local broadcasters, and identifies the limitations of local broadcasters' CG production and utilization through case analysis for each broadcast program type. We want to derive a model that is a compromise line. In addition, I would like to suggest a plan that can be applied more actively and practically to local broadcasting programs. In order to solve this problem, this study first examines "Analysis of cases of use of broadcasting CG production in broadcasting programs" and then "more efficient broadcasting CG production techniques by identifying problems in broadcasting CG production methods and utilization of local broadcasters" and how to actively use it". In addition, the results of this study are expected to contribute to the establishment of a new role and practical broadcast CG production model for broadcast graphic designers in charge of broadcast CG production and the technical perspective of broadcast program production by local broadcasters.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.111-117
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• 2021

With the rapid development of advanced science and technology including the 4th industrial revolution, the future battlefield environment is evolving at a rapid pace. In order to actively respond to issues such as reduction of military resources and shortening of service period, and to emphasize the realization of human-centered values, the Ministry of National Defense is re-establishing the role of the Army in accordance with the defense reform and is promoting the Warrior Platform, a next-generation individual combat system. In this paper, we intend to present the optimal warrior platform architecture suitable for the Korean Army by realizing the concept of future ground operations and analyzing overseas cases. We analyze the essential abilities required of individual combatants and the abilities required for each unit type, and specifically presents a plan for integration and linkage of warrior platform equipment. We also propose an efficient business promotion direction by presenting the data flow and power connection diagram between the devices that need integration and interworking.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.165-177
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• 2022

The purpose of this study is to present the direction of employment guidance for long-term service through the analysis of the cause of employment of employed students who graduated from specialized high school. In particular, the purpose is to present student guidance plans for long-term service by analyzing personal reasons for students graduating from commercial high schools and policy factors for individual, school, company, and government service after employment. To this end, a survey was conducted for graduates of commercial high schools nationwide, and the validity, reliability, and causality of the survey data were analyzed by applying Exploratory Factor Analysis, Cronbach's Alpha, and decision tree analysis techniques. We found that personal goal setting for employment is an important factor for working for more than 1 year, personal relationships at work and personal characteristics are important factors for working for more than 3 years. In addition, we found that the reason for getting a job is that personal reasons and school recommendations are great, special lectures on employment, camps, and 'advice from seniors and teachers' programs are helpful in finding a job, and accounting and computer related subjects are helpful for long-term employment. Accordingly, in specialized high schools, it is required to prepare specific instructional measures for education such as setting personal goals and the formation of human relationships that are the basis of social life, and to actively operate the above subjects and programs to help with employment and longevity.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.344-351
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• 2023

Purpose: This study aimed to evaluate the effectiveness of a dielectric barrier discharge (DBD) plasma module for sterilizing airborne bacteria in indoor spaces and measure the concentration of ozone generated during plasma discharge. Method: The DBD plasma module was installed in a 76m³ space, and air samples were collected under various discharge times to compare the reduction of airborne bacteria. Result: The results showed a significant decrease in airborne bacteria, ranging from 92.057% to 99.999%, with an average ozone concentration of 0.04 ppm, below the reference value. Conclusion: The study suggests that plasma discharge can be used as a means of preventing the spread of airborne bacteria and viruses, while ensuring safety for human exposure.

• 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 Ginseng Research
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• v.48 no.1
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• pp.77-88
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• 2024

Background: Lung inflammation occurs in many lung diseases, but has limited effective therapeutics. Ginseng and its derivatives have anti-inflammatory effects, but their unstable physicochemical and metabolic properties hinder their application in the treatment. Panaxadiol (PD) is a stable saponin among ginsenosides. Inhalation administration may solve these issues, and the specific mechanism of action needs to be studied. Methods: A mouse model of lung inflammation induced by lipopolysaccharide (LPS), an in vitro macrophage inflammation model, and a coculture model of epithelial cells and macrophages were used to study the effects and mechanisms of inhalation delivery of PD. Pathology and molecular assessments were used to evaluate efficacy. Transcriptome sequencing was used to screen the mechanism and target. Finally, the efficacy and mechanism were verified in a human BALF cell model. Results: Inhaled PD reduced LPS-induced lung inflammation in mice in a dose-dependent manner, including inflammatory cell infiltration, lung tissue pathology, and inflammatory factor expression. Meanwhile, the dose of inhalation was much lower than that of intragastric administration under the same therapeutic effect, which may be related to its higher bioavailability and superior pharmacokinetic parameters. Using transcriptome analysis and verification by a coculture model of macrophage and epithelial cells, we found that PD may act by inhibiting TNFA/TNFAR and IL7/IL7R signaling to reduce macrophage inflammatory factor-induced epithelial apoptosis and promote proliferation. Conclusion: PD inhalation alleviates lung inflammation and pathology by inhibiting TNFA/TNFAR and IL7/IL7R signaling between macrophages and epithelial cells. PD may be a novel drug for the clinical treatment of lung inflammation.