• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.693-694
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• 2015

This paper present a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HOG(histogram of oriented gradient) descriptor and the extracted features are learned by SVM(support vector machine) classifier. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the method yields accuracy of over 90% in decision.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.577-583
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• 2007

The character strings on chips and components are so tiny and numerous that it is a very difficult work for people to perform. In this paper, we propose a mark automatic checking system, which will determine whether chip is wrong-mark or not by recognizing characters on chips. Lots of faulty detection conditions and template matching methods are used to inspect the faulty mark items. The faulty detection classifies conditions as five kinds-darkness, matching, area, broken and branch. A series of experimentation show that the method proposed here can offer an effective way to determine wrong-mark on chips.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.667-668
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• 2007

운전하고 있으나 방사상계통 운전에 대한 정보공유가 미비하여 특정 계통조작시 정전으로 이어질 우려가 상존하고 있다. 본 논문에서는 방사상 계통과 환상계통을 자동 판단하여 계통조작시 정전사고로 이어질 수 있는 경우 사전 경고를 발생하여 정전조작을 미연에 방지할 수 있도록 하는 알고리즘을 개발하였다. 개발된 알고리즘은 SCADA와 미리 구축된 Rule Base를 이용하여 인근지역의 정보를 공유하고, 간단한 집합론을 이용한 수학적 연산작업을 통해 방사상계통인지 환상계통인지를 판단한다. SCADA 시뮬레이터를 통한 다양한 시험을 통해 개발된 알고리즘의 신뢰성을 검증하였으며 이를 구현한 방사상계통 자동판별시스템을 개발하여 실계통에 적용하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.81-86
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• 2022

In this work, we propose a method for automatically determining the domain of columns of file data structured by csv format. New data can be generated through convergence between data and data, and the consistency of the joined columns must be maintained in order for these new data to become an important resource. One of the methods for measuring data quality is a domain-based quality diagnosis method. Domain is the broadest indicator that defines the nature of each column, so a method of automatically determining it is necessary. Although previous studies mainly studied domain automatic discrimination of relational databases, this study developed a model that can automate domains using the characteristics of file data. In order to specialize in the domain discrimination of file data, the data were simplified and patterned using a regular expression, and the contents of the data header corresponding to the column name were analyzed, and the suffix used was used as a derived variable. When derivatives of regular expressions and suffixes were added, the result of automatically determining the domain with an accuracy of 95% greater than the existing method of 87% was derived. This study is expected to reduce the quality measurement period and number of people by presenting an automation methodology to the quality diagnosis of public data.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.371-373
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• 2013

정박중인 선박의 안전을 위하여 항해사 및 선장, 해상교통관제사(VTSO, Vessel Traffic Service Operator)는 수시로 선박이 주묘가 되는지 확인하여야 한다. 일반적으로 정박선의 주묘여부 판별은 선위측정에 의한 방법과 선체움직임을 감지하여 확인하는 방법이 있다. 하지만 VTSO는 실제 선박의 구체적인 정박상황 파악이 어려우므로 수신되는 위치정보를 바탕으로 주묘여부를 판별해야 한다. 본 연구는 VTS에서 AIS에 의해 수집되는 데이터를 활용하여 VTSO의 경험적 주묘여부 판별 알고리즘을 토대로 VTS에서 활용가능한 정박선 주묘판별 프로그램을 제시하고자 한다. 또한 구성된 프로그램을 완도주변해역에 적용하여 선박주묘여부를 판별하였다.

• Journal of Ginseng Research
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• v.26 no.1
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• pp.6-9
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• 2002

This study was undertaken to evaluate the automatic decision-making on the rusty root of fresh ginseng (Panax ginseng C.A. Meyer) by an image analyzer. Critical value of rusty root of ginseng by image analyzing was the percentage of grey value 0∼148 area (G 148) to the total area of grey value 0∼255. And the discriminant formula of rusty root of ginseng as follows; rusty root of ginseng : 6.68$\times$G(148) +3.74, normal ginseng : 2.86$\times$G(148) +9.96, and fitness rates of this formula were 89.8%. Also, we developed the automatic rusty root of decision-making program. As the result of this study, the automatic decision-making on the rusty root of fresh ginseng by an image analyzer seems to have high possibility.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.215-218
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• 2012

농업에 있어 해충 방제는 중요한 일이다. 특히, 볼록총채벌레는 최근 감귤원 해충 피해의 주요 해충으로 인식되어 주기적인 예찰이 이루어지고 있으나 성충의 크기가 0.8mm 정도로 작아 육안 식별에 어려움이 있다. 본 논문에서는 예찰 트랩에 포집된 볼록총채벌레를 자동으로 판별하기 위한 전처리 알고리즘을 제안하였다. 광학 현미경의 배율을 30, 40, 50배로 조정하여 영상을 획득하였고, 제안한 알고리즘을 적용하여 판별 성능을 측정한 결과, 50배율에서 가장 높은 판별율을 보였으나 더 많은 볼록총채벌레 영상을 대상으로 알고리즘을 테스트하고 개선할 필요가 있다.

• Proceedings of the Korean Society for Information Management Conference
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• 2006.08a
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• pp.169-174
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• 2006

웹 상의 기사들의 종류를 보도, 기획/해설, 인터뷰/대담, 논설, 신변잡기로 나누어 자동요약을 위한 프레임을 작성하였다. 각 기사 프레임들은 글 구조적으로 분석되어 '사건, 상황, 연관, 과정, 원인, 결과, 결론, 전망, 방안, 평가 등'과 같은 문단 슬롯이 부여되었고 문단 슬롯은 다시 문장 슬롯으로 세분되었다. 'if-needed' 패싯으로 육하원칙인 '주체, 객체, 시간, 장소, 원인, 방법'을 택하였다. 슬롯이나 패싯의 실제값들을 추출 표현하는 과정에서 문구의 수사적 역할과 단어 최상위 범주 및 줄거리 단위를 참조하였다. 기사의 유형과 문단 및 문장 슬롯을 판별하기 위해서 유형 판별 규칙과 슬롯 판별 규칙을 구비하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.905-908
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• 2010

Our purpose is to develope the system which detects splogs automatically among blogs on Web environment. After removing HTML of blogs, they are tagged by part of speech(POS) tagger. Words and their POS tags information is used as a feature type. Among features, we select useful features with $x^2$ statistics and train the SVM with the selected features. Our system acquired 90.5% of F1 measure with SPLOG data set.