• Journal of the Korean Orthopaedic Association
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• v.56 no.6
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• pp.540-545
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• 2021

A transverse fracture is the most common type of displaced patella fracture requiring surgery. These fractures are commonly fixed with parallel Kirschner wires or screws that cross the fracture line, often with an additional tension band. Nevertheless, conventional fixation methods of patella fractures have prevalent complications caused by the protrusion of wires or pins. These complications necessitate additional surgery for hardware removal, increase medical cost, and can limit the function of the knee joint. This paper reports cases treated with a minimally invasive load-sharing percutaneous suture of the patella tendon. The procedure provides reliable fixation for transverse patella fractures, minimizes soft tissue injuries, preserves blood flow, and reduces postoperative pain. In addition, the procedure also reduces the irritation and pain caused by the internal fixture, thereby reducing the risk of restricted knee joint movement.

• Smart Media Journal
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• v.11 no.9
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• pp.75-80
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• 2022

Recently, research and development on immersive virtual reality(VR) technology to provide a realistic experience is being widely conducted. To provide realistic experience in immersive virtual reality for VR participants, virtual environments should consist of high-realistic environments using 3D reconstruction. In this paper, to acquire 3D information in real space using multiple cameras in the reconstruction process, we propose a novel method of optimal camera placement for accurate reconstruction to minimize distortion of 3D information. Through our approach in this paper, real 3D information can obtain with minimized errors during environment reconstruction, and it is possible to provide a more immersive experience with the created virtual environment.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3528-3537
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• 2013

Evaluation indices of the panelizing optimization for Architectural freeform surfaces are proposed for quantitative evaluation through the case studies on panelizing optimization and evaluation index for Architectural freeform surfaces. Proposed evaluation items are adherence to original design intent, production ease, and continuity. The evaluation index for adherence to original design intent is surfaces fitness, the evaluation indices for production ease are planarity, planar panel ratio, and the evaluation indices for continuity are tangent continuity, and divergence. Algorithms are also suggested to compute the proposed evaluation indices.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.193-196
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• 2001

본 논문은 ATM(Abstract Timed Machine)으로 명세된 실시간 시스템에 대한 재/역공학 측면에서의 개발 및 검증을 위한 코드 변환기를 설계한다. ATM은 모드(mede), 전이(transition), 포트(per)로 구성되는데, 순공학 과정에서 실시간 시스템을 설계, 명세 하는 기존의 정형기법과는 달리 ATM은 소프트웨어의 순환공학 과정에서 사용하기 위해 설계되었다. ATM은 기존 정형기법이 순공학 과정에서의 특정 물리적 환경에서 실행되는 동적행위에 대한 부적절한 표현에 대해 순환공학에서 실시간 시스템의 속성은 물론 특정 환경과 동적 정보 등을 명세하기 위한 정형 기법으로서, 본 논문에서는 DoME을 이용하여 ATM 명세도구를 개발하고 이를 이용하여 실시간 시스템의 특정 요구사항을 위한 ATM을 명세한다. 또한 해당 ATM을 DOME/ATM 스크립트 파일로 저장하고 이에 대한 명세분석을 통해 노드와 관련된 정보를 추출하여 다른 분석도구가 이용할 수 있도록 DB에 저장하거나 매개 언어인 SRL/ATM으로 변환하며, 이러한 SRL/ATM으로부터 실행코드에 대한 관련 정보를 추출하여 실시간 시스템 개발 및 검증을 위한 Ada 코드를 생성할 수 있는 코드 변환기를 설계한다.

• The Journal of Bigdata
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• v.6 no.2
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• pp.169-179
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• 2021

Consumers' food consumption behavior is likely to be affected not only by structured data such as consumer panel data but also by unstructured data such as mass media and social media. In this study, a deep learning-based consumption prediction model is generated and verified for the fusion data set linking structured data and unstructured data related to food consumption. The results of the study showed that model accuracy was improved when combining structured data and unstructured data. In addition, unstructured data were found to improve model predictability. As a result of using the SHAP technique to identify the importance of variables, it was found that variables related to blog and video data were on the top list and had a positive correlation with the amount of paprika purchased. In addition, according to the experimental results, it was confirmed that the machine learning model showed higher accuracy than the deep learning model and could be an efficient alternative to the existing time series analysis modeling.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.565-575
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• 2022

Thanks to the latest developments in digital architectural technologies, free-form designs that maximize the creativity of architects have rapidly increased. However, there are a lot of difficulties in forming various free-form curved surfaces. In panelizing to produce free forms, the methods of mesh, developable surface, tessellation and subdivision are applied. The process of applying such panelizing methods when producing free-form panels is complex, time-consuming and requires a vast amount of manpower when extracting production data. Therefore, algorithms are needed to quickly and systematically extract production data that are needed for panel production after a free-form building is designed. In this respect, the purpose of this study is to propose mathematical algorithms for the automatic generation of production data of free-form panels in consideration of the building model, performance of production equipment and pattern information. To accomplish this, mathematical algorithms were suggested upon panelizing, and production data for a CNC machine were extracted by mapping as free-form curved surfaces. The study's findings may contribute to improved productivity and reduced cost by realizing the automatic generation of data for production of free-form concrete panels.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.236-236
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• 2021

최근 텍스트와 같은 비정형 데이터의 생성 속도가 급격하게 증가함에 따라, 이를 분석하기 위한 기술들의 필요성이 커지고 있다. 텍스트 마이닝은 자연어 처리기술을 사용하여 비정형 텍스트를 정형화하고, 문서에서 가치있는 정보를 획득할 수 있는 기법 중 하나이다. 텍스트 마이닝 기법은 일반적으로 각각의 분서별로 특정 용어의 사용 빈도를 나타내는 문서-용어 빈도행렬을 사용하여 용어의 중요도를 나타내고, 다양한 연구 분야에서 이를 활용하고 있다. 하지만, 문서-용어 빈도 행렬에서 나타내는 용어들의 빈도들은 문서들의 차별성과 그에 따른 용어들의 중요도를 나타내기 어렵기때문에, 용어 가중치를 적용하여 문서가 가지고 있는 특징을 분류하는 방법이 필수적이다. 다양한 용어 가중치를 적용하는 방법들이 개발되어 적용되고 있지만, 환경 분야에서는 용어 가중치 기법 적용에 따른 효율성 평가 연구가 미비한 상황이다. 또한, 환경 이슈 분석의 경우 단순히 문서들에 특징을 파악하고 주어진 문서들을 분류하기보다, 시간적 분포도에 따른 각 문서의 특징을 반영하는 것도 상대적으로 중요하다. 따라서, 본 연구에서는 텍스트 마이닝을 이용하여 2015-2020년의 서울지역 환경뉴스 데이터를 사용하여 환경 이슈 분석에 적합한 용어 가중치 기법들을 비교분석하였다. 용어 가중치 기법으로는 TF-IDF (Term frequency-inverse document frquency), BM25, TF-IGM (TF-inverse gravity moment), TF-IDF-ICSDF (TF-IDF-inverse classs space density frequency)를 적용하였다. 본 연구를 통해 환경문서 및 개체 분류에 대한 최적화된 용어 가중치 기법을 제시하고, 서울지역의 환경 이슈와 관련된 핵심어 추출정보를 제공하고자 한다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.211-215
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• 2009

As Programmable Logic Controllers (PLCs) are increasingly used to implement safety critical systems such as nuclear instrumentation & control system, formal verification for PLC based programs is becoming essential. This paper proposes a formal verification technique for PLC program implemented with function block diagram (FBD). In order to verify an FBD program, we translate an FBD program into a Verilog model and perform model checking using SMV model checker We developed a tool, FBD Verifier, which translates FBD programs into Verilog models automatically and supports efficient and intuitive visual analysis of a counterexample. With the proposed approach and the tool, we verified large FBD programs implementing reactor protection system of Korea Nuclear Instrumentation and Control System R&D Center (KNICS) successfully.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.891-897
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• 2022

In big data analysis, raw text data mostly exists in various unstructured data forms, so it becomes a structured data form that can be analyzed only after undergoing heuristic pre-processing and computer post-processing cleansing. Therefore, in this study, unnecessary elements are purified through pre-processing of the collected raw data in order to apply the wordcloud of R program, which is one of the text data analysis techniques, and stopwords are removed in the post-processing process. Then, a case study of wordcloud analysis was conducted, which calculates the frequency of occurrence of words and expresses words with high frequency as key issues. In this study, to improve the problems of the "nested stopword source code" method, which is the existing stopword processing method, using the word cloud technique of R, we propose the use of "general stopword corpus" and "user-defined stopword corpus" and conduct case analysis. The advantages and disadvantages of the proposed "unstructured data cleansing process model" are comparatively verified and presented, and the practical application of word cloud visualization analysis using the "proposed external corpus cleansing technique" is presented.