• Journal of the Korean Society for Precision Engineering
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• v.16 no.9
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• pp.25-34
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• 1999

In spite of the advance of 3D solid modeling technology, there are some distinct areas where 2D CAD S/W are still dominant, and more competent comparing with 3D CAD S/W. For example, in the manufacturing of 2D-shaped electrical parts, most related manufacturing tools have 2D geometric features by nature, and 3D solid models applied to these parts have substantial overheads. Nevertheless, most 2D CAD S/W have no topological inquiry services because they have no such information on their geometrical database inherently. Thus, it is needed to extract such information from 2D CAD database for developing more advanced application such as automated drafting/design S/W. In this paper, the extraction of topological information from 2D CAD has been performed in general way using concept of B-rep. A general extraction algorithm, data structure and meta file format for 2D topological object have been developed and successfully applied to the development of the automated lead frame die design system in Samsung Aerospace. it is also possible to provide a flexible, powerful topology-oriented functionality on any common 2D CAD S/W.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1019-1021
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• 2013

오늘날 소프트웨어의 복잡화에 따라 그 테스트 방법 또한 다양화 되고 있다. 입력의 따른 결과 값을 비교하여 대상의 내부 구조에 대한 깊은 이해 없이 가능한 블랙박스 테스팅은 오늘날의 복잡한 소프트웨어의 테스팅에 있어 매우 적합한 방법이다. 하지만 이러한 테스트를 위한 테스트 케이스 생성을 위해서는 요구 명세와 더불어 이에 대한 깊은 이해를 필요로 한다. 이러한 문제를 해결하기 최근에는 UML과 같은 정형화된 명세 기반의 테스트 케이스 생성기법이 연구되고 있지만, 모델 기반의 개발 방법이 사용되지 않는 부분에서는 매번 이루어지는 코드 변경에 따라 모델을 다시 수정하는 번거로움이 필요하다. 이에 본 논문에서는 함수 레벨의 블랙박스 테스트를 위하여 코드를 이용하여 테스트 케이스를 생성하는 방법을 제안한다. 이를 위해 먼저 대상 함수를 추상화한 후 함수의 각 인자들을 이용하여 각 조건문 상의 인자의 쓰임을 분석하여 각 조건의 기준 값을 추출하고 이로부터 테스트 케이스를 추출하는 방법을 제안한다. 이러한 방법은 이미 구현되어 있는 코드를 사용함으로써 새로운 요구 명세에 대한 이해의 필요성을 줄이고 더불어 코드 기반의 테스트 케이스의 자동 생성연구의 초석이 된다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.45-46
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• 2021

본 논문에서는 제조산업의 제품 품질검사의 자동화를 위한 딥러닝 기법을 제안하고 모델의 성능 최적화를 위한 특징 추출 필터의 크기를 비교한다. 이미지 특징을 자동 추출할 수 있는 CNN을 사용하여 전문인력 없이 제품의 표면 결함을 검출하고 제품의 적합성을 판단할 수 있는 이미지 처리 알고리즘을 구축하고 산업 현장에 적용하기 위한 검증 지표로 검출 정확도와 연산속도를 측정하여 결함 검출 알고리즘의 성능을 확인한다. 또한 연산량에 따른 성능 비교를 위해 필터의 크기에 따른 CNN의 성능을 비교하여 결함 검출 알고리즘의 성능을 최적화한다. 본 논문에서는 커널의 크기를 다르게 적용했을 때 빠른 연산으로 높은 정확도의 검출 결과를 얻었다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.203-207
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• 2008

본 논문에서는 의료기관의 검사코드인 LOCAL 코드(Local Code)를 LOINC 코드(Local Laboratory Result Code)와 매핑하기 위한 알고리즘을 구현하고, 이를 기반으로, LOCAL 코드의 매핑 및 입력을 지원하는 새로운 시스템을 제안한다. 이를 위해, 먼저, LOCAL 코드를 변환하여 LOINC의 여섯 가지 속성을 추출한 대응 코드로 생성한다. 추출한 대응코드는 LOINC 코드와 매핑하는 키 값이 된다. 대응코드와 LOINC 코드의 매핑은 각 속성을 우선순위에 따라 비교하는 과정을 포함하며, 매핑 결과 수에 따라 자동 매핑되거나, 상 하위 개념을 조합 또는 System 코드를 재검색하는 결과 최소화 단계를 수행한다. 본 연구에서는 매핑 알고리즘을 기반으로 LOINC 코드로 매핑 하고, 새로운 LOCAL 코드를 LOINC 코드로 입력할 수 있는 새로운 시스템을 구축하였다. 본 연구의 목표는 LOINC를 활용하여 방대한 진단검사 결과데이터를 표준화하고, 이를 통해 의료 기관 간 EMR을 실현하고 구축하는 기반요소를 마련하는데 있다. 본 연구를 통해, 국내 의료기관 간 검사 결과의 통합과 호환이 가능하게 할 것이며, 검사코드의 표준화를 위한 기반요소를 마련할 수 있을 것으로 기대한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.27-35
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• 2013

Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.873-877
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• 2010

CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1429-1434
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• 2017

In automatic defect detection problem for spectacle lenses, it is important to extract lens area accurately. Many existing detection methods fail to do it due to insufficient minute noise removal. In this paper, we propose an automatic defect detection method using Bresenham algorithm and adaptive binarization strategy. After usual average binarization, we apply Bresenham algorithm that has the power in extracting ellipse shape from image. Then, adaptive binarization strategy is applied to the critical minute noise removal inside the lens area. After noise removal, We can also compute the influence factor of the defect based on the fuzzy logic with two membership functions such as the size of the defect and the distance of the defect from the center of the lens. In experiment, our method successfully extracts defects in 10 out of 12 example images that include CHEMI, MID, HL, HM type lenses.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.384-392
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• 2000

As a software becomes more complicated and large-scaled, it is very important for a software engineer to analyze user's requirements precisely and apply them effectively in the development stage. Due to the growth of the internet, the necessity of requirements elicitation and analysis in distributed environments has also become larger. This paper proposes a requirements elicitation supporting system that offer the basis for effectively analyzing requirements collected in distributed environments. The proposed system automatically categorizes collected requirements sentences into selected subject fields by measuring their similarity using a similarity measurement technique. Therefore, it reduces the difficulties in the initial stage of requirements analysis and it supports rapid and correct requirements analysis. This paper verifies the efficiency of the proposed system in similarity measurement techniques through experiments, and presents a process for requirements specifications elicitation using the embodied system

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.803-808
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• 2002

This paper describes automatic methods for detection of kidney and kidney tumor on abdominal CT scans. The abdominal CT images were digitalized using a film digitizer and a gray-level threshold method was used to segment the kidney. Based on texture analysis results, which were perform on sample images of kidney tumors, SEED region of kidney tumor was selected as result of homogeneity test. The average and standard deviation, which are representative statistical moments, were used to as an acceptance criteria for homogeneous test. Region growing method was used to segment the kidney tumor from the center pixel of selected SEED region using a gray-level value as an acceptance criteria for homogeneity test. These method were applied to 113 images of 9 cases, which were scanned by GE Hispeed Advantage CT scanner and digitalized by Lumisvs LS-40 film digitizer. The sensitivity was 85% and there was no false-positive results.