• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.281-288
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• 2003

In karst formation area, the tunnel support system is an important factor for the tunnel safety during operation. This paper presents the simplified tunnel support systems to be adopt in karst formation. For the tunnel planned in the project area, karst features and the expected scenarios in the tunnel area were developed based on the results of the geological and geotechnical assessment. In order to provide specific supporting system and construction details for a wide range of possible karst features, the generalized typical support systems are developed according to the classification of karst features. In addition, the initial support systems and construction sequence for each karst feature are also presented in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1889-1893
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• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

• KIEAE Journal
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• v.9 no.2
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• pp.27-36
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• 2009

Due to rapid advances in science and technology and peoples life value, multi-dimensional functionality of the house has been possible and demanded. Among them, intellectual function and health support function appeared prominent and the former can support the later. The purpose of this study was to delineate health support planning features for smart home. Thirty six planning elements were extracted for initial pool for survey to find out what consumers demanded. Two hundred and nine data were collected through the web-survey. Important planning features were identified in relation to three different health dimensions that is physical/physiological, psychological, and social health. Generally consumers' responses were positive for all features. Major health friendly features highly demanded by consumers were found gas detect system, security system, and a call alarm system. The result of this study is expected to be used as a basic reference to develop strategies for smart home and to grasp current housing culture.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.365-368
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• 2005

We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.395-404
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• 2013

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1681-1689
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• 2010

Several studies on the use of Support Vector Machines (SVMs) for diagnosing rotating machinery have been successfully carried out, but the fault classification depends on the input features as well as a multi-classification scheme, binary optimizer, kernel function, and the parameter to be used in the kernel function. Most of the published papers on multiclass SVM applications report the use of the same features to classify the faults. In this study, simple statistical features are determined on the basis of time domain vibration signals for various fault conditions, and the optimal features for each fault condition are selected. Then, the optimal features are used in the SVM training and in the classification of each fault condition. Simulation results using experimental data show that the results of the proposed stepwise classification approach with a relatively short training time are comparable to those for a single multi-class SVM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.153-172
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• 2022

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29), Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.685-694
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• 2022

The goal of few-shot semantic segmentation is to build a network that quickly adapts to novel classes with extreme data shortage regimes. Most existing few-shot segmentation methods leverage single or multiple prototypes from extracted support features. Although there have been promising results for natural images, these methods are not directly applicable to the aerial image domain. A key factor in few-shot segmentation on aerial images is to effectively exploit information that is robust against extreme changes in background and object scales. In this paper, we propose a Mask-Guided Attention module to extract more comprehensive support features for few-shot segmentation in aerial images. Taking advantage of the support ground-truth masks, the area correlated to the foreground object is highlighted and enables the support encoder to extract comprehensive support features with contextual information. To facilitate reproducible studies of the task of few-shot semantic segmentation in aerial images, we further present the few-shot segmentation benchmark iSAID-, which is constructed from a large-scale iSAID dataset. Extensive experimental results including comparisons with the state-of-the-art methods and ablation studies demonstrate the effectiveness of the proposed method.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.499-517
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• 2008

In this paper, we propose an effective emotion feature extraction method for Korean and evaluate their availability in sentiment classification. Korean emotion features are expanded from several representative emotion words and they play an important role in building in an effective sentiment classification system. Firstly, synonym information of English word thesaurus is used to extract effective emotion features and then the extracted English emotion features are translated into Korean. To evaluate the extracted Korean emotion features, we represent each document using the extracted features and classify it using SVM(Support Vector Machine). In experimental results, the sentiment classification system using the extracted Korean emotion features obtained more improved performance(14.1%) than the system using content-words based features which have generally used in common text classification systems.

• International Journal of Contents
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• v.2 no.4
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• pp.1-7
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• 2006

The goal of Ambient Intelligence (AmI) is to build a smart environment for users where they are supported in some of their activities by many interaction mechanisms. The diversity of AmI characteristics requires special support from Operating Systems (OSes). In this paper, in order to support a conscious choice of an operating system for any specific AmI application, features requested by AmI systems were characterized and defined considering various applications. Then, characteristics of existing Operating Systems have been investigated in the context of AmI application support to relate their key characteristics to the typical requirements of AmI systems. Qualitative mapping table between AmI characteristics and as features has been proposed with an illustration of how to use it. As no as completely covers the range of characteristics required by AmI systems, challenging issues are summarized for the development of a new as and a product line of OSes.