• Archives of Pharmacal Research
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• v.27 no.12
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• pp.1270-1274
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• 2004

Random amplified polymorphic DNA (RAPD) analysis was used to determine the genetic relationships among seventeen species of the Acanthopanax species. The DNA isolated from the leaves of the samples was used as template in polymerase chain reaction (PCR) with twenty random decamer primers in order to distinguish plant subspecies at the level of their genomes. The RAPD patterns were compared by calculating pairwise distances using Dice similarity index, and produced to the genetic similarity dendrogram by unweighted pair-group method arithmetic averaged (UPGMA) analysis, showing three groups; a major cluster(twelve species), minor cluster (4 species) and single-clustering species. The results of RAPD were compatible with the morphological classification, as well as the chemotaxonomic classification of the Acanthopanax species. The Acanthopanax species containing 3,4-seco-lupane type triterpene compounds in their leaves corresponded to the major cluster, another species having oleanane or normal lupane type constituents to minor clusters, and one species not containing triterpenoidal compound to single-cluster.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.82-96
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• 2020

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has become the goal of many researches over the past decades. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method considering the imbalance of users' origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random, concentration and directed cases. The result shows that the proposed method reduce the drop of service ratio due to an increase in demand density and save computation time compared to other algorithms. In addition, compared to other clustering-based algorithms, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.157-165
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• 2011

It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.867-889
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• 2014

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.

• International Journal of Industrial Entomology and Biomaterials
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• v.29 no.2
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• pp.169-178
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• 2014

In this study, we investigated genetic diversity of 22 microsporidian isolates infecting tropical tasar silkworm, Antheraea mylitta collected from various geographical forest locations in the state of Jharkhand, India, using polymerase chain reaction (PCR)-based marker assay: random amplified polymorphic DNA (RAPD). A type species, NIK-1s_mys was used as control for comparison. The shape of mature microsporidians was found to be oval to elongate, measuring 3.80 to $5.10{\mu}m$ in length and 2.56 to $3.30{\mu}m$ in width. Of the 20 RAPD primers screened, 16 primers generated reproducible profiles with 298 polymorphic fragments displaying high degree of polymorphism (97%). A total of 14 RAPD primers produced 45 unique putative genetic markers, which were used to differentiate the microsporidians. Calculation of genetic distance coefficients based on dice coefficient method and clustering with un-weighted pair group method using arithmetic average (UPGMA) analysis was conducted to unravel the genetic diversity of microsporidians infecting tasar silkworm. The similarity coefficients varied from 0.059 to 0.980. UPGMA analysis generated a dendrogram with four microsporidian groups, which appear to be different from each other as well as from NIK-1s_mys. Two-dimensional distribution based on Euclidean distance matrix also revealed considerable variability among different microsporidians identified from the tasar silkworms. Clustering of few microsporidian isolates was in accordance with the geographic origin. The results indicate that the RAPD profiles and specific/unique genetic markers can be used for differentiating as well as to identify different microsporidians with considerable accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.57-68
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• 2017

Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

• Composites Research
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• v.30 no.5
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• pp.323-330
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• 2017

In this study, multiscale analysis in which the information obtained from molecular dynamics simulation is applied to the continuum mechanics level is conducted to investigate the effects of clustering of silicon carbide nanoparticles reinforced into polypropylene matrix on mechanical behavior of nanocomposites. The elastic behavior of polymer nanocomposites is observed for various states of nanoparticulate agglomeration according to the model reflecting the degradation of interphase properties. In addition, factors which mainly affect the mechanical behavior of the nanocomposites are identified, and new index 'clustering density' is defined. The correlation between the clustering density and the elastic modulus of nanocomposites is understood. As the clustering density increases, the interfacial effect decreased and finally the improvement of mechanical properties is suppressed. By considering the random distribution of the nanoparticles, the range of elastic modulus of nanocomposites for same value of clustering density can be investigated. The correlation can be expressed in the form of exponential function, and the mechanical behavior of the polymer nanocomposites can be effectively predicted by using the nanoparticulate clustering density.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.101-106
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• 2012

Ship handling simulator is a virtual ship navigating system with three dimensional screen system and simulation programs. FTS simulation can produce theoretically infinite experiment tests without time constraint, but which results in collecting determinstic observations. RTS simulation can collect statistical observations but has disadvantage of spending at least 30 minutes for a single experiment. The previous studies suggested that the number of experiment conditions to be tested could be reduced to obtain random data with RTS simulation by focusing on highly difficult experiment condition for ship handling. It has the limitation of not estimating the distribution of ship handling difficulty for the route. In this paper, similarity and clustering analysis are suggested for reduction methodology of experiment conditions. Similarity of experiment conditions are measured as follows: euclidean distance of ship handling difficulty index and correlation matrix of distance differences from the designed route. Clustering analysis and multi-dimensional scaling are applied to classify experiment conditions with measured similarity into reducing the number of RTS simulation conditions. An empirical result on Dangin harbor is shown and discussed.