• 한국환경복원기술학회지
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• 제20권3호
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• pp.19-31
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• 2017

Plants are the basis for sustainable green growth, and the value of existence and importance of trees including landscape Plants can't be emphasized enough. Therefore, they are precious living things thriving in all sorts of public services, and continuous civil complaints for justifiable compensation of landscape Plants are filed. First, the standard formula of planting intervals according to production target specifications is calculated using root-collar caliper and diameter at breast height, and apply (1) standard medium sized trees which have not yet reached commercialization [deciduous tree production goal (R(B) less than 6cm]= (target standard)= [target standard $R(cm){\times}15{\times}0.7$]. (2) In case of commercialization(R6~R10)= [target standard $R(cm){\times}15{\pm}5%$], (3) In case of more than R12= [target standard $R(cm){\times}15{\times}130%$] shall be applied. In case of using diameter at breast height (4) In case of commercialization(B6~B10)= [target standard $B(cm){\times}20{\times}15{\pm}5%$], (5) In case of more than B12= [target standard $B(cm){\times}20{\times}130%$] shall be applied. Second, appraisal methods based on tree classification of compensation for loss are classified according to planted locations. (1) landscape trees within a house=[price of arrival at the site+planting cost], (2) landscape trees in places such as arboretum=[management technology of tress + relocation expenses considering scarcity of the trees (3) landscape trees in a place of loads= [landscape tree production cost + work out added price. In case of producted landscape threes (4) landscape trees ready to be commercialized as sales loss.

• 대한산업공학회지
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• 제33권2호
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• 품질경영학회지
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• 제36권1호
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• 대한산업공학회지
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• 제40권4호
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• pp.375-381
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• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.

• 서비스연구
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• 제3권1호
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• pp.9-16
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• 2013

데이터 분석가에게 많은 노력이 요구되지 않으면서 사용자가 쉽게 분석결과를 이해할 수 있는 범용 분류기법으로서 가장 대표적인 것은 Breiman이 개발한 의사결정나무를 들 수 있다. 의사결정나무에서 기본이 되는 2가지 핵심내용은 독립변수의 차원 공간을 반복적으로 분할하는 것과 평가용 데이터를 사용하여 가지치기를 하는 것이다. 분류문제에서 반응변수는 범주형 변수여야 한다. 반복적 분할은 변수 의 차원 공간을 겹치지 않는 다차원 직사각형으로 나눈다. 여기서 변수는 연속형, 이진 혹은 서열의 척도이다. 본 논문에서는 새로운 사례를 분류함에 있어서 분류의 성능을 평가하기 위해 분류나무의 정확도 정밀도 재현률 등을 실험하고자 한다.

• 한국산림과학회지
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• 제34권1호
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• pp.47-56
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• 1977

I have investigated and compared the morphology of vascular bundle shown in the section of culm wall of bamboo trees growing on Mt. Jiri which were classified by Grosser and Liese with their methods of morphological classification. The results obtained were as follows: 1. It was shown that there are no b.g.i. types of bamboo classified by Grosser and Liese among the bamboo trees on Mt. Jiri (Phyllostachys and Sasa). 2. As for the thickness of the culm wall in the culm, it was shown that the culm wall of the Phyllostachys becomes thinner in proportion to its nearness to the upper part of the tree, but no distinctive difference appeared in the Sasa. 3. The c, d, and e types of Sasa were the same as those of the Phyllostachys, but there was a vascular bundle type of the a' type, which was quite different from that of the Phyllostachys. 4. It was shown that the a', d, and e types of Sasa were distributed in a zone less than 500m above sea level, but no a' type was distributed in the high mountain area except for the c, d and e types which ranged from 600m to 1000m above sea level. Such facts mean that the vascular bundle sheath has changed in quantity because of the height of mountain. 5. In general, as compared with the Phyllostachys, the Sasa (types a, c, d and e which included a new type a) have fewer vascular bundles. 6. Considering the above results, it is thought that not by the current Sasa classification method based on observation of the the study of Sasa form the outside, but by a new method of classification based on the aspect of the physiological construction as seen from the inside wall is advanced. I believe this new method of classification to be a first step towards an epoch-making methodological advance and encourage the further study of it.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1409-1411
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• 2003

The application of simulated KOMPSAT-2 imagery to monitor graveyards is to be developed. Positions calculated from image were compared with those obtained from Geographic Positioning System. With 24 checkpoints, the position of graveyards showed within 5-meter range. Unsupervised classification, supervised classification, and objected-orientation classification algorithms were used to extract the graveyard. Unsupervised classification with masking processes based on National topographic data gives the best result. The graveyards were categorized with four types in field studies while the two types of graveyards were shown in descriptive statistics. Cluster Analysis and discriminant analysis showed the consistency with two types of tombs. It was hard to get a specific spectral signature of graveyards, as they are covered with grasses at different levels and shaded from the surrounding trees. The slopes and aspects of location of graveyards did not make any difference in the spectral signatures. This study gives the basic spectral characteristics for further development of objected-oriented classification algorithms and plausibility of KOMPSAT-2 images for management of mountainous areas in the aspect of position accuracy and classification accuracy.

• 한국산림과학회지
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• 제25권1호
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• pp.13-47
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• 1975

대나무류(類)는 열대방지(熱帶方地)에서 총생(叢生)하는 대형(大型)의 대나무가 많고, 온대(溫帶)가 되면 소형(小型)의 세류(笹類)로 퇴화(退化)하여, 그 종류(種類)는 세계(世界)에 50속(屬) 1,000여종(餘種)이나 있다고하여 그 종류수(種類數)는 대단(大端)히 많다. 이와 같은 대나무는 동양(東洋)에서는 건엽용(建葉用)과 공예용(工藝用)으로 이용(利用)되고있으며, 또한 죽순(竹筍)은 식용(食用)으로서 특유(特有)한 맛이있어 상용(賞用)되고있을 뿐만아니라 최근(最近)에는 세류(笹類)의 액즙(液汁)이 암(癌)에도 효과(効果)가 있다고 하여 중요(重要)한 산업(産業)으로서 발달(發達)하게되었다. 그리고 동남아(東南亞)에서는 삽목(揷木)으로서 용이(容易)하게 증산(增産)하여 pulp재(材)로 사용(使用)하게되여 대단(大端)히 중요시(重要視)하고있다. 이와같이 대나무는 인류생활(人類生活)에 필요부가결(必要不可決)한 목본식물(木本植物)이지만 아직까지 그 형태(形態)의 분류(分類)가 명확(明確)하게 되어 있지 않고 지연되여 있는 형편이다. 18세기(世紀) 중반기(中半期)의 Linne시대(時代)에 들어와서 생식기관(生殖器管)을 주체(主體)로 한 그 형태(形態)의 분류체계(分類體系)가 만들어진 이래(以來) 수정(修正)이 거듭되여온 바 있으나 대나무는 개화(開花)가 일정(一定)한 주기(周期)가 있어서 60-120년(年)의 기나긴 세월(歲月)이 소요(所要)되기 때문에 그의 형태적(形態的) 분류체계(分類體系)를 완성(完成)하는일은 극(極)히 어려운 일이었다. 오늘날까지 대나무에 관(關)한 많은 문헌(文獻)이 있기는 하나 그 시료(試料)가 적기때문에 불확실(不確實)한 기재(記載)도 많이있었고 속(屬)이 변경(變更)된것도 간혹(間或)있어서 그것을 공인(公認)할수 없는 것이 많이 있다. 그래서 내부(內部)의 형태적(形態的)인 분류(分類)에 관(關)하여는 근년(近年)에 이르러 중국(中國)의 Liese씨(氏)에 의(依)하여 겨우 시작(始作)이 되였으며, 또한 독일(獨逸)의 Grosser씨등(氏等)이 유관속(維管束)의 형태(形態)에 착안(着眼)하여 새로운 형태(形態)의 분류(分類)를 시도(試圖)한바있다. 그러나 이들의 이 형태(形態)에 관한 분류(分類)는 Holttum의 자방(子房)의 형태(形態)에 의(依)한 분류(分類)와 밀접(密接)한 관계(關係)가 있는것 뿐이었다. 따라서 필자(筆者)는 유관속초(維管束鞘)의 형태(形態)에 의(依)해 자유중국산(自由中國産)의 대나무 11속(屬) 26종류(種類)를 재료(材料)로하여 대나무의 형태(形態)에 관(關)한 분류체계(分類體系)에 대(對)하여 재고(再考)를 시도(試圖)하여 보았다. 그 결과(結果) Grosser씨(氏) 등(等)의 형태(形態)의 분류(分類)와 일부(一部)는 일치(一致)하였으나, Bambusa와 Dendrocalamus는 분류(分類)하기가 곤란(困難)하였던 것을 고립유관속초(孤立維管束鞘)의 존부(存否)로서 명확(明確)히 구별(區別)할수있었고 또한 종류(種類)가 많은 Bambusa를 2개(個)의 형(型)으로 나눌수가 있었다. 따라서 이 결과(結果)는 앞으로 아속(亞屬)이나 혹(或)은 절(節)로서의 분류(分類)로서 고려(考慮)되여야 할 문제(問題)라고 생각한다. 왜냐하면 이 근거(根據)는, 죽간(竹稈)의 최외층(最外層)에서 최내층(最內層)으로 향(向)하여 변화(變化)하고있는 형태(形態)에 착안(着眼)하여, 그 분화적(分化的)인 면(面)을 관찰(觀察)하였기 때문이다. 이 형태적(形態的)인 분화(分化)에 관(關)한 관찰(觀察)은 형태분류(形態分類)의 철칙(鐵則)인 "간단(簡單)한 것에서 복잡(複雜)한 것으로 진화(進化)한다"라고하는 철칙(鐵則)에 있어서도 충분(充分)히 조사(調査)하여 보았는데, 그 결과(結果)는 생식기관(生殖器官)의 형태적분류(形態的分類)와 대조(對照)하여 결정(決定)되어야 할 것이며, 금후(今後)의 문제(問題)로서 계속연구(繼續硏究)할까 생각한다.

• The Korean Journal of Ecology
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• 제23권6호
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• pp.453-460
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• 2000

The forest vegetation of Mt. seondal was classified into eight communities and one afforestation by the phytosocialogical method (Z-M method). In general, Quercus mongolica trees occupied most of the area, while afforestation forest was distributed on the lower slope, cultivated land, and at the vicinity of village. The vegetation on the top part of Mt. Seondal was comparatively well preserved, but that in the lower areas has been disturbed heavily by human activity and some had mixed forests composed of pine trees, oaks, ashes, and Rhododendron micrantum shrub. By cluster analysis method. nine groups were identified as follows : Quercus mongolica group, Q. mongolica - Pinus densiflora group, Q. mongolica - Rhododendron schlipen - bachii group, Q. mongolica - Symplocos chinensis for. pilosa group, P. densiflora group, Juglans mandshurica group, Fraxinus mandshurica group, Betula costata group and Larix leptolepis group. These groups showed differences in species composition, but Quercus mongolica, Q. mongolica - P. densiflora, Q. mongolica - R. schlippenbachii and Q. mongolica - S. chinensis for. pilosa groups among them showed very similar floristic composition to each other. In the relationship between polar ordination axes and environmental variables, altitude was the environmental factor determining variation in species composition along axis X and soil moisture was the environmental along axis Y. They were the main factors in determining forest vegetation. The result of cluster analysis and polar ordination for the forest vegetation were corresponded to those of phytosocialogical classification in classifying vegetation.

• Computers and Concrete
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• 제16권5호
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.