• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3184-3190
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• 2013

These days, the great part of design processes in the field of ship or offshore manufacturing are planed and implemented using the customized CAD system for each ship-building companies. It means that all information for design and production could be extracted and reused at the useful other area cost considerable time and efforts. The representative example is the estimation of welding length and material amount which is demanded during the construction of ship or offshore structures. The proper estimation of welding material to be used and the usage of them at the stage of schedule planning is mostly important to achieve the seamless process of production and expect the costing in advance. This study is related to the calculation of welding length and needed material amount at the stage of design complete utilizing the CAD system. The calculated amount are classified according to welding position, stage, block, bevel and welding type. Moreover it is possible to predict the working time for welding operation and could be used efficiently for the cost management using the results of this research.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.556-565
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• 2009

In this paper, we propose a CAD software framework to automatically generate a shop drawing. Shop drawing is to draw the geometric figures representing an arrangement of steel bars for a concrete building on its structural design, based on its construction specifications and the design rules, and its well-formed process lead to be automated. A key point of the design automation is to minimize the user interactions by automatically recognizing the design specifications and to finally generate the shape of the geometric figures. The graphic pipeline of the proposed framework consists of four stages; a specification DB, specification extraction, binding, and rendering. To effectively extract all specifications only for a figure from the DB and bind them to its shape, we use a hierarchical approach; the specifications are classified into three common, structural, and figure classes, and each attribute is extracted in design phases. Based on our framework, we implemented a specialized CAD for shop drawing using AutoCAD and could easily update it according to user's demands.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.396-397
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• 2022

본 논문에서는 등기하 해석법을 이용하여 선형 탄성문제에 대한 형상 최적설계 기법을 개발하였다. 실용적인 공학문제에 대한 많은 최적설계 문제에서는 초기의 데이터가 CAD 모델로부터 주어지는 경우가 많다. 그러나 대부분의 설계 최적화 도구들은 유한요소법에 기초하고 있기 때문에 설계자는 이에 앞서 CAD 데이터를 유한요소 데이터로 변환해야 한다. 이 변환과정에서 기하 모델의 근사화에 따른 수치적 오류가 발생하게 되고, 이는 응답 해석뿐만 아니라 설계민감도 해석에 있어서도 정확도 문제를 발생시킨다. 이러한 점에서 등기하 해석법은 형상 최적설계에 있어서 유망한 방법론중 하나가 될 수 있다. 등기하 해석법의 핵심은 해석에 사용되는 기저 함수와 기하 모델을 구성하는 함수가 정확히 일치한다는 것이다. 이러한 기하학적으로 정확한 모델은 설계민감도 해석 및 형상 최적설계에 있어서도 사용된다. 이로 인해 높은 정확도의 설계민감도를 얻을 수 있으며, 이는 설계구배 기반의 최적화에 있어서 매우 중요하게 작용한다. 수치 예제를 통하여 본 논문에서 제시된 등기하 해석 기반의 형상 최적설계 방법론이 타당함을 확인하였다. 본 논문에는 등기하 해석법을 이용하여 선형 탄성문제에 대한 형상 최적설계 하였다.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.91-97
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• 2019

When a radar tracks the warhead of a ballistic missile, decoys of a ballistic missile put a heavy burden on the radar resource management tracking the targets. To reduce this burden, it is necessary to be able to separate the signal of the warhead from the received dynamic radar cross section (RCS) signal on the radar. In this paper, we propose the method of separating the dynamic RCS of each target from the received signal by the Hough transform which extracts straight lines from the image. The micro motion of the targets was implemented using a 3D CAD model of the warhead and decoys. Then, we calculated the dynamic RCS from the 3D CAD model having micromotion and verified the performance by applying the proposed algorithm. Simulation results show that the proposed method can separate the signals of the warhead and decoys at the signal-to-noise ratio (SNR) of 10dB.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.358-366
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• 2012

Cirrhosis is a consequence of chronic liver disease characterized by replacement of liver tissue by fibrosis, scar tissue and regenerative nodules leading to loss of liver function. Liver Cirrhosis is most commonly caused by alcoholism, hepatitis B and C, and fatty liver disease, but has many other possible causes. Some cases are idiopathic disease from unknown cause. Abdomen of liver Computed tomography(CT) is one of the primary imaging procedures for evaluating liver disease such as liver cirrhosis, Alcoholic liver disease(ALD), cancer, and interval changes because it is economical and easy to use. The purpose of this study is to detect technique for computer-aided diagnosis(CAD) to identify liver cirrhosis in abdomen CT. We experimented on the principal components analysis(PCA) algorithm in the other method and suggested texture information analysis(TIA). Forty clinical cases involving a total of 634 CT sectional images were used in this study. Liver cirrhosis was detected by PCA method(detection rate of 35%), and by TIA methods(detection rate of 100%-AGI, TM, MU, EN). Our present results show that our method can be regarded as a technique for CAD systems to detect liver cirrhosis in CT liver images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.34-43
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• 2010

Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.234-237
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• 2009

많은 건설 현장에서 카메라와 CCTV(Closed-circuit Television)와 같은 장비를 활용하여 건설 현장의 상황을 모니터링 하고 있다. 하지만 많은 작업이 실외에서 이루어지는 토목 건축공사의 특성상 적절한 수준의 영상 데이터를 축적하는 것은 쉽지 않은 일이다. 특히, 이미지 프로세싱기법을 사용 하여 자동화된 건설 관리의 수행 시, 영상 데이터의 품질에 따라 에러가 발생하여 건설 관리자가 잘못된 정보를 얻게 될 경우도 발생하게 된다. 본 연구에서는 케니엣지(Canny Edge) 인식기법과 워터쉐드(Watershed) 변환, 그리고 3D CAD Mask를 이용한 건축 구조물 기둥의 시공 상황 분석 기법에 근거하여, 영상 데이터 분석 시 오류를 최소화하기 위한 에러 제거 알고리즘을 제시한다. 실제 데이터와 비교를 통하여 그 활용 가능성 또한 검증한다.

• Annual Conference of KIPS
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• 2004.05a
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• pp.749-752
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• 2004

CT 영상에서 폐암 추출을 위한 컴퓨터지원진단시스템(CAD)에서 전처리 시스템은 매우 중요한 역할을 담당한다. 본 논문에서는 CT 영상에서 폐암 추출을 위한 전처리 기법을 소개한다. CT 영상에서 폐 영역 추출 과정에서 가장 먼저 수행되는 이진화를 위해 k-means 클러스터링 알고리즘을 이용하고, 비관심 영역 제거 방법으로 연결요소를 분석하고, 이진화 과정에서 발생한 폐 외곽 분실을 재구성하기 위해 Rolling Ball 알고리즘을 수행한다. 또한 분할된 폐 영역에서 폐암 후보자를 선출하기 위해 분할과정에서 수행하였던 이진화 방법을 폐 영역에 다시 한번 적용하고 잡음제거를 위해 모폴러지 기법을 사용한 전처리 기법을 제안한다.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.883-890
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• 2022

Purpose: Currently, in the case of domestic fire fighting facility design, it is difficult to secure highquality manpower due to low design costs and overheated competition between companies, so there is a limit to improving the fire safety performance of buildings. Accordingly, AI-based firefighting design solutions were studied to solve these problems and secure leading fire engineering technologies. Method: Through AutoCAD, which is widely used in existing fire fighting design, the procedures required for basic design and implementation design were processed, and AI technology was utilized through the YOLO v4 object recognition deep learning model. Result: Through the design process for fire fighting facilities, the facility was determined and the drawing design automation was carried out. In addition, by learning images of doors and pillars, artificial intelligence recognized the part and implemented the function of selecting boundary areas and installing piping and fire fighting facilities. Conclusion: Based on artificial intelligence technology, it was confirmed that human and material resources could be reduced when creating basic and implementation design drawings for building fire protection facilities, and technology was secured in artificial intelligence-based fire fighting design through prior technology development.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.