• 한국정보통신학회논문지
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• 제16권9호
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• pp.1870-1877
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• 2012

천이신호는 지속시간이 짧으면서 길이의 변화가 크고, 시변성 및 비정재성 특성을 갖는다. 이러한 천이신호의 식별에는 분석 프레임 단위로 참조신호에 대한 기준패턴을 만들어 입력신호와의 유사도를 비교하는 방법이 효과적일 수 있다. 본 연구에서는 참조신호의 기준패턴으로 프레임 기반의 특징벡터들에 대해 확률통계 모형인 정규혼합모델을 적용하는 방법을 제안하고, 다양한 수중 천이신호에 대한 식별 실험을 통해 제안한 방법의 타당성을 검증하였다.

• 한국농공학회논문집
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• 제49권1호
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• pp.23-33
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• 2007

Classification results of rural villages provide useful information about rural village characteristics to select similar villages in rural development project; many researches about regional classification have been practiced. Recently rural amenity was introduced as an alternative for rural development, and rural villages have been surveyed to find potential resources for rural development by 'Rural Amenity Resources Survey Project'. Accumulated information through this survey project could be used to classify rural villages. However existing rural classification method using statistical data is not efficient method to use rural amenity resources information described with text. We introduced Information Bottleneck Method (IBM) based on information theory and implemented this method to classification with rural amenity resources information of Yanggang-myen, Yeongdong-gun in Chungbuk province.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.2423-2428
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• 2008

A turbo classifier having a rotating rotor of two stage classification region has been developed to have a cut size of 1 micro meter. Particle number concentrations were counted using Aerosol Particle Sizer (APS, TSI co., USA) at inlet and outlet of the classifier. Partial classification efficiency was obtained at various rotation speeds, total flow rates, and feed rates of powders, and classification characteristic depending on design parameters was discussed. Classification performance was enhanced as rotation speed of rotor increased and total flow rate decreased.

• Geomechanics and Engineering
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• 제35권1호
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• pp.67-80
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• 2023

Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.449-452
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• 2023

The rapid development of the Internet has led to a significant increase in multimedia content in social networks. How to better analyze and improve video classification models has become an important task. Deep learning models have typical "black box" characteristics. The model requires explainable analysis. This article uses two classification models: ConvLSTM and VGG16+LSTM models. And combined with the explainable method of LRP, generate visualized explainable results. Finally, based on the experimental results, the accuracy of the classification model is: ConvLSTM: 75.94%, VGG16+LSTM: 92.50%. We conducted explainable analysis on the VGG16+LSTM model combined with the LRP method. We found VGG16+LSTM classification model tends to use the frames biased towards the latter half of the video and the last frame as the basis for classification.

• 산업공학
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• 제24권2호
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• pp.97-104
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• 2011

Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments' results show improved performance in various classification problems.

• ETRI Journal
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• 제21권4호
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• pp.41-51
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• 1999

This research is an attempt to obtain more accurate land cover information from LANDSAT images. Canonical correlation analysis, which has not been widely used in the image classification community, was applied to the classification of a LANDSAT images. It was found that it is easy to select training areas on the classification using canonical correlation analysis in comparison with the maximum likelihood classifier of $ERDAS^{(R)}$ software. In other words, the selected positions of training areas hardly affect the classification results using canonical correlation analysis. when the same training areas are used, the mapping accuracy of the canonical correlation classification results compared with the ground truth data is not lower than that of the maximum likelihood classifier. The kappa analysis for the canonical correlation classifier and the maximum likelihood classifier showed that the two methods are alike in classification accuracy. However, the canonical correlation classifier has better points than the maximum likelihood classifier in classification characteristics. Therefore, the classification using canonical correlation analysis applied in this research is effective for the extraction of land cover information from LANDSAT images and will be able to be put to practical use.

• 한국도서관정보학회지
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• 제43권3호
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• pp.79-100
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• 2012

중국도서관분류법(中國圖書館分類法)(CLC: Chinese Library Classification)은 중국에서 가장 널리 통용되는 국가 표준분류법으로 1975년에 초판을 발행한 이래 2010년에는 제5판을 출판함으로써 약 9년마다 개정을 하고 있다. 이 연구에서는 CLC의 성립배경과 발전과정 그리고 제5판의 특성과 개정내용을 분석함으로써 한국과 문화적으로 오랜 관련성을 유지해온 중국의 대표적인 분류법을 고찰하고 한국십진분류법(KDC) 전개에 참고할 시사점을 파악하였다.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• 한국식품조리과학회지
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• 제26권3호
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• pp.300-306
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• 2010

Arabica coffee has been classified for trading according to the New York Board of Trade (NYBOT) green coffee classification. The aim of the present study is to evaluate the physiochemical and sensorial characteristics of coffees classified by NYBOT as NY2 (specialty coffee), NY3/4 and NY4/5 (commercial coffee). The density of green coffee was higher for the NY2 sample. The amount of total solids in brewed coffee increase as the green coffee grade decreased and the pH levels decreased as the coffee grade decreased. Descriptive analysis using a 15cm line scale was carried out by 12 trained panelists prepared by espresso coffee and consumer preference tests were carried out by 168 consumers. The NY2 sample had the highest fruity, acidity, sweetness and aftertaste characteristics. In addition, green, rio, sour and astringency characteristics increased as the coffee grade decreased. Acceptance of aroma, flavor, taste balance and overall were higher for the NY2 sample. In the consumer preference test, the NY2 and NY4/5 samples had a similar distribution, but consumers between the ages of 20 to 30 who like to drink brewed coffee more than instant coffee preferred the NY2 than sample over the NY3/4 sample. In conclusion, significant differences were observed among the three groups of green coffee classification in all physicochemical and sensory parameters.