• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.173-179
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• 1999

In reverse engineering, when a shape containing multi-patched surfaces is digitized, the boundaries of these surfaces should be detected. The objective of this paper is to introduce a computationally efficient segmentation technique for extracting edges, ad partitioning the 3D measuring point data based on the location of the boundaries. The procedure begins with the identification of the edge points. An automatic edge-based approach is developed on the basis of local geometry. A parametric quadric surface approximation method is used to estimate the local surface curvature properties. the least-square approximation scheme minimizes the sum of the squares of the actual euclidean distance between the neighborhood data points and the parametric quadric surface. The surface curvatures and the principal directions are computed from the locally approximated surfaces. Edge points are identified as the curvature extremes, and zero-crossing, which are found from the estimated surface curvatures. After edge points are identified, edge-neighborhood chain-coding algorithm is used for forming boundary curves. The original point set is then broke down into subsets, which meet along the boundaries, by scan line algorithm. All point data are applied to each boundary loops to partition the points to different regions. Experimental results are presented to verify the developed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.81-90
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• 2004

Recently, It has been increased to use a multi-dimensional data in various applications with a rapid growth of the computing environment. In this paper, we propose the vector approximate tree for content-based retrieval of multi-dimensional data. The proposed index structure reduces the depth of tree by storing the many region information in a node because of representing region information using space partition based method and vector approximation method. Also it efficiently handles 'dimensionality curse' that causes a problem of multi-dimensional index structure by assigning the multi-dimensional data space to dynamic bit. And it provides the more correct regions by representing the child region information as the parent region information relatively. We show that our index structure outperforms the existing index structure by various experimental evaluations.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.17-25
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• 2006

Nowadays most colleges are confronting with a serious problem because many students have left their majors at the colleges. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, the objective of this paper Is to find a predicting model of students leaving their majors. The sample for this study was chosen from a university in Kangwon-Do during seven years(2000.3.1 $\sim$ 2006. 6.30). In this study, the ratio of training sample versus testing sample among partition data was controlled as 50% : 50% for a validation test of data division. Also, this study provides values about accuracy, sensitivity, specificity about three kinds of algorithms including CHAID, CART and C4.5. In addition, ROC chart and gains chart were used for classification of students leaving their majors. The analysis results were very informative since those enable us to know the most important factors such as semester taking a course, grade on cultural subjects, scholarship, grade on majors, and total completion of courses which can affect students leaving their majors.

• Natural Product Sciences
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• v.15 no.3
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• pp.144-150
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• 2009

Gradient centrifugal partition chromatography (GCPC) method was developed and applied to isolate 3,5-dimethoxyphenanthrene-2,7-diol (DMP) and batatasin-I (BA-I) from the dichloromethane soluble extract of Dioscorea opposita. In this method, the lower phase of n-hexane-methanol-water system (HMW, 10 : 9 : 1, v/v) was used as a mobile phase A (MpA) and water was used as a mobile phase B (MpB). This gradient CPC method is comparable to that of reversed-phase HPLC method in that the stationary upper-phase of HMW (10 : 9 : 1 v/v) works as if it were reversed-phase silica gel due to its hydrophobic property, while the lower phase (MpA) and water (MpB) functioned as hydrophilic mobile phases. The initial condition of the mobile phase was 20% MpA/80% MpB and maintained for 150 min to obtain DMP (1.2 mg), and then MpA was increased up to 50% to elute BA-I (1.7 mg). The purities of DMP and BA-I were 94.1% and 98.3% with the recovery yields of 83% and 86%, respectively. Similar results were obtained by linear-gradient CPC. The CPC peak fractions were identified by comparing their retention time to those of authentic samples of DMP and BA-I and their spectroscopic data ($^1$H NMR and $^{13}$C NMR) to those of literature values.

• The Korean Journal of Food And Nutrition
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• v.27 no.3
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• pp.532-537
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• 2014

Cortex Phellodendri (CP) is derived from the dried bark of Phellodendron amurense. It has been widely used as a drug in traditional Korea medicine for treating diarrhea, jaundice, swelling pains in the knees and feet, urinary tract infections, and infections of the body surface. Many analytical methods have been used to study oriental herbal medicines, such as thin-layer chromatography, column liquid chromatography, and high performance liquid chromatography (HPLC). In this study, preparative centrifugal partition chromatography (CPC) was successfully carried out in order to separate pure compounds from a CP methanol extract. The optimum two-phase CPC solvent system was composed of n-butanol: acetic acid: water (4:1:5 v/v/v). The flow rate of the mobile phase was 3 mL/min in ascending mode with rotation at 1,000 rpm. The CPC-separated fraction and purification procedures were carried out by preparatory HPLC. The $^1H$ NMR spectrum revealed that the resonances at ${\delta}$ 4.10 and 4.20 ppm corresponded to three protons ($-OCH_3$), whereas those at ${\delta}$ 6.10 ppm corresponded to two protons ($-OCH_2O-$). Further, two aromatic protons (H-11 and H-12) conveys a doublet-doublet pattern. The H-11 doublet and H-12 doublet appear at ${\delta}$ 7.98 and 8.11, respectively. The $^{13}C$ NMR. spectrum showed a tetrasubstituted with a methylenedioxy group at C2 and C3, and two methoxy groups at C9 and C10. The chemical structure of the berberine was identified by $^1H$, $^{13}C$-nuclear magnetic resonance and electrospray ionization-mass spectroscopy spectral data analysis.

• Journal of Environmental Health Sciences
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• v.48 no.2
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• pp.75-85
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• 2022

Background: Various types of semi-volatile organic compounds (SVOCs) exist in the public's living environment. They occur in different forms in terms of their physical and chemical properties and partition coefficients. As a consequence, indoor exposure to SVOCs occurs via various routes, including inhalation of air and airborne particles, skin contact, and dust intake. Objectives: To propose a method for assessing human exposure to the SVOCs occurring in the air of an indoor environment, the concentrations of SVOCs in house dust and organic films measured in a real residential environment were estimated in terms of gas-phase concentration using the partition coefficient. Assessment of inhalation exposure to SVOCs was performed using this method. Methods: Phthalates were collected from samples of house dust and organic films from 110 households in a real residential environment. To perform an exposures assessment of the phthalates present in organic films, gas-phase concentration was calculated using the partition coefficient. The airborne gas-phase concentrations of phthalates from the house dust and organic films were estimated and exposure assessment was performed based on the assumption of inhalation exposure from air. Results: As a result of the exposure assessment for gas-phase phthalates from house dust and organic films, preschool children showed the highest level of inhalation of phthalates, followed by school children, adults, and adolescents. Conclusions: This study includes the limitation of not considering different SVOCs exposure pathways in the health impact assessment, including those of phthalates in the indoor living environment. However, this study has the significance of performing exposure assessment based on exposure to SVOCs present in indoor air that originated from organic films in the indoor residential environment. Therefore, the results of this study should be useful as basic data for exposure and health risk assessments of SVOCs associated with organic films in the indoor environment.

• Journal of Environmental Health Sciences
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• v.46 no.1
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• pp.1-10
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• 2020

Objectives: This study intends to evaluate the ecological risk of lead (Pb), arsenic (As), and their compounds according to the 2010 action plan on inventory and management for national priority chemicals and provide calculations of risks to the environment. By doing so, we aim to inform risk management measures for the target chemicals. Methods: We conducted species sensitivity distribution (SSD) analysis using the collected ecotoxicity data and obtained predicted no effect concentrations (PNECs) for the in-water environment using a hazardous concentration of 5% (HC5) protective of most species (95%) in the environment. Based on the calculated PNECs for aquatic organisms, PNEC values for soil and sediment were calculated using the partition coefficient. We also calculated predicted exposure concentration (PEC) from nation-wide environmental monitoring data and then the hazard quotient (HQ) was calculated using PNEC for environmental media. Results: Ecological toxicity data was categorized into five groups and five species for Pb and four groups and four species for As. Based on the HC5 values from SSD analysis, the PNEC value for aquatic organisms was calculated as 0.40 ㎍/L for Pb and 0.13 ㎍/L for As. PNEC values for soil and sediment calculated using a partition coefficient were 77.36 and 350.50 mg/kg for Pb and 24.20 and 112.75 mg/kg for As. The analysis of national environmental monitoring data showed that PEC values in water were 0.284 ㎍/L for Pb and 0.024 ㎍/L for As, while those in soil and sediment were respectively 45.9 and 44 mg/kg for Pb, and 11.40 and 19.80 mg/kg for As. Conclusions: HQs of Pb and As were 0.70 and 0.18 in water, while those in soil and sediment were 0.59 and 0.13 for Pb and 0.47 and 0.18 for As. With HQs <1 of lead and arsenic in the environment, their ecological risk levels are found to be low.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.367-380
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• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.