• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.356-366
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• 2021

The high-tech industry, a leader in the national economy, has a larger investment cost compared to general buildings, a shorter construction period, and requires continuous investment. Therefore, accurate construction cost prediction and quick decision-making are important factors for efficient cost and process management. Overseas, the construction information classification system has been standardized since 1980 and has been continuously developed, improving construction productivity by systematically collecting and utilizing project life cycle information. At domestic construction sites, attempts have been made to standardize the classification system of construction information, but it is difficult to achieve continuous standardization and systematization due to the absence of a standardization body and differences in cost and process management methods for each construction company. Particular, in the case of the high-tech industry, the standardization and systematization level of the construction information classification system for high-tech facility construction is very low due to problems such as large scale, numerous types of work, complex construction and security. Therefore, the purpose of this study is to construct a construction information classification system suitable for high-tech facility construction through collection, classification, and analysis of related project data constructed in Korea. Based on the WBS (Work Breakdown Structure) and CBS (Cost Breakdown Structure) classified and analyzed through this study, a code system through hierarchical classification was proposed, and the cost model of buildings by linking WBS and CBS was three-dimensionalized and the utilized method was presented. Through this, an information classification system based on inter-relationships can be developed beyond the one-way tree structure, which is a general construction information classification system, and effects such as shortening of construction period and cost reduction will be maximized.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.569-578
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• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.769-777
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• 2021

Purpose: Earthquake disasters are frequently occur unpredictable situations due to various variables and unexpected situations. As a result, the work process itself is not uniform, making it difficult for public officials in the disaster safety department to familiarize themselves with the earthquake field manual. This paper is specifically and accurately grasp the current work situation conducted by the Disaster and Safety Countermeasures Headquarters of the Gyeongsangbuk-do Office and present a plan for designing serious game scenarios necessary for field manual learning. Method: In this study, scenarios were designed based on the GBS(Goal Based Scenario) model, and in the process of assigning missions and roles based on the Gyeongsangbuk-do earthquake field manual, public officials related to earthquakes were able to acquire knowledge and skills to solve practical tasks. Result: Scenario data of the proposed technique was implemented as a systematic procedure by processing various earthquake-related information into logical data and simplifying and abstracting it for game expression for earthquake situation training. Conclusion: In the event of an earthquake due to learning through serious games, related public officials of Gyeongsangbuk-do provincial are expected to be able to respond quickly to various earthquake disasters.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.2
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• pp.73-78
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• 2022

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Journal of the Korean Society of Floral Art and Design
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• no.45
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• pp.47-56
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• 2021

It was applied by making the models such as the prior learning (e-learning), modeling by manual, learner's practice, 1:1 teaching coaching, self evaluation, coaching behavior assessment(primary, secondary), and self-directed practice. First, the cognitive practice education through the prior learning is very essential in the practice of floral design. Second, the practice class of floral design is a class where the professor generally set an example first, and the learners followed. Third, this study was to prepare the checklist, reflect it through the self evaluation, and prepare the evaluation form in accordance with the element, principle, and technical parts of floral design about the finished works. Fourth, contrary to the existing class completing within the class hour, the practice class is a process of trying to do self-directed practice, returning to home. Fifth, this study was to evaluate the works the learner made once again through the sketching and photographing by placing the work process of portfolio at the last step. To conclude, this study has found that such series of process through six steps on the practice form by the learner only would be excellent teaching learning model to improving the basic capacity of floral design. Accordingly, the development of teaching materials related to this and adaptation in the field in the future is considered as it will be very helpful to the learners' self-directed learning.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.171-176
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• 2023

The purpose of this study is to analyze the introduction and use cases of Metaverse in the financial sector to learn lessons and implications. Let's take a look. The era of the metaverse is coming. The financial sector is pioneering the blue ocean market in a new era and working with the MZ generation. In order to expand contact points, we are very interested in the new business model, Metaverse, and are actively engaged in research and development. appear to be participating. In the case of finance, information is efficiently transmitted through metaverse, and customers It is predicted that the convenience of customers will be greatly improved by making it possible to use convenient services without visiting a branch. Additionally, by utilizing technologies such as AR and VR, we are trying to provide services linked to the metaverse in earnest. In addition, new financial services such as non-face-to-face asset management consulting services and brokerage services for funds through Metaverse Business models are also expected to be created. It is still in its infancy, and it is currently in its infancy, Metaverse is being used for educational purposes.