• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.232-239
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• 2014

As online texts have been rapidly growing, their automatic classification gains more interest with machine learning methods. Nevertheless, comparatively few research could be found, aiming for Korean texts. Evaluating them with statistical methods are also rare. This study took a sample of tweets and used machine learning methods to classify emotions with features of morphemes and n-grams. As a result, about 76% of emotions contained in tweets was correctly classified. Of the two methods compared in this study, Support Vector Machines were found more accurate than Na$\ddot{i}$ve Bayes. The linear model of SVM was not inferior to the non-linear one. Morphological features did not contribute to accuracy more than did the n-grams.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.115-124
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• 2017

In this paper, a different technique to predict the effects of soil-structure interaction (SSI) on seismic response of building systems is investigated. The technique use a machine learning algorithm called Support Vector Regression (SVR) with technical and analytical results as input features. Normally, the effects of SSI on seismic response of existing building systems can be identified by different types of large data sets. Therefore, predicting and estimating the seismic response of building is a difficult task. It is possible to approximate a real valued function of the seismic response and make accurate investing choices regarding the design of building system and reduce the risk involved, by giving the right experimental and/or numerical data to a machine learning regression, such as SVR. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The results show that the performance of the technique can be predicted by reducing the number of real data input features. Further, performance enhancement was achieved by optimizing the RBF kernel and SVR parameters through grid search.

• Journal of the Korean earth science society
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• v.24 no.2
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• pp.82-92
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• 2003

This qualitative research describes unique features of seven exemplary science teachers' professional development activities. Description of excellence in effective teachers' professional development efforts will provide some insights into required inservice initiatives and support systems for teachers' professional learning. Exemplary science teachers' professional development activities can be classified into four types: (1) practicing classroom supervision, (2) participating in voluntary communities of teachers, (3) playing instructional leader roles in teacher training programs, and (4) continuing one's studies at a graduate school. One of the common features of these exemplary teachers was that they can both articulate what makes teachers professionals and practice in accordance with their perspectives. These exemplary teachers not only improved their own classroom practices, but also participated actively in various professional communities to share their practical knowledge with their colleagues. The teachers have formed special-interest groups to investigate better ways of science teaching. They also took an active role in teachers' in-service education. Teachers' quality practices lie at the heart of classroom change. However, it's important to remember that there must be a support system that sustains and encourages teachers' initiatives. The implications for the support system to encourage teachers' professional development efforts are discussed.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4311-4314
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• 2012

Posttraumatic growth (PTG) is the experience of positive change that occurs as a result of the struggle with highly challenging life crises. The need to understand PTG in relation to actual changes in an individual's life has recently been raised. Little is known about the role of social support in the experience of positive outcomes. The purpose of this study is to investigate the role of perceived social support in enhancing PTG in cancer patients. This study involved 105 cancer patients. The data were collected using a questionnaire that determined the socio-demographic features, posttraumatic growth inventory (PTGI) and perceived social support. Participants reported relatively high levels of PTG and social support. Total perceived social support, support from family, and friends were significantly positive associated with the development of PTG among cancer patients. Accordingly, the social surroundings of the patient should be informed about the importance of social support and how it helps the patient; they should be made aware of necessity of social support.

• Proceedings of the CALSEC Conference
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• 1998.10a
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• pp.129-140
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• 1998

ㆍ ECRC ㆍ Knowledge Management ㆍ KM technologies ㆍ KBTS System -Mistakes KMS -Discussion KMS -Distinguished Features(omitted)

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• Journal of Industrial Convergence
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• v.1 no.2
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• pp.125-140
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• 2003

This study examined testing condition when consumers have a strong ordinal preference and in a absence of established ordinal for a given attribute, how adding common features to two brands in a given product category affects brand choice. Research findings presented in this study are generally consistent with the preference confirmation view of choice. The data provides support for the proposition that (1) the effect of common features is moderated by the relative importance of brand attributes(Hypothesis 1) and (2) in the presence of primary attribute adding common features increases the likelihood of choosing the brand with the best value on this attribute(Hypotheses 2).

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.537-545
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• 2022

Emotional experience (James, 1884)[1] can affect people's behavior. There are few types of research on VR marketing(Maojun Zhou, Zeru Yan, 2018)[2] design based on emotional experience. This article is based on emotional evaluation theory and empirical research, and the VR marketing case "Buy+" online shopping platform (Wu Yongyi, 2016). It is concluded that there are three levels of emotional experience definition on VR marketing which decompose the features of the VR marketing design of "Buy+ as an online-shop" correspondingly and find out the design features of VR marketing from the perspective of emotional experience. Finally, through the analysis of the questionnaire data, it verified that vividness, functionality and effectiveness could represent the features of VR marketing design. Moreover, it analyzed the correlation among these factors. Vividness and functionality have the closest relationship among them. The definition, the components, and the correlation of the three-layer emotional experience obtained from this research can provide theoretical support and reference for other VR marketing designs.