• The Journal of the Society of Korean Medicine Diagnostics
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• v.16 no.2
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• pp.59-70
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• 2012

Objectives : Pulse diagnosis by Expert is the only way to classify 8 Constitutions so the study to supplement classifying method by the questionnaire has developed and modified and ECM-32 System has designed in 2010. But analyzing with Decision tree had many nodes and 32 important questions omitted while processing the data. So this study was to classify the 8 constitution patients into 2 groups first and analyze its characters in consecutive order. Methods : The participants of this study were 1027 patients who classified into one of the 8 constitutions according to pulse diagnosis and answered 251 questionnaires in 2010. They were divided into sympathetic nerve acceleration constitution and parasympathetic nerve acceleration constitution and analyzed with decision tree. Results : The reponses of the questionnaire were analyzed with 4 methods of 5 scales interval method from 0 to 5, Na, Low(1,2), Medium(3), High(4,5), average value, Y/N dichotomy. Average Value had no significance. 1. From the 5 scale interval method 6 questionnaires with 7 nodes (F5e, B1d, F7f, F2a, F1b, C4L) were significant. The accuracy was 92.5%. 2. From L, M, H method 7 questionnaires with 7 nodes(F5e, B1d, F7f, F1a, B1c, C4L, P3d) were significant. The accuracy was 92.5%. 3. From Y/N dichotomy 9 questionnaires with 9 nodes( F5e, B1d, F7f, F1a, B1c, C4L, B1b, P1i, B2a) were significant. The accuracy was 93.18%. Conclusions : Based on this study, Yes or No dichotomy method was most significant and categorized among the 4 methods. Unlike previous studies which used interval scale method only, Y/N dichotomy method was more statistically significant with the questionnaire to supplement the method of pulse diagnosis. For further study by analyzing decision tree method in consecutive order, the patients can be divided into 8 Constitutions with higher significance with less questionnaires.

• Journal of the Korean Institute of Landscape Architecture
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• v.19 no.3
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• pp.41-54
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• 1991

This study was executed to analyze the vegetational landscape structure and administration planning of Younguan Royal Tomb forest by the classification and DCA ordination were applied to the study are in order to classify them into several groups based on environmental variables. Also Younguan Royal Tomb forest comparison of a pair of photographs taken in 1920s and 1990. Sixteen sites were sampled with clumped sampling method in september 1990 and five quadrats were examined in each site. By classification and DCA, the plant community were divided into two groups(A. japonica community, P. densiflora, Quercus acutissima community) by the humus. Pinus densiflora community of actual vegetion covered 15.9%, Alnus japonica 6.5% and Quercus community 65.6% of the total area. And the afforested vegetation of P. koraiensis. P. rigida and Juglans mandshurica covered 13.2%. DCA ordination showed that successional trends of tree species seem to be from P. densiflora, Q. aliena to Q. acutissima in P. densiflora community and from A. japonica community of the upper layer. By the comparison of couple photographs between 1920s and 1990 we can recognize the change of historical landscape composed of P. densiflora community, A. japonaca and those community shall be conserved by the disclimax method, i.e. the broadleaf vegetation in the underlayer in the P. densiflora community should be cleared out. Otherwise the vegetational landscape sructure in Younguan Royal Tomb is successed to progress succession to Q. acutissima landscape.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.1-24
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• 2016

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers' satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.41-47
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• 2011

Processing of the raw LiDAR data requires the high-end processor, because data form is a vector. In contrast, if LiDAR data is converted into a regular grid pattern by filltering, that has advantage of being in a low-cost equipment, because of the simple structure and faster processing speed. Especially, by using grid data classification, such as Quadtree, some of trees and cars are removed, so it has advantage of modeling. Therefore, this study presents the algorithm for automatic extraction of ground points using Quadtree and refion growing method from LiDAR data. In addition, Error analysis was performed based on the 1:5000 digital map of sample area to analyze the classification of ground points. In a result, the ground classification accuracy is over 98%. So it has the advantage of extracting the ground points. In addition, non-ground points, such as cars and tree, are effectively removed as using Quadtree and region growing method.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.275-284
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• 2001

This study was carried out to evaluate high resolution satellite imagery of IKONOS for classifying the land cover, especially forest type. The IKONOS imagery of 11km$\times$11km size was taken on April 24, 2000 in Bong-pyoung Myun Pyungchang-Gun, Kangwon Province. Land cover classes were water, coniferous evergreen, Larix leptolepis, broad-leaved tree, bare land, farm land, grassland, sandy soil and asphalted area. Supervised classification method with algorithm of maximum likelihood was applied for classification. The terrestrial survey was also carried out to collect the reference data in this area. The accuracy of the classification was analyzed with the items of overall accuracy, producer's accuracy, user's accuracy and k for test area through the error matrix. In the accuracy analysis of the test area, overall accuracy was 94.3%, producer's accuracy was 77.0-99.9%, user's accuracy was 71.9-100% and k and 0.93. Classes of bare land, sandy soil and farm land were less clear than other classes, whereas classification result of IKONOS in forest area showed higher performance than that of other resolution(5-30m) satellite data.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.2
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• pp.16-25
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• 2007

The purpose of the study is to analyse and classify the characteristics of Gongsanseong forest through phytosociological and quadrat method with 20 plot samples. In result, the forest was classified as Quercus acutissima community. The subcommunity was composed of Quercus aliena and Zelkova serrata. The average vegetation coverage of Quercus aliena subcommunity was upper tree layer (83%), lower tree layer (48%), shrub layer (39%), and herb layer (30%), while the average vegetation coverage of Zelkova serrata subcommunity was upper tree layer (76%), lower tree layer (52%), shrub layer (40%), and herb layer (45%). The order of importance value of the forest community with DBH 2cm above plants was Quercus acutissima (72.62), Quercus aliena (40.52), Prunus sargentii (19.81), Styrax japonica (19.39), Zelkova serrata (15.78), Robonia pseudoacacia (14.76), Quercus variabilis (13.83), Sorbus alnifolia (13.71), Platycarya strobilacea (10.74), Pinus densiflora (10.08), and Quercus serrata (9.31). According to breast diameter analysis results, it is expected that the importance value of Prunus sargentii and Zelkova serrata will be continuously increased while Quercus sp. is dominating the forest. With the result of ordination analysis, the relationship between the forest community and environmental factors could be affected by soil nutrition, moisture, and elevation etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.718-723
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• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.531-540
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• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.