• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• Analytical Science and Technology
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• v.21 no.5
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• pp.392-396
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• 2008

Acer tegmentosum is a tree used to treat various liver diseases in Korea. There have been some concern regarding the safety of Acer tegmentosum due to some toxic chemical compounds in its stems. Supercritical fluid extraction (SFE) was employed to develop a removing method of toxic compounds from Acer tegmentosum. The toxic compounds were effectively extracted with ethanol modified supercritical fluid $CO_2$. The optimum condition of SFE was 100 bar of pressure, $40^{\circ}C$ of extraction temperature, 3 mL/min of $CO_2$ flow rate, 0.2 mL/min of modifier (ethanol) flow rate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.29-35
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• 2013

Generally, Calculation of carbon dioxide absorption in the forest area is calculated using the information of the forest, such as tree height and DBH(Diameter of Breast Height). Tree height and DBH of these are obtained using the remote sensing data such as imagery and information of local forest survey. However, Mixed forest with a high proportion of field survey to lower the accuracy of forest information. In this study, vertical structure of the tree were identified by applying region growing method based on the slope using LiDAR data and height and number of the tree were identified by applying extracting top of the tree algorithm. Through the vertex tree extraction algorithm to identify height of tree and the number of individuals, substitute this for the DBH relation formula which is drawn from data through field surveys. In this, a quantitative calculation of carbon dioxide absorption were able to calculate the basic data. Also, carbon dioxide absorption of three type trees were calculated and average per unit area of carbon dioxide absorption were able to estimate.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.3.1-3.7
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• 2014

Objectives This study was conducted to determine the depositional characteristics of several tree barks, including Ginkgo (Ginkgo biloba), Pine (Pinus densiflora), Platanus (Platanus), and Metasequoia (Metasequoia glyptostroboides). These were used as passive air sampler (PAS) of atmospheric polybrominated diphenyl ethers (PBDEs). Methods Tree barks were sampled from the same site. PBDEs were analyzed by high-resolution gas chromatography/high-resolution mass spectrometer, and the lipid content was measured using the gravimetric method by n-hexane extraction. Results Gingko contained the highest lipid content (7.82 mg/g dry), whereas pine (4.85 mg/g dry), Platanus (3.61 mg/g dry), and Metasequoia (0.97 mg/g dry) had relatively lower content. The highest total PBDEs concentration was observed in Metasequoia (83,159.0 pg/g dry), followed by Ginkgo (53,538.4 pg/g dry), Pine (20,266.4 pg/g dry), and Platanus (12,572.0 pg/g dry). There were poor correlations between lipid content and total PBDE concentrations in tree barks ($R^2$=0.1011, p =0.682). Among the PBDE congeners, BDE 206, 207 and 209 were highly brominated PBDEs that are sorbed to particulates in ambient air, which accounted for 90.5% (84.3-95.6%) of the concentration and were therefore identified as the main PBDE congener. The concentrations of particulate PBDEs deposited on tree barks were dependent on morphological characteristics such as surface area or roughness of barks. Conclusions Therefore, when using the tree barks as the PAS of the atmospheric PBDEs, samples belonging to same tree species should be collected to reduce errors and to obtain reliable data.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.452-458
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• 2013

Over the past several years, many studies have been carried out in the field of 3D data inspection systems. Several attempts have been made to improve the quality of manufactured parts. The introduction of laser sensors for inspection has made it possible to acquire data at a remarkably high speed. In this paper, a robust inspection technique for detecting defects in 3D pressed parts using laser-scanned data is proposed. Point cloud data are segmented for the extraction of features. These segmented features are used for shape matching during the localization process. An iterative closest point (ICP) algorithm is used for the localization of the scanned model and CAD model. To achieve a higher accuracy rate, the ICP algorithm is modified and then used for matching. To enhance the speed of the matching process, aKd-tree algorithm is used. Then, the deviation of the scanned points from the CAD model is computed.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.1
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• pp.39-44
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• 2013

Trees are important ground objects that cause oxygen and reduce carbon dioxide in urban areas. For management of the trees, many studies using LIDAR data have been conducted. But, they rely on overseas developed LIDAR data processing software applications because there is a lack of domestically developed software applications. Therefore, this work was intended to propose an automation process that helps to extract trees automatically from LIDAR data. The proposed process has the function to classify LIDAR data and to extract building regions and trees automatically. It was applied to a study place in Yongin to conduct a test. As a result, about 88% of trees were extracted from the automation process.

• Korean Journal of Medicinal Crop Science
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• v.21 no.3
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• pp.204-212
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• 2013

In general, stepwise hot steaming process is known to be effective in improving its biological activities; however, not much employed in processing Codonopsis lanceolata due to its hardness. The complex processed C. lanceolata showed highest free radical scavenging acitivity as 45.21%. Total phenol and flavonoid content were of complex processed C. lanceolata higher than conventional extract and alone steaming process. It was showed the lower melanogenesis rate on melanin production test by B16F10 cells as 27.46%. High inhibitory of tyrosinase was also measured as 28.61% by adding steamed Codonopsis lanceolata extracts by high pressure extraction of 1.0 $mg/m{\ell}$. And anti-wrinkle activity were 39.08%. In comparing phenolic acids profiles in the extract, in general higher amounts of polyphenol were obtained possibly by easy release of active components during thermal processing, which results in better antioxidant activities than that of general extract. This findings can also be supported by result that the extract by steaming process showed better activities than the general extraction extract.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.709-716
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• 2000

We present an efficient algorithm using region-based average of variations of local correlation coefficients (LCC) for the extraction of texture regions. The key idea of this algorithm for the classification of texture and shade regions is to utilize the fact that the averages of the variations of LCCs according to different orientations texture regions are clearly larger than those in shade regions. In order to evaluate the performance of the proposed algorithm, we use nine test images (Lena, Bsail, Camera Man, Face, Woman, Elaine, Jet, Tree, and Tank) of 8-bit 256$\times$256 pixels. Experimental results show that the proposed feature extracts well the regions which appear visually as texture regions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.