• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.1022-1031
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• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Korean Journal of Environment and Ecology
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• v.25 no.1
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• pp.47-56
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• 2011

The research was conducted to find optimal habitats of warm-temperate evergreen broad-leaved trees, and to investigate climate factors to determine their distribution using classification tree (CT) analysis. The warm-temperate evergreen broad-leaved trees model (EG-model) constructed by CT analysis showed that Mean minimum temperature of the coldest month (TMC) is a major climate factor in determining distribution of warm-temperate evergreen broad-leaved trees. The areas above the $-5.95^{\circ}C$ of TMC revealed the optimal habitats of the trees. The coldest month mean temperature (CMT) equitable to $-5.95^{\circ}C$ of TMC is $-1.7^{\circ}C$, which is lower than $-1^{\circ}C$ of CMT of warm-temperate evergreen broad-leaved trees. Suitable habitats were defined for warm-temperate evergreen broad-leaved trees in Korea. These habitats were classified into two areas according to the value of TMC. One area with more than$-5.95^{\circ}C$ of TMC was favorable to trees if the summer precipitation (PRS) is above 826.5mm; the other one with less than $-5.95^{\circ}C$ of TMC was favorable if PRS is above 1219mm. These favorable conditions of habitats were similar to those of warm-temperate evergreen broad-leaved trees in Japan. We figured out from these results that distribution of warm-temperate evergreen broad-leaved trees were expanded to inland areas of southern parts of Korean peninsula, and ares with the higher latitude. Finally, the northern limits of warm-temperate evergreen broad-leaved trees might be adjusted accordingly.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.829-836
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• 2005

The way to assign a splitting criterion for correct object classification is the main issue in all decisions trees. This paper describes new splitting rules for classification in order to find an optimal split point. Unlike the current splitting rules that are provided by searching all threshold values, this paper proposes the splitting rules that we based on the probabilities of pre assigned intervals. Our methodology provides that user can control the accuracy of tree by adjusting the number of intervals. In addition, we applied the proposed splitting rules to a set of image data that was retrieved by parameterized feature extraction to recognize image objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.1
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• pp.55-60
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• 1993

Forest occupied a part of natural ecosystem carries out a role of purifying air, preserving water resource, prevention of the breeding and extermination, recreation areas and etc that preserve and for me one's living environment. In this study, the classification for management of this forest is performed with Landsat TM Image. The classes are decided needle-leaf trees, broad-leaf trees, farming land and grass land, and water. When the TM digital images are classified on computer, water is represented in 7∼13 D.N. of 4th band. But the others is appeared similar mostly specific values so that must be done image processing. When the images compounded 2ed band and 3ed band are processed with ratio of enhancement. Needle-leaf treas is represented in l18∼136 D.N. of 1st band, broad-leaf trees in 72∼91 D.N. of 3ed band, farm land and glass land in 96∼120 of 3ed band. Forest Information is classified with M.L.C, an image classification method. The errors of needle-leaf trees, broad-leaf trees, farm land and grass land, and water are appeared each －7.43, ＋1.89,＋7.58 and －2.04 as compared the digital image with investigation on the scene. Finally, these results are useful for classification of forest vegetation with Landsat TM Image.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.371-377
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• 2010

In this study, we propose two informal hypothesis tests which may be useful in determining the number of trees in a random forest for use in classification. The first test declares that a case is 'easy' if the hypothesis of the equality of probabilities of two most popular classes is rejected. The second test declares that a case is 'hard' if the hypothesis that the relative difference or the margin of victory between the probabilities of two most popular classes is greater than or equal to some small number, say 0.05, is rejected. We propose to continue generating trees until all (or all but a small fraction) of the training cases are declared easy or hard. The advantage of combining the second test along with the first test is that the number of trees required to stop becomes much smaller than the first test only, where all (or all but a small fraction) of the training cases should be declared easy.

• Journal of Korean Society of Forest Science
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• v.31 no.1
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• pp.53-61
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• 1976

Though it is not easy for those who study dendrology to memorize all the scientific names of trees, the names remaines in their memory can facilitate the understanding of foreign technical books. The scientific name of a tree indicates characteristics of shape, color, and other aspects of the tree and by analyzing the name we can see common element found in other scientific names of trees. It is helpful to those who want to memorize and study the scientific names of trees if they understand their etymology. The preseut study is the seconds report of the investigation which aims at examining the etymology of the scientific names of native and foreign trees growing in Korea and their original names not only at the habitat but in Japan, China, England, Germany, and France. While the first report, which was made known in Theses Vol. 9. (The City College of Seoul 1975), is the examination of the scientific names of trees belonging to Gymnospermae, the present report is that of scientific names of trees belonging to Piperales: 2 families, 2 genera and 2 species; and trees belonging to Salicales: 1 family, 3 genera, 44 species, 16 varieties, and 3 forms. As the etymology of the scientific names of trees is made clear, this study will help those who want memorize the scientific names and study foreign technical books and it is also useful for international interchange of trees. The classification is depended chiefly on Dendrology by Prof. Lee Tchang-bok and "Plant Resources of Korea" shown in Biblography No. 10; the native names of trees on Jumoku Daizusetsu by Dr. Uehara; and etymology on A source-Book of Biological Names and Terms by E.C. Jager. In the column of etymology of the scientific names for genera, species, varieties and forms, Gr. stands for Greek, L. for Latin, NL. for New Latin, and genit. for genitive.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3286-3300
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• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.705-719
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• 2010

We investigated the speckle filtering effect in supervised classification of the C-band polarimetric Ground Based SAR image data. Wishart classification method was used for the supervised classification of the polarimetric GB-SAR image data and total of 6 kinds of speckle filters were applied before supervised classification, which are boxcar, Gaussian, Lopez, IDAN, the refined Lee, and the refined Lee sigma filters. For each filters, we changed the filtering kernel size from $3{\times}3$ to $9{\times}9$ to investigate the filtering size effect also. The refined Lee filter with the kernel size of bigger than $5{\times}5$ showed the best result for the Wishart supervised classification of polarimetric GB-SAR image data. The result also showed that the type of trees could be discriminated by Wishart supervised classification of polarimetric GB-SAR image data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.407-410
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

• Bulletin of the Korean Mathematical Society
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• v.45 no.1
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• pp.95-99
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• 2008

It is known that each eigenvalue of a real symmetric, irreducible, tridiagonal matrix has multiplicity 1. The graph of such a matrix is a path. In this paper, we extend the result by classifying those trees for which each of the associated acyclic matrices has distinct eigenvalues whenever the diagonal entries are distinct.