• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.91-100
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• 2003

In this paper, to improve the retrieval Performance, an efficient combination of the MPEG-7 visual descriptors, such as the edge histogram descriptor (EHD), the color layout descriptor (CLD), and the homogeneous texture descriptor (HTD), is proposed in the framework of the relevance feedback approach. The EHD represents spatial distribution of edges in local image regions and it is considered as an important feature to represent the content of the image. The CLD specifies spatial distribution of colors and is widely used in image retrieval due to its simplicity and fast operation speed. The HTD describes precise statistical distribution of the image texture. Both the feature vector for the query image and the weighting factors among the combined descriptors are adaptively determined during the relevance feedback. Experimental results show that the proposed method improves the retrieval performance significantly tot natural images.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Journal of the Korean Chemical Society
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• v.55 no.5
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• pp.733-740
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• 2011

Multiple regression analysis was used for the calculation of pKa values of 15 substituted benzaldoximes by using various types of descriptors as parameters. These descriptors are based on quantum mechanical treatments. They were derived by employing semi-empirical calculation represented by the PM3 model and an Abinitio method expressed by Hartree-Fock(HF) model performed at the 6-311 G(d, p) level of theory. The parameters tested for their ability to represent the variations observed in the experimental pKa(s) are atomic and structural properties including Muliken charges on the atoms of hydroxyl group and C=N bond, the angle $C_6-C_1-C_7$, and length of O-H bond. Molecular properties are also used like energies of HOMO and LUMO, hardness(${\eta}$), chemical potential(${\mu}$), total energy(TE), dipole of molecule(DM), and electrophilicity index(W). The relation between pKa values and each of these parameters of the studied compounds is investigated. Depending on these relations, two sets of parameters were constructed for comparison between the PM3 and HF methods. The results obtained favor the Abinitio method for such applications although both models proved to have high predictive power and have sufficient reliability to describe the effect of substituents on pKa values of benzaldoxime compounds under consideration which is clear from the values of correlation coefficient $R^2$ obtained and the consistency between the experimental and the calculated values.

• School Mathematics
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• v.16 no.3
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• pp.519-542
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• 2014

This study analyzed characteristics which emerged while 45 secondary school teachers observed a video clip about a statistics instruction. The aim of this study based on the analysis was to deduct implications in terms of the various means which would enhance teachers' knowledge in teaching statistics and assist in designing statistics education programs for teachers and professional development initiatives. To achieve this goal, this research firstly developed framework descriptors which provided this study with theoretical foundations to investigate what characteristics appeared in the teachers' observation. Secondly, this study probed the observation results from the teachers in the light of the framework. Therefore, some issues in the teacher education program for teaching statistics were thirdly identified in the categories of 'focus of instruction', 'role of the teacher and discourse' and 'data and technology' based on the analysis. This research inspires the elaboration of exactly what features effective statistics classes have through the framework descriptors and additionally the elucidation of essential matters relevant to statistics education on the basis of the issues.

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1643-1650
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• 2013

A non linear QSAR model was constructed on a series of 57 xanthone and curcuminoide derivatives as ${\alpha}$-glucosidase inhibitors by back-propagation neural network method. The neural network architecture was optimized to obtain a three-layer neural network, composed of five descriptors, nine hidden neurons and one output neuron. A good predictive determination coefficient was obtained (${R^2}_{Pset}$ = 86.7%), the statistical results being better than those obtained with the same data set using a multiple regression analysis (MLR). As in the MLR model, the descriptor MATS7v weighted by Van der Waals volume was found as the most important independent variable on the ${\alpha}$-glucosidase inhibitory.

• Food Science and Biotechnology
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• v.16 no.3
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• pp.361-366
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• 2007

As the application of quantitative risk assessment (QRA) to food safety becomes widespread, it is now being questioned whether experimental results and simulated results coincide. Therefore, this paper comparatively analyzed experimental data and simulated data of the cross contamination, which needs summation of the simplest calculations in QRA, of chicken by Monte Carlo simulation and fuzzy math computation. In order to verify summation, the following basic operation was performed. For the experiment, thigh, breast, and a mixture of both parts were preserved for 24 hr at $20^{\circ}C$, and then the cell number of Salmonella spp. was measured. In order to examine the differences between experimental results and simulated results, we applied the descriptive statistics. The result was that mean value by fuzzy math computation was more similar to the experimental than that by Monte Carlo simulation, whereas other statistical descriptors by Monte Carlo simulation were more similar.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.81-88
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• 2015

c-Jun N-terminal kinase-3 (JNK3) is a member of the mitogen-activated protein kinase family (MAPK), and plays an important role in neurological disorders. Therefore, identification of selective JNK3 inhibitor may contribute towards neuroprotection therapies. In this work, we performed hologram quantitative structure-activity relationship (HQSAR) on a series of thiophene trisubstituted derivatives. The best predictions were obtained for HQSAR model with $q^2=0.628$ and $r^2=0.986$. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. HQSAR result showed that atom, bond and chirality descriptors play an important role in JNK3 activity and also shows that electronegative groups is highly favourble to enhance the biological activity. Our results could be useful to design novel and selective JNK3 inhibitors.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.