• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.880-884
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• 2002

According to the development of computer technology, especially in 3D graphics and visualization, the interest for 3D GIS has been increasing. Several commercial GIS softwares are ready to provide 3D function in their traditional 2D GIS. However, most of these systems are focused on visualization of 3D objects and supports few analysis functions. Therefore in this paper, we design not only a spatial operation processor which can support spatial analysis functions as well as 3D visualization, but also implement a prototype to operate them. In order to support interoperability between the existing models, the proposed spatial operation processor supports the 3D spatial operations based on 3D geometry object model which is designed to extend 2D geometry model of OGIS consortium, and supports index based on R$^*$-Tree. The proposed spatial operation processor can be applied in 3D GIS to support 3D analysis functions.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.101-110
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• 2010

In this study, a prediction model for incident reaction time was developed so that we can cope with the increasing demand for information related to the accident reaction time. For this, the time for dealing with accidents and dependent variables were classified into incident grade, A, B, and C. Then, fifteen independent variables including traffic volume, number of accident-related vehicles and the accidents time zone were utilized. As a result, traffic volume, possibility of including heavy vehicles, and an accident time zone were found as important variables. The results showed that the model has some degree of explanatory power. In addition, when the CHAID Technique was applied, the Answer Tree was constructed based on the variables included in the prediction model for incident reaction time. Using the developed Answer Tree model, accidents firstly were classified into grades A, B, and C. In the secondary classification, they were grouped according to the traffic volume. This study is expected to make a contribution to provide expressway users with quicker and more effective traffic information through the prediction model for incident reaction time and the Answer Tree, when incidents happen on expressway

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• Journal of Korean Society of Forest Science
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• v.85 no.1
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• pp.56-65
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• 1996

Current volume tables might underestimate or overestimate the volumes of individual trees in a specific region because the tables were made using the data from broad region. This study provides a statistical method of local correction, which is the ratio-of-means estimator, when the table is applied to the data from a specific region. Data used in this study were 411 trees of Larix leptolepis from Hongchon region. Five statistical models for individual tree volume equation were evaluated based on 3 evalation criteria and the best equation fitted to the data from Hongchon region was selected. The volume estimated by the selected equation was then compared with the volume estimated by the current volume table. From the ratio-of-means estimate based on the volumes estimated by selected equation and by current volume table, the local correction was made. The correction equation was $V_{Hongchon}=1.078$ $V_{volume\;table}$. It is also proved that the correction equation can simply and precisely estimate tree volumes of Larix leptolepis in Hongchon region using the current volume table.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.531-540
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• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.87-97
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• 2011

In this paper, we propose a new close combat expert system to overcome the difference of combat damage assessments between combat units belong to their own model in Combined Arms Integrated Interoperability System(CAIIS) which will be deployed in the early future. When it happens to engage in a battle among combat units belong to their own model in CAIIS, the result of damage assessment is different severely. This is related to CAIIS's confidence and need to be overcome. We propose the expert system for close combat damage assessment with a decision tree. Simulation results show that the proposed expert system is valid well. Because the proposed expert system is made not as an independent system but as an inner module type of CAIIS, CAIIS will be simpler system than we expect. And we will hope to reduce the cost of CAIIS.

• Journal of agriculture & life science
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• v.52 no.6
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• pp.37-47
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• 2018

The objective of this study was to analyze the influences of forest management activity on the diameter distribution of Larix kaempferi Carriere stands in South Korea. We used 232 managed stands data, 47 unmanaged stands data of National Forest Inventory for this study. We employed the Weibull distribution function for estimating diameter based on percentiles and parameter recovery method. The results revealed that the average diameter breast height movements and growth of tree in the managed stands higher than the unmanaged stands according to the scenario: age, site index, and tree density change. The finding shows the percentage of the total amount of large class diameter was also high in the managed stands. The results of this study could be apply for the estimation of multi-products of timbers per diameter classes and stand structure development for Larix kaempferi Carriere stands in South Korea.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.