• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.5
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• pp.97-104
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• 2017

Greenspace enlargement through proper landscape planting is essential to creating a low carbon society. This study analyzed changes in stem diameter growth rates(DGR), ratios of below ground/above ground biomass(B/A), and carbon sequestration by age of major landscape tree species. Landscape trees for study were 11 species and 112 individuals planted in middle region of Korea. The DGR and B/A were analyzed based on data measured through a direct harvesting method including root digging. The carbon sequestration by tree age was estimated applying the derived regression models. The annual DGR at breast height of trees over 30 years averaged 0.72 cm/yr for deciduous species and 0.83 cm/yr for evergreen species. The B/A of the trees over 30 years averaged 0.23 for evergreen species and 0.40 for deciduous species, about 1.7 times higher than evergreen species. The B/A by age in this study did not correspond to the existing result that it decreased as tree ages became older. Of the study tree species, cumulative carbon sequestration over 25 years was greatest with Zelkova serrata(198.3 kg), followed by Prunus yedoensis(121.7 kg), Pinus koraiensis(117.5 kg), and Pinus densiflora (77.4 kg) in that order. The cumulative carbon sequestration by Z. serrata offset about 5% of carbon emissions per capita from household electricity use for the same period. The growth rates and carbon sequestration for landscape trees were much greater than those for forest trees even for the same species. Based on these results, landscape planting and management strategies were explored to improve carbon sequestration, including tree species selection, planting density, and growth ground improvement. This study breaks new ground in discovering changes in growth and carbon sequestration by age of landscape trees and is expected to be useful in establishing urban greenspaces towards a low carbon society.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.83-86
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• 2000

본 논문에서는 비정형 단위를 사용한 음성 합성 시스템의 합성음에 대한 자연성을 향상시키기 위한 휴지 구간 추출 및 휴지 지속시간 예측 모델을 제안한다. 제안된 휴지 지속시간 예측 모델은 트리 기반 모델링 기법 중 하나인 CART (Classification And Regression Trees)방법을 이용하였다. 이를 위해 남성 단일 화자가 발성한 6,220개의 어절경계 포함하는 총 400문장의 문 음성 데이터베이스를 구축하였고, 이 데이터베이스로부터 V-fold Cross-Validation 방법에 의해 최적의 트리를 결정하였다. 이 모델을 평가한 결과, 휴지 구간 추출 정확율은 $81\%$로 휴지 구간 존재 추출 정확율은 $83\%, 휴지 구간 비존재 추출 정확율은 $80\%이었고, 실 휴지지속시간과 예측 휴지지속시간과의 다중상관 계수는 0.84로, 오차 범위 20ms 이내에서 의 정 확율은 $88\%$ 이었다. 또한, 휴지지속시간을 예측하여 적용한 합성음을 청취 실험한 결과 자연 음성과 대체적으로 유사하게 나타났다.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.561-574
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• 2010

We studied the performance of 8 data mining algorithms including decision trees, logistic regression, LDA, QDA, Neral network, and SVM and their combinations of 2 ensemble techniques, bagging and boosting. In this study, we utilized 13 data sets with binary responses. Sensitivity, Specificity and missclassificate error were used as criteria for comparison.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.263-268
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• 2003

Breiman, Friedman, Olshen and Stone(1984)의 전체탐색법에 의한 회귀나무는 상대적으로 많은 분리가 가능한 변수로 분리기준이 정해지는 편의 현상을 갖고 있다. 본 연구에서는 이런 문제점을 해결할 수 있는 알고리즘을 제안하여 변수선택편의가 없는 회귀나무를 만들고자 한다. 제안하는 알고리즘은 노드의 분리변수를 선택하는 단계와 그 선택된 변수에 의해 이진분리를 위한 분리점을 찾는 단계로 구성되어 있다. 예측변수 중에서 목표변수와 가장 밀접하게 연관된 예측변수는 예측변수의 자료의 종류에 따라 스피어만의 순위상관계수에 의한 검정 혹은 크루스칼-왈리스의 통계량에 의한 검정을 수행하여 가장 통계적으로 유의한 변수로 선택하였고, 선택된 변수에만 Breiman et al.(1984)의 전체선택법을 적용하여 분리점을 결정하였다. 모의실험을 통해 변수선택편의, 변수선택력 , 그리고 평균제곱오차 측면에서 Breiman et al. (1984)의 CART(Classification and Regression Trees)와 제안한 알고리즘을 서로 비교하였다. 또한, 두 알고리즘을 실제 자료에 적용하여 효율을 서로 비교하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.849-854
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• 2011

To minimize the spreading effect from the events of the system, a rule-based expert system is very effective. However, because the events of the large-scale system are diverse and the load condition is very variable, it is very difficult to construct the rule-based expert system. To solve this problem, this paper studies a methodology which constructs a rule-based expert system by applying a CART(Classification and Regression Trees) algorithm based decision tree determination method to event case examples.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.30-33
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• 2016

With the advent of big data, data mining is more increasingly utilized in various decision-making fields by extracting hidden and meaningful information from large amounts of data. Even as exponential increase of the request of unrevealing the hidden meaning behind data, it becomes more and more important to decide to select which data mining algorithm and how to use it. There are several mainly used data mining algorithms in biology and clinics highlighted; Logistic regression, Neural networks, Supportvector machine, and variety of statistical techniques. In this paper it is attempted to compare the classification performance of an exemplary algorithm J48 and REPTree of ML algorithms. It is confirmed that more accurate classification algorithm is provided by the performance comparison results. More accurate prediction is possible with the algorithm for the goal of experiment. Based on this, it is expected to be relatively difficult visually detailed classification and distinction.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.983-984
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• 2008

This paper proposes a variable break that can offset prediction error as well as a pre-selection methods, based on the variable break, for enhanced unit selection. In Japanese, a sentence consists of several APs (Accentual phrases) and MPs (Major phrases), and the breaks between these phrases must predicted to realize text-to-speech systems. An MP also consists of several APs and plays a decisive role in making synthetic speech natural and understandable because short pauses appear at its boundary. The variable break is defined as a break that is able to change easily from an AP to an MP boundary, or from an MP to an AP boundary. Using CART (Classification and Regression Trees), the variable break is modeled stochastically, and then we pre-select candidate units in the unit-selection process. As the experimental results show, it was possible to complement a break prediction error and improve the naturalness of synthetic speech.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.4
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• pp.591-595
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• 2002

Three hundred thirty five progeny testing steers of Korean beef cattle were evaluated ultrasonically for back fat thickness (BFT), longissimus muscle area (LMA) and intramuscular fat (IF) before slaughter. Class measurements associated with the Korean yield grade and quality grade were also obtained. Residual standard deviation between ultrasonic estimates and carcass measurements of BFT, LMA were 1.49 mm and $0.96cm^2$. The linear correlation coefficients (p<0.01) between ultrasonic estimates and carcass measurements of BFT, LMA and IF were 0.75, 0.57 and 0.67, respectively. Results for improving predictions of yield grade by four methods-the Korean yield grade index equation, fat depth alone, regression and decision tree methods were 75.4%, 79.6%, 64.3% and 81.4%, respectively. We conclude that the decision tree method can easily predict yield grade and is also useful for increasing prediction accuracy rate.

• Journal of Forest and Environmental Science
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• v.31 no.4
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• pp.267-271
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• 2015

The variations of wood density and mechanical properties of Juniperus polycarpos trees were studied in a natural forest in Iran. Sample disks were taken from each tree to examine wood density and mechanical properties (MOE and MOR) from pith to bark at breast height, 50%, and 75% of total tree height. The analysis of variance (ANOVA) indicated that radial position and height significantly affected all wood properties. The wood density, MOE and MOR were decreased along horizontal position from the pith to the bark and vertical direction from base upwards. Regression analysis showed that modulus of elasticity (MOE) and modulus of rupture (MOR) had a positive correlation with wood density.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.455-464
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• 2011

The purpose of this study is to clarify correlations between soil environments and the growth of trees in forests and thereon analyze effects of seasonal changes in such environments on such growth. To determine seasonal factors of soil affecting the Tree Vitality of Pinus thunbergii, first of all, the study designated the Tree Vitality as a dependent variable and soil hardness, moisture, pH, K, Na, Mg and Ca as independent variables. Then the study performed Pearson's coefficient analysis. To clarify what soil factors influence the seasonal growth of Pinus thunbergii multiple regression analysis is carried out, and findings are as follow; the growth of Pinus thunbergii was basically influenced by pH, followed by soil hardness in spring, K, followed by moisture in summer, and by soil hardness in winter. However, no soil factors affected the vitality at the significance level of 5% for t.