• 한국농공학회지
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• 제45권3호
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• pp.73-83
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• 2003

Methods of fuzzy C-means have been used to characterize geotechnical information from static cone penetration data. As contrary with traditional classification methods such as Robertson classification chart, the FCM expresses classes not conclusiveness but fuzzy. The results show that the FCM is useful to characterize ground information that can not be easily found by using normal classification chart. But optimal number of classes may not be easily defined. So, the optimal number of classes should be determined considering not only technical measures but engineering aspects.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• 한국멀티미디어학회논문지
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• 제15권2호
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• pp.195-203
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• 2012

본 논문은 다중센서 영상을 이용한 결정 융합 기반의 지상 표적 분류 알고리즘 및 특징 추출 기법을 제안한다. 표적의 인식률 향상을 위하여 가중 투표 방법을 적용함으로써 개별 분류기로부터 획득된 결과를 융합하였다. 또한 개별 센서 영상 내에 속한 표적을 분류하기 위해 CCD 영상으로부터 획득한 CM 영상의 밝기 차이와 FLIR 영상 내 표적의 윤곽선 정보 및 차량과 포탑의 너비 비율을 이용하여 스케일과 회전변화에 강인한 특징들을 추출하였다. 마지막으로 실험을 통하여 본 논문에서 제안한 지상 표적 분류 알고리즘과 특징 추출 기법에 대한 성능을 검증한다.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2005년도 학술발표회 논문집
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• pp.90-101
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• 2005

For the site characterization at two representative inland areas, Gyeongju and Hongsung, in Korea, in-situ seismic tests containing boring investigations and resonant column tests were performed and site-specific ground response analyses were conducted using equivalent linear as well as nonlinear scheme. The soil deposits in Korea were shallower and stiffer than those in the western US, from which the site classification system and site coefficients in Korea were derived. Most sites were categorized as site classes C and D based on the mean shear wave velocity to 30 m, Vs30 ranging between 250 and 650 m/s. Based on the acceleration response spectra determined from the site-specific analyses, the site coefficients specified in the Korean seismic design guide underestimate the ground motion in the short-period band and overestimate the ground motion in mid-period band. These differences can be explained by the differences in the bedrock depth and the soil stiffness profile between Korea and western US. The site coefficients were re-evaluated and the preliminary site classification system was introduced accounting for the local geologic conditions on the Korean peninsula.

• 한국멀티미디어학회논문지
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• 제20권7호
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• pp.994-1003
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• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.426-429
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• 2006

Flood caused by Typhoon and severe rain during summer is the most destructive natural disasters in Korea. Almost every year flood has resulted in a big lost of national infrastructure and loss of civilian lives. It usually takes time and great efforts to estimate the flood-related damages. Government also has pursued proper standard and tool for using state-of-art technologies. High resolution satellite imagery is one of the most promising sources of ground truth information since it provides detailed and current ground information such as building, road, and bare ground. Once high resolution imagery is utilized, it can greatly reduce the amount of field work and cost for flood related damage assessment. The classification of high resolution image is pre-required step to be utilized for the damage assessment. The classified image combined with additional data such as DEM and DSM can help to estimate the flooded areas per each classified land use. This paper applied object-oriented classification scheme to interpret an image not based in a single pixel but in meaningful image objects and their mutual relations. When comparing it with other classification algorithms, object-oriented classification was very effective and accurate. In this paper, IKONOS image is used, but similar level of high resolution Korean KOMPSAT series can be investigated once they are available.

• 한국식품조리과학회지
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• 제22권3호통권93호
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• pp.299-306
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• 2006

The numerous varieties of coffee beans contain a wide range prices and qualities. While the varieties of green coffee beans can generally be distinguished by their appearance, this visual criterion is impossible after the roasting process. Therefore, we need to develop a classification method or device. In this study, the potential of a new type of electronic nose, fast gas chromatography based on a surface acoustic wave sensor(SAW), was evaluated for the classification of origins and blended commercial brands in roasted coffee beans. Eight blended commercial brands and the origins of four similarly roasted ground coffee beans(with no significant difference of color) were rapidly(90 sec/sample) classified. The reproductive results were easily understandable over the aroma image pattern by $VaporPrint^{TM}$. In conclusion, GC-SAW electronic nose can be applied to the classification of origins and commercial brands in roasted ground coffee beans and to e evaluation of the similarities and differences of volatile pattern between samples.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.433-438
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• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• 제어로봇시스템학회논문지
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• 제18권7호
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• pp.644-649
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• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

• 대한원격탐사학회지
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• 제27권6호
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.