• International journal of advanced smart convergence
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• 제8권2호
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• 한국안전학회지
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• 제33권6호
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• pp.128-135
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• 2018

Currently, the government selects products that are likely to cause harm to consumers as safety management targets and classifies them into three types: safety certification, safety confirmation, and supplier conformity verification. In addition, the government conducts safety surveys on products in circulation or accident products, and recalls products that are of great concern to consumer risks. In this paper, we have developed RAP (Risk Assessment method based on Probability), which is a probability based product risk assessment method, for the classification of safety management type of product and safety investigation, and have shown an application example. In this process, information is used for the CISS (Consumer Injury Surveillance System) of the Korean Consumer Agency. In addition, we apply the cluster analysis to classify the current supervised children products into three groups. Then, we confirm the effectiveness of RAP by comparing the result of RAP application, cluster analysis result and current safety management classification type. Also, we recognize the need to review the current safety management classification criteria for classifying products into three types.

• 대한한의학원전학회지
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• 제36권3호
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• pp.89-128
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• 2023

Objectives : This study aims to diachronically examine the classification systems of all materia medica, followed by categorization and analysis of each category to deduce each category's characteristic. This will provide foundation for further examining classifications of bencao in contemporary herbology. Methods : Classification systems from a total of 93 bencao related texts were collected and categorized. Each category's classification system was analyzed to determine its meaning. The classification systems were compared from a diachronic perspective, to further deduce each system's problem from a historical context. Results : The classification systems of materia medica could be summarized as following three standards: quality, origin, and medical application. In reality, bencao could be generally classified according to origin and medical application. The origin-based classification system provided a stable and flexible classification outline in the expansion process of bencao. The medical application-based classification strengthened the relationship between bencao and illness pattern, improving clinical applicability. Conclusions : In the history of herbology, the two classification systems created the current of herbology through mutual contribution and conflict. We hope that further discussion on the direction towards which classification system of bencao in contemporary herbology should head will proceed based on this study.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• 한국환경복원기술학회지
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• 제20권3호
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• pp.1-18
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• 2017

This study constructed a high-resolution bioclimatic classification map of South Korea which classifies land into homogeneous zones by similar environment properties using advanced statistical techniques compared to existing ecological area classification studies. The climate data provided by WorldClim(1960-1990) were used to generate 27 bioclimatic variables affecting biological habitats, and key environmental variables were derived from Correlation Analysis and Principal Component Analysis. Clustering Analysis was performed using the ISODATA method to construct a 30'(~1km) resolution bioclimatic classification map. South Korea was divided into 21 regions and the results of classification were verified by correlation analysis with the Gross Primary Production(GPP), Actual Vegetation map made by the Ministry of Environment. Each zones' were described and named by its environmental characteristics and major vegetation distribution. This study could provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions.

• 대한산업공학회지
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• 제37권4호
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• pp.358-366
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• 2011

Multi-criteria ABC inventory classification, which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. The weighted linear optimization (WLO) model proposed by Ramanathan (2006) solves the problem of multi-criteria ABC inventory classification by generating a set of criterion weights for each inventory item and assigning a normalized score to the item for ABC analysis. However, the WLO model has some limitations. First, many inventory items can share the same optimal score, which can hinder a precise classification of inventory items. Second, the model allows too much flexibility in weighting multiple criteria; each item is allowed to choose its own weights so that it can maximize its score. As a result, if an item dominates the others in terms of a certain criterion, it may be classified into a higher class regardless of other criteria by assigning an overwhelming weight to the criterion. Consequently, an item with a high value in an unimportant criterion and low values in others may be inappropriately classified as class A, leading to an inaccurate classification of inventory items. To overcome these shortcomings, we extend the WLO model by using the cross-efficiency method in data envelopment analysis. We claim that the proposed model can provide a more reasonable and accurate classification of inventory items by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.

• International Journal of Oral Biology
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• 제43권2호
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.190-193
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• 2000

In this paper, we investigate the distribution of classification accuracies of multiclass problems in the feature space and analyze performances of the conventional feature extraction algorithms. In order to find the distribution of classification accuracies, we sample the feature space and compute the classification accuracy corresponding to each sampling point. Experimental results showed that there exist much better feature sets that the conventional feature extraction algorithms fail to find. In addition, the distribution of classification accuracies is useful for developing and evaluating the feature extraction algorithm.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.225-233
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• 2013

The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.