• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.280-285
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• 1994

This study was conducted to investigate the effects of acid deposition on forest soil, major five Korean forest soils(Brown, Dark red, Gray brown, Red and Yellow, and Volcanic ash forest soils) The samples were subjected to receive 1200mm($100mm{\times}12$ times) of artificial acid rain adjusted to pH5.6, 4.0, 3.0 and 2.0. The results obtained of major importance are summarized as follow ; 1. Ca appeared mostly affected at pH treatment of 2.0, while less affected by other pH treatments. Leaching of Ca rapidly increased with increasing of artificial acid rain acidity and application times in Dark red forest soil. 2. In the cases of Mg, K and Na, they showed gradual increase with the addition of artificial acid rain. Mg and Na losses showed similar leaching patterns, but they didn't show difference among the five forest soils. 3. Exchangeable canon concentrations in the soil leachates, which looked slightly different among the five forest soils, were the highest in pH2.0 treatment. Hydrogen ion comsumption capability by exchangeable canon was the highest in Dark red forest soil followed by Volcanic ash, Red and Yellow, Gray brown and Brown forest soils when artificial acid rain were treated.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.138-146
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• 2003

This paper deals with the experimental results that apply to a new wire by an artificial flame-maker because it's difficult to directly analyze the characteristic of deterioration by a forest fire. Those results include tension load, extension rate and torsion number for a conductor. In addition, there's been an experiment and analysis about the mechanical characteristics of the wire of ACSR 480$\textrm{mm}^2$ which was removed from Pohang area by a forest fire. Then, the database will be made to predict the state of deteriorated wires by a forest fire using those two data, and data necessary to diagnose the life state of an ACSR wire affected by a forest fire will be given.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.173-180
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• 2003

This paper deals with the experimental results that apply to a new wire by an artificial flame-maker because it's difficult to directly analyze the characteristic of deterioration by a forest fire. Those results include surface characteristics for a conductor. In addition, there's been an experiment and analysis about the surface characteristics of the wire of ACSR 480$\textrm{mm}^2$ which was removed from Pohang area by a forest fire. Then, the database will be made to predict the state of deteriorated wires by a forest fire using those two data, and data necessary to diagnose the life state of an ACSR wire affected by a forest fire will be given.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.27-32
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• 2020

In this paper, we propose an algorithm to measure the accumulated status of scrap boxes where metal scraps are accumulated. The accumulated status measuring is defined as a multi-class classification problem, and the method with deep learning classify the accumulated status using only the scrap box image. The learning was conducted by the Transfer Learning method, and the deep learning model was NASNet-A. In order to improve the accuracy of the model, we combined the Random Forest classifier with the trained NASNet-A and improved the model through post-processing. Testing with 4,195 data collected in the field showed 55% accuracy when only NASNet-A was applied, and the proposed method, NASNet with Random Forest, improved the accuracy by 88%.

• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• Journal of Environmental Science International
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• v.19 no.7
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• pp.843-848
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• 2010

This study focused on the characteristics of change soil water with respect to soil thickness and soil mixture ratio, in order to effectively carry out an afforestation system for a roof with a low level of management and a light weight. Soil hardness tended to increase as sand particle was increase regardless soil thickness and soil porosity had more higher artificial soil than natural soil mixture. In case of soil pH, natural soil mixture had between 6.7 and 7.4, and artificial soil mixture had 6.0~6.8. Organic matter, electrical conductance and exchangeable content were highest in $L_{10}$, which it had the highest leafmold ratio. Soil moisture tension(kPa) in 15cm soil thickness was observed natural soil mixture had a considerable change but artificial soil mixture had a gradual change when non-rainfall kept on. In the experimental $L_{10}$, $S_{10}$, $S_7L_3$ and $S_5L_5$ object, the amount of moisture tended to rapidly decrease. However, in the experimental $P_7P_1L_2$, $P_6P_2L_2$, $P_5P_3L_2$ and $P_4P_4L_2$ objects, which contained pearlite and peat moss, the amount of moisture tended to gradually decrease. As a result, the use of a artificial soil mixture soil seems to be required for the afforestation of a roof for a low level of management.

• The Korean Journal of Ecology
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• v.21 no.6
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• pp.791-797
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• 1998

An attempt to clarify the landscape structure of urban areas was carried out on Mt. Hwangryung located in the center of Pusan, southern Korea. By means of aerial photographs and field survey, a vegetation map including land-use pattern was made. Landscape structure was described by analyzing the vegetation map. Landscape element types were classified into secondary forest, introduced plantation, and other elements including urbanized area. almus firma and Pinus thunbergii communities, introduced plantation elements, formed matrix and some secondary forest elements and the other artificial plantations of small scale tended to distribute as small patches in such matrix. The number of patches per unit area in secondary forest elements was more than that in introduced plantation element. The result on patech size was vice versa. As the results of landscape ecological analyses, it was estimated that differentiation of patches recognized in community level would be related to artificial interference and those in sub-communities levels to natural process such as progression of succession. On the other hand, restoration plans in viewpoints of restoration and landscape ecology were suggested to improve ecological quality of Mt. hwangryung.