• Genomics & Informatics
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• v.17 no.4
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.285-289
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• 1999

A processing system to produce cloud-free composite image data set was developed. In the process, a fine geometric correction based on orbit parameters and ground control points and radiometric correction based on 6S code are applied. Presently, by using AVHRR image data received at Tokyo, Okinawa, Ulaanbaatar and Bangkok, data set of 10 days composite images covering almost whole Asian region.

• Restorative Dentistry and Endodontics
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• v.41 no.1
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• pp.37-43
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• 2016

Objectives: The purpose of this study was to compare the marginal and internal fit of nano-composite CAD-CAM restorations. Materials and Methods: A full veneer crown and an mesio-occluso-distal (MOD) inlay cavity, which were prepared on extracted human molars, were used as templates of epoxy resin replicas. The prepared teeth were scanned and CAD-CAM restorations were milled using Lava Ultimate (LU) and experimental nano-composite CAD/CAM blocks (EB) under the same milling parameters. To assess the marginal and internal fit, the restorations were cemented to replicas and were embedded in an acrylic mold for sectioning at 0.5 mm intervals. The measured gap data were pooled according to the block types and measuring points for statistical analysis. Results: Both the block type and measuring point significantly affected gap values, and their interaction was significant (p = 0.000). In crowns and inlays made from the two blocks, gap values were significantly larger in the occlusal area than in the axial area, while gap values in the marginal area were smallest (p < 0.001). Among the blocks, the restorations milled from EB had a significantly larger gap at all measuring points than those milled from LU (p = 0.000). Conclusions: The marginal and internal gaps of the two nano-composite CAD/CAM blocks differed according to the measuring points. Among the internal area of the two nano-composite CAD/CAM restorations, occlusal gap data were significantly larger than axial gap data. The EB crowns and inlays had significantly larger gaps than LU restorations.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.133-152
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• 1998

The central composite design is widely used in the response surface analysis, because it can fit the second order model with small experimental points. In practice, the experimental data are not always obtained on all the points. When there are missing observations, many problems due to the missing cells can occur. In this paper, the influence of a missing cell on the central composite design is discussed. First, the influences of a missing cell on the variances of estimated regression coefficents are compared as $\alpha$ varies. Second, how the average predition variance is affected by a missing sell is discussed. And the influence on rotatability is investigated. Third, the influence of a missing cell on optimality, especially on D-optimality and A-optimality, is examined.

• Composites Research
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• v.32 no.5
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• pp.216-221
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• 2019

Piecewise integrated composite (PIC) beam has different stacking sequences for several regions with respect to their superior load-resisting capabilities. On the interest of current research is to improve bending characteristics of PIC beam, with assigning specific stacking sequence to a specific region with the help of machine learning techniques. 240 elements of from the FE model were chosen to be reference points. Preliminary FE analysis revealed triaxialities at those regularly distributed reference points to obtain learning data creation of machine learning. Triaxiality values catagorise the type of loading i.e. tension, compression or shear. Machine learning model was formulated by learning data as well as hyperparameters and proper load fidelity was suggested by tuned values of hyperparameters, however, comparatively higher nonlinearity intensive region, such as side face of the beam showed poor load fidelity. Therefore, irregular distribution of reference points, i.e., dense reference points were distributed in the severe changes of loading, on the contrary, coarse distribution for rare changes of loading, was prepared for machine learning model. FE model with irregularly distributed reference points showed better load fidelity compared to the results from the model with regular distribution of reference points.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.577-589
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• 2021

Drone mapping systems can be applied to many fields such as disaster damage investigation, environmental monitoring, and construction process monitoring. To integrate individual sensors attached to a drone, it was essential to undergo complicated procedures including time synchronization. Recently, a variety of composite sensors are released which consist of visual sensors and GPS/INS. Composite sensors integrate multi-sensory data internally, and they provide geotagged image files to users. Therefore, to use composite sensors in drone mapping systems, mapping accuracies from composite sensors should be examined. In this study, we analyzed the mapping accuracies of a composite sensor, focusing on the data acquisition area and pre-calibration effect. In the first experiment, we analyzed how mapping accuracy varies with the number of ground control points. When 2 GCPs were used for mapping, the total RMSE has been reduced by 40 cm from more than 1 m to about 60 cm. In the second experiment, we assessed mapping accuracies based on whether pre-calibration is conducted or not. Using a few ground control points showed the pre-calibration does not affect mapping accuracies. The formation of weak geometry of the image sequences has resulted that pre-calibration can be essential to decrease possible mapping errors. In the absence of ground control points, pre-calibration also can improve mapping errors. Based on this study, we expect future drone mapping systems using composite sensors will contribute to streamlining a survey and calibration process depending on the data acquisition circumstances.

• Genomics & Informatics
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• v.17 no.1
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• pp.4.1-4.7
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• 2019

In this paper, we propose a window-based mechanism visualization approach as an alternative way to measure the seriousness of the difference among data-insights extracted from a composite biodata point. The approach is based on two components: undirected graph and Mosaab-metric space. The significant application of this approach is to visualize the segmented genome of a virus. We use Influenza and Ebola viruses as examples to demonstrate the robustness of this approach and to conduct comparisons. This approach can provide researchers with deep insights about information structures extracted from a segmented genome as a composite biodata point, and consequently, to capture the segmented genetic variations and diversity (variants) in composite data points.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.9
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• pp.2154-2164
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• 1995

In this study, a dielectric cure monitoring system which consists of an electric circuit, a sensor and a personal computer was developed to on-line monitor the dielectric properties of carbon fiber epoxy composite materials. Also, the kinetic model of carbon fiber epoxy composite materials was developed by curve fitting of differential scanning calorimetry data. The start and end points of cure and the relationship between the dissipation factor and the degree of cure were obtained by comparing the dissipation factor from the dielectric properties with the degree of cure from the DSC data. The relationship between the dissipation factor and the degree of cure was tested under various temperature profiles.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.298-302
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• 1994

A three dimensional FE mesh generation scheme based on mapping approach is proposed in this study. A volume in Euclcdian space is represented by composite hyperpatches which are piecewise cubic functions with parameters u,v,w. A key idea in the proposed approach is that sampled grid data points only on the boundary surfaces are needed for the shape representation. Inner points which are necessary of form a hyperpatch are internally generated by Coons patches. This approach is most appropriate for the shapes which are compositions of hexahedron-like shapes and also severely curved.