• 인지과학
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• 제19권4호
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• pp.499-517
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• 2008

본 논문에서는 한국어 감정 분류에 기반이 되는 감정 자질 추출의 효과적인 추출 방법을 제안하고 평가하여, 그 유용성을 보인다. 한국어 감정 자질 추출은 감정을 지닌 대표적인 어휘로부터 시작하여 확장할 수 있으며, 이와 같이 추출된 감정 자질들은 문서의 감정을 분류하는데 중요한 역할을 한다. 문서 감정 분류에 핵심이 되는 감정 자질의 추출을 위해서는 영어 단어 시소러스 유의어 정보를 이용하여 자질들을 확장하고, 영한사전을 이용하여 확장된 자질들을 번역하여 감정 자질들을 추출하였다. 추출된 한국어 감정 자질들을 평가하기 위하여, 이진 분류 기법인 지지 벡터 기계(Support Vector Machine)를 사용해서 한국어 감정 자질로 표현된 입력문서의 감정을 분류하였다. 실험 결과, 추출된 감정 자질을 사용한 경우가 일반적인 정보 검색에서 사용하는 내용어(Content Word) 기반의 자질을 사용한 경우보다 약 14.1%의 성능 향상을 보였다.

• 한국문헌정보학회지
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• 제45권1호
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• pp.29-49
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• 2011

한식은 세계화의 충분한 잠재력과 가능성을 갖고 있으며 한식의 다양성과 특수성이 국가경쟁력을 제공하는 국가 홍보 전략의 하나가 될 수 있다. 이를 위해서 가장 먼저 바탕이 되어야하는 것이 한식과 관련해서 쏟아져 나오는 정보의 조직화이다. 따라서 본 연구는 한식에 관한 자료의 분류 현황 및 사례 분석을 바탕으로 한식이 문헌분류표에 반영된 정도와 앞으로 개선되어야할 사항을 파악하고 DDC의 수정 전개안의 제안을 통해 DDC 22판 개정의 근거와 국내 도서관의 DDC 수정 전개 활용을 제공하고자 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• 대한원격탐사학회지
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• 제34권4호
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• pp.591-600
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• 2018

Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.

• 인터넷정보학회논문지
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• 제24권1호
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• 한국컴퓨터정보학회논문지
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• 제20권3호
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• pp.81-88
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• 2015

최근 보안(security)분야는 클라우드 컴퓨팅(cloud computing)이나 사물 인터넷(internet of things) 등과 같은 다양한 기술들이 등장하면서 중요성이 더욱 부각되고 있다. 이러한 가운데 국내에서는 보안 분야를 정보 보안(information security)과 물리 보안(physical security), 융합 보안(convergence security)으로 분류하고 있으며, 이와 같은 국내 보안 분류체계는 산업 분야별 현황 분석 및 통계와 로드 맵 등에 매우 중요한 기준이 되고 있다. 이러한 분류체계 중, '융합 보안'은 다양한 산업 분야로부터 많은 주목을 받고 있으나, 국내에서는 '융합 보안'에 대한 분류체계를 관련 기관별로 달리 하고 있어, 데이터의 정확성과 호환성 등에 신뢰성이 결여되는 등의 문제로 체계적인 보안 분야의 분류체계가 필요한 실정이다. 따라서 본 논문은 국내의 분류체계의 현황과 특징들을 사례를 통해 비교 분석함으로써, 분류 항목의 추가 및 삭제가 용이하고, 새로운 기술동향에 적합한 확장이 용이하도록 향상된 분류체계를 제안하고자 한다. 향후, 제안하는 분류체계는 국내 보안 분류체계의 구축을 위한 자료로 활용될 것으로 기대한다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.383-387
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• 2007

The improved accuracy of the Land Cover/Land Use Map constructed using Hyperspectal Satellite Image and the possibility of real time classification of Land Use using optimal Band Selective Factor enable the change detection from automatic classification using the existed Land Cover/Land Use Map and the newly acquired Hyperspectral Satellite Image. In this study, the effective analysis techniques for automatic generation of training regions, automatic classification and automatic change detection are proposed to minimize the expert's interpretation for automatic update of the Land Cover/Land Use Map. The proposed algorithms performed successfully the automatic Land Cover/Land Use Map construction, automatic change detection and automatic update on the image which contained the changed region. It would increase applicability in actual services. Also, it would be expected to present the effective methods of constructing national land monitoring system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.945-950
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• 1990

An automatic visual classification system is introduced which provides for measuring the length and diameter of coilform cores and dividing them into 5 different classed in terms of how far their length be from the desired length. This task is fully automated by controlling two STEP motors and by using image processing techniques. The classification procedure is broken into three logical parts, First, cores in the form of randomly stacked bundle are lined up one by one so as to be well captured by a camera. The second part involves capturing core image. Then, it enters the measuring process. Finally, this machine would retain all the information relating to the length. According to the final result, cores are sent to the corresponding bin. This considerably simplifies the selecting task and facilitates a greatly improved reliablity in precision. The average classifying capability is about 2 pieces per second.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.302-307
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• 1990

An automatic visual classification system is introduced which provides for measuring the length and diameter of coilform cores and dividing them into 5 different classes in terms of how far their length be from the desired length. This task is fully automated by controlling two STEP motors and by using image processing techniques. The classification procedure is broken into three logical parts. Fist, cores in the form of randomly stacked bundle are lined up one by one so as to be well captured by a cameras. The second part involves capturing core image. Then, it enters the measuring process. Finally, this machine would retain all tire information relating to the length. According to the final result, cores are sent to the corresponding bin. This considerably simplifies the selecting task and facilitates a greatly improved reliability in precision. The average classifying capability about 2 pieces per second.