• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Journal of the Korean Society for information Management
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• v.40 no.2
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• pp.137-156
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• 2023

As social and political paradigms change, public institution tasks and structures are constantly created, integrated, or abolished. From an effective record management perspective, it is necessary to review whether the previously established record classification schemes reflect these changes and remain relevant to current tasks. However, in most institutions, the restructuring process relies on manual labor and the experiential judgment of practitioners or institutional record managers, making it difficult to reflect changes in a timely manner or comprehensively understand the overall context. To address these issues and improve the efficiency of record management, this study proposes an approach using automation and intelligence technologies to restructure the classification schemes, ensuring records are filed within an appropriate context. Furthermore, the proposed approach was applied to the target institution, its results were used as the basis for interviews with the practitioners to verify the effectiveness and limitations of the approach. It is, aiming to enhance the accuracy and reliability of the restructured record classification schemes and promote the standardization of record management.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.1
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• pp.43-59
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• 2022

Electronic public records are classified simultaneously as production, a preservation period is granted, and after a certain period, they are transferred to an archive and preserved. This study intends to find a way to improve the efficiency in classifying transferred records and maintain consistent standards. To this end, the current record classification work process carried out by the National Archives of Korea was analyzed, and problems were identified. As a way to minimize the manual work of record classification by converging the required improvement, the process of identifying outlier candidates based on a list consisting of classified information of the transferred records was proposed and systemized. Furthermore, the proposed outlier discrimination process was applied to the actual records transferred to the National Archives of Korea. The results were standardized and constructed as a training data format that can be used for machine learning in the future.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.120-130
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• 2021

Recently, the government is reinforcing safety management at construction sites to prevent safety accidents in construction works, and the safety management plan of workers is an important situation. Meanwhile, modular construction is expected to be gradually expanded to middle and high-rise buildings, but active measures to ensure worker safety are insufficient. This study is a preliminary study of the development of a checklist for preventive worker safety management. The purpose of this study is to derive a checklist classification system for the safety management of workers in the field of modular construction by preceding studies, case analysis, and expert advisory opinions. The classification system consists of large categories of factory manufacturing, transportation, and on-site construction, and the sub-system consists of six sub-classes: foundation work, frame work, modular frame installation work, finishing work, and facility work. Among them, the sub-classification of modular frame installation work consists of 12 unit works, centering on module lifting and assembly module work, which are the main construction processes. And the classification system reflects the three main management factors and contents for defined safety management. It is expected that the research results of this study can contribute to efficient safety management at the modular construction site.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.99-107
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• 2024

While the demand for extrapolating video content horizontally or vertically is increasing, even the most advanced techniques cannot successfully extrapolate all videos. Therefore, it is important to determine if a given video can be well extrapolated before attempting the actual extrapolation. This can help avoid wasting computing resources. This paper proposes a video classifier that can identify if a video is suitable for horizontal extrapolation. The classifier utilizes optical flow and an adaptive Gaussian blur network, which can be applied to flow-based video extrapolation methods. The labeling for training was rigorously conducted through user tests and quantitative evaluations. As a result of learning from this labeled dataset, a network was developed to determine the extrapolation capability of a given video. The proposed classifier achieved much more accurate classification performance than methods that simply use the original video or fixed blur alone by effectively capturing the characteristics of the video through optical flow and adaptive Gaussian blur network. This classifier can be utilized in various fields in conjunction with automatic video extrapolation techniques for immersive viewing experiences.

• Parasites, Hosts and Diseases
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• v.21 no.1
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• pp.111-117
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• 1983

1979∼1980연 칠월초에 서부 비무장지대 (DMZ)에서 모기 유충채집을 실시한 바 Aedes alboscutellatus의 유충을 제집하여 사육한 결과 성충(♀,♂), 완전유충, 유충 및 편의 탈피각등 분류에 필요한 자료를 얻었다. 이 종은 Reisen등(1971)에 의하여 한국에서 처음 성충(♀)채집을 기록하였으나 이를 확증할 만한 표본이나 분류학적인 기재가 없어 분류상의 오독으로 간주되어 왔었다. 저자등은 본 채집을 통하여 분류상 필요한 모든 자료를 수집하였기에 성충(♀, ♂), 유충, 항, 자충생식기등의 특징을 Reinert(1973)의 기재와 상세히 비교 검토한 결과 웅충에서 gonostylar claw와 basal mesal lobe에 있는 강모의 수가 다르고 자웅충에서는 subspiracular area에 횐 비늘들이 모여있는 점을 발견하였다. 그러나 이들 차이점을 여기에서는 단지 지리적 변화로 간주하였으므로 더 많은 수의 채집을 통하여 보다 확실한 구각이 요구된다 이 표본의 장기보관을 위하여 암수 각 한개섹의 표본과 그에 수반되는 유충 및 편의 탈피곡 표본을 미국 Smithonian연구소에 보냈으며 나머지는 미 8군 예방의무부 곤충연구실에 보관되어 있다.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.855-865
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• 1996

In this paper, we propose a automation of the classification process for reusable software component and construction method of structured software components library. In order to efficient and automatic classification of software component, we decide the facets to represent characteristics of software component by acquiring semantic and syntactic information from software components descriptions in natural language, and compose the software component identifier or automatic extract terms corresponds to each facets. And then, in order to construct the structured software components library, we sore in the near location with software components of similar characteristic according to semantic similarity of the classified software components. As the result of applying proposed method, we can easily identify similar software components, the classification process of software components become simple, and the software components store in the structured software components library.

• Explosives and Blasting
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• v.20 no.3
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• pp.25-38
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• 2002

터널 굴착선 여굴(Overbreak)은 발파공법에 의한 괄착 중에 필연적으로 발생하는 현상으로서 숏크리트, 라이닝 등의 보강비 추가 발생과 버력 처리량의 증대로 공기 및 공사비를 증가시키는 주요한 요인으로 작용한다. 또한 터널 굴착선 암반의 손상으로 균열층이 형성되거나 부석이 발생하여 안전문제를 야기시키기도 한다. 이러한 여굴 발생은 천공오차, 발파패턴의 오류, 잘못된 화약선정, 불규칙한 암반 특성 등에 그 원인이 있으나, 지금까지 터널 여굴은 천공 및 발파기술에 의해 좌우된다라는 인식이 대부분이었다. 그러나 여굴 발생에 중요한 원인으로 터널 굴착선 암반의 특성과 이에 적합한 발파패턴 및 화약류를 들 수 있다. 본 연구는 여굴 발생에 영향을 미치는 암반상태를 파악하기 위해서 터널 굴착선 주변암반의 균열정도, 강도, 불연속면의 간격, 방향, 간극, 충전물 상태 등의 6가지 요소를 이용하여 암반을 분류하는 발파암 분류법(BI)을 새로 제안하였고, 이 분류에 따라 외곽 공의 간격과 장약밀도를 달리 하는 발파패턴을 정립하였다. 또한 화약의 순폭도와 Air Deck 효과를 이용하여 장약밀도를 조절할 수 있는 N.D.C(New Deck Charge) 발파공법을 개발함으로써 여굴을 최소화할 수 있었다.