• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.395-400
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• 2020

Coronavirus disease 2019 (COVID-19) was identified in December 2019 in China and has spread globally, resulting in an ongoing pandemic. Because COVID-19 is spread mainly from person to person, every person is required to wear a facemask in public. On the other hand, many people are still not wearing facemasks despite official advice. This paper proposes a method to predict whether a human subject is wearing a facemask or not. In the proposed method, two eye regions are detected, and the mask region (i.e., face regions below two eyes) is predicted and extracted based on the two eye locations. For more accurate extraction of the mask region, the facial region was aligned by rotating it such that the line connecting the two eye centers was horizontal. The mask region extracted from the aligned face was fed into a convolutional neural network (CNN), producing the classification result (with or without a mask). The experimental result on 186 test images showed that the proposed method achieves a very high accuracy of 98.4%.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.859-864
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• 2006

The openness of the Web allows any user to access almost any type of information easily at any time and anywhere. However, with function of easy access for useful information, internet has dysfunctions of providing users with harmful contents indiscriminately. Some information, such as adult content, is not appropriate for all users, notably children. Additionally for adults, some contents included in abnormal porn sites can do ordinary people's mental health harm. In the meantime, since Internet is a worldwide open network it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse way, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.171-178
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• 2006

Most approaches for extracting hypernyms of a noun from its definitions in an MRD rely on lexical patterns compiled by human experts. Not only these approaches require high cost for compiling lexical patterns but also it is very difficult for human experts to compile a set of lexical patterns with a broad-coverage because in natural languages there are various expressions which represent same concept. To alleviate these problems, this paper proposes a new method for extracting hypernyms of a noun from its definitions in an MRD. In proposed approach, we use only syntactic (part-of-speech) patterns instead of lexical patterns in identifying hypernyms to reduce the number of patterns with keeping their coverage broad. Our experiment has shown that the classification accuracy of the proposed method is 92.37% which is significantly much better than that of previous approaches.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.65-71
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• 2009

Natural language processing is necessary in order to efficiently perform filtering tremendous information produced in information retrieval of world wide web. This paper suggested an algorithm for meaning of numerals in the text. The algorithm for meaning of numerals utilized context-free grammars with the chart parsing technique, interpreted affixes connected with the numerals and was designed to disambiguate their meanings systematically supported by the n-gram based words. And the algorithm was designed to use POS (part-of-speech) taggers, to automatically recognize restriction conditions of trigram words, and to gradually disambiguate the meaning of the numerals. This research performed experiment for the suggested system of the numeral interpretation. The result showed that the frequency-proportional method recognized the numerals with 86.3% accuracy and the condition-proportional method with 82.8% accuracy.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.4
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• pp.19-33
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• 2008

Dynamic response measurements from natural excitation were carried out for 25- and 42-story buildings to evaluate their inherent properties, such as natural frequencies, mode shapes and damping ratios. Both are reinforced concrete buildings adopting a core wall, or with shear walls as the major lateral force resisting system, but frames are added in the plan or elevation. In particular, shear walls in a 25-story building are converted to frames from the 4th floor level downwards while maintaining a core wall throughout, resulting in a fairly complex structure. Due to this, along with similar stiffness characteristics in the principal directions, significantly coupled and closely spaced modes of motion are expected in this building, making identification rather difficult. By using various state-of-the-art system identification methods, the modal parameters are extracted, and the results are then compared. Three frequency-domain and four time-domain based operational modal identification methods are considered. Overall, all natural frequencies and damping ratios estimated from the different identification methods showed a greater consistency for both buildings, while mode shapes exhibited some degree of discrepancy, varying from method to method. On the other hand, in comparison with analysis results obtained using the initial finite element(FE) models, test results exhibited a significant difference of about doubled frequencies, at least for the three lower modes in both buildings. To improve the correlation between test and analysis, a few manual schemes of FE model updating based on plausible reasons have been applied, and acceptable results are obtained. The advantages and disadvantages of each identification method used are addressed, and some difficulties that might arise from the updating of FE models, including automatic procedures, for such large structures are carefully discussed.

• Progress in Medical Physics
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• v.26 no.1
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• pp.52-58
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• 2015

The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.

• Review of KIISC
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• v.15 no.1
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• pp.41-49
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• 2005

정보화에 대한 의존도가 심화됨에 따라 사이버상의 테러는 기업과 국가안보를 위협하는 단계에까지 이르렀으나 아직까지 이를 방어하기 위한 정보보호시스템은 침해사고에 대한 정보가 공유되지 못하고 독립되어 운영되고 있는 실정이다. 이에 기업과 국가는 물론 전세계에서 발생되는 실시간 위협 상황에 대해 조기분석과 대응을 위한 정보공유의 필요성이 무엇보다 강조되고 있다. 본 논문에서는 종합침해사고대응시스템에서 침해사고에 대한 실시간 분석 및 대응을 위한 중요자인인 블랙리스트 DB 구축방법과 관리방안을 제시하였다. 인터넷상에서 광범위하고 지속적인 공격을 시도하는 공격 IP정보를 효율적으로 판별하고 추출한 IP를 실시간으로 자동대응할 수 있는 모델을 제안하였으며 사고 시나리오를 통해 통해 검증하였다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2037-2038
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• 2011

피부에 나타나는 난치성 질환인 백반증은 심리적인 위축감을 주어 정상적인 생활에 지장을 줄 수 있는 질병이다. 이에 따라서 본 연구에서는 피부에 나타나는 백반증의 진행 상태를 판단하기 위하여 L*a*b* 컬러 공간으로 변환된 피부 영상에 Otsu 임계값 설정 기법을 적용하여 백반증의 발병 영역을 자동으로 판별하는 알고리즘을 제안하였다.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.151-158
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• 1986

In this paper, the design and implementation of a microcomputer-based measuring system for quantitative analysis of oculomotor system are described. An algorithm for microcomputer analysis of electro-oculographicaay recorded horizontal saccadic eye movements is presented. From a brief, 4-min recording session detailed statistical information about saccade amplitude, duration, and velocity can be obtained.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.502-505
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• 2000

본 논문에서는 상태 전이 분석과 연관 마이닝 기법을 이용하여 새로운 침입 탐지 알고리즘인 침입 시나리오 자동 생성 알고리즘(Automatic Generation Algorithm of the Penetration Scenarios : AGAPS)을 개발하고자 한다. 침입을 탐지하기 위하여, 먼저 상태 전이 기법을 이용하여 네트워크를 통해 전달된 명령어들에 대한 상태 테이블을 생성한다. 그리고 연관 마이닝 기법을 이용하여 명령어들의 연관 규칙을발견한 후, 이러한 명령어들이 불법 침입과 관련된 명령어들인지를 판별함으로서 불법 침입 여부를 판단한다.