• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.5 s.118
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• pp.70-75
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• 2006

The purpose of this study is to explore the effects of difference in urban tree plantings on microclimate amelioration, and to suggest essential information for quantifying urban energy budgets and energy savings. This study was focused on measuring and analyzing air temperature and relative humidity in summer. Daytime air temperatures at places with 8%, 24%, 44%, 79%, and 100% cover of woody plants were, respectively, $0.6^{\circ}C,\;1.3^{\circ}C,\;2.4^{\circ}C,\;3.5^{\circ}C,\;and\;4.8^{\circ}C$ cooler, compared to a place with 0% cover. A 10% increase of woody plant cover was estimated to reduce the temperature by approximately $0.55^{\circ}C$. The temperature reduction effects were relatively greater between places with lower cover of woody plants than between those with higher cover. Woody plant cover and crown volume were the appropriate indicators which quantified the effects of tree plantings on air temperatures, based on the correlation analysis. Regression equations to estimate temperature change ($Y:^{\circ}C$) using woody plant cover ($X_1:%$) or crown volume ($X_2:m^3$) as independent variables are as follows: $$1nY=3.3233-0.0018X_1\;(r^2=0.99,\;p<0.0001)\;Y=27.5297-0.0019X_2\;(r^2=0.96,\;p=0.0007)$$

• Journal of Ecology and Environment
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• v.32 no.2
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• pp.109-113
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• 2009

Tree-ring width analysis has been used to assess the effects of air pollution on tree growth around industrial complexes. Our study was conducted to elucidate the effect of air pollutants on annual ring growth in black pines (Pinus thunbergii) of age 41$\sim$48 years around Ulsan Metropolitan City. The growth data were analyzed by multiple regression and the results are as follows: 1. The annual ring increment of black pines increased with tree age until age 40 years and then decreased gradually after age 40 years. 2. The increment of annual ring width of black pines was affected more by precipitation and evapotranspiration than air temperature. An annual ring decline appeared in the years 1968$\sim$1983, when annual ring indices below zero were observed. Decreased annual ring growth during this period may have been due to air pollution. 3. The heavy metal with the strongest effect on annual ring growth of black pines in the experimental stand was lead (Pb). The concentration of lead in the stand was estimated as over 6 ppm. 4. The technique of tree-ring width analysis may be useful for estimation of the extent of pollution in forest areas near industrial complexes.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.185-196
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• 2021

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.389-401
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• 2011

Most nations around the world have expressed significant concern in the climate change due to a rapid increase in green-house gases and thus reach an international agreement to control total amount of these gases for the mitigation of global warming. As the most important absorber of carbon dioxide, one of major green-house gases, forest resources should be more tightly managed with a means to measure their total amount, forest biomass, efficiently and accurately. Forest biomass has close relations with forest areas and tree height. Airborne LiDAR data helps extract biophysical properties on forest resources such as tree height more efficiently by providing detailed spatial information about the wide-range ground surface. Many researchers have thus developed various methods to estimate tree height using LiDAR data, which retain different performance and characteristics depending on forest environment and data characteristics. In this study, we attempted to investigate such various techniques to estimate tree height, elaborate their advantages and limitations, and suggest future research directions. We first examined the characteristics of LiDAR data applied to forest studies and then analyzed methods on filtering, a precedent procedure for tree height estimation. Regarding the methods for tree height estimation, we classified them into two categories: individual tree-based and regression-based method and described the representative methods under each category with a summary of their analysis results. Finally, we reviewed techniques regarding data fusion between LiDAR and other remote sensing data for future work.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Journal of the Korean Institute of Landscape Architecture
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• v.28 no.4
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• pp.9-20
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• 2000

The purpose of thus paper is to provide the knowledge on preparing for the planting soil and planting method, and maintenance at the reclaimed seaside. Based on the collected data from the field work, the soil environment, the growth of height, inter-node, tree ring and roots of the two species had been analyzed. The determinant of soil factors, affecting the growth of trees, turned out to be six elements such as soil hardness, soil acidity, potassium, calcium, magnesium and total nitrogen. Because the variances of both growth of tree height and tree ring are greater than that of root, the growth characteristics of ground parts of the species by the individual tree species is more dynamical than those of underground parts. From the mean difference test the growth of height, root between Pinus thunbergii and Zelkova serrata, have been turned out to be statistically significant at 5 percent level. Pinus thunbergii is a sapling, so it grows faster than Zelkova serrata while Pinus thunbergii has better roots system than Zelkova serrata. From the correlation analysis, it showed the very strong correlation between tree height growth and potassim, while the lowest correlation coefficient was between soil hardness and potassim as 0.744. From the multiple regression analysis, both soil hardness and magnesium affect to the tree growth, soil hardness and potassium to the tree growth, potassium and calcium to the rot growth, respectively. Using this research results, we can be use the planting plan including revegetation, construction and maintenance of the reclaimed seaside. In the future, the planting method including the ground preparation and tree species selection for the reclaimed seaside should be accompanied in advanced through the soil survey and relevant analysis.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.99-100
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• 2022

As the danger of exposure to the asbestos has been revealed, the importance of demolition asbestos in existing buildings has been raised. Extensive body of study has been conducted to evaluate the risk of demolition asbestos, but there were confined types of variables caused by not reflecting categorical information and limitations in collecting quantitative information. Thus, this study aims to derive a model that predicts the risk in workplace of demolition asbestos by collecting categorical and continuous variables. For this purpose, categorical and continuous variables were collected from asbestos demolition reports, and the risk assessment score was set as the dependent variable. In this study, the influence of each variable was identified using logistic regression, and the risk prediction model methodologies were compared through decision tree regression and artificial neural network. As a result, a conditional risk prediction model was derived to evaluate the risk of demolition asbestos, and this model is expected to be used to ensure the safety of asbestos demolition workers.