• Parasites, Hosts and Diseases
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• v.54 no.4
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• pp.503-507
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• 2016

The genus Spirometra belongs to the family Diphyllobothriidae and order Pseudophyllidea, and includes intestinal parasites of cats and dogs. In this study, a plerocercoid labeled as Spirometra mansonoides from the USA was examined for species identification and phylogenetic analysis using 2 complete mitochondrial genes, cytochrome c oxidase I (cox1) and NADH dehydrogenase subunit 3 (nad3). The cox1 sequences (1,566 bp) of the plerocercoid specimen (USA) showed 99.2% similarity to the reference sequences of the plerocercoid of Korean Spirometra decipiens (GenBank no. KJ599679), and 99.1% similarity in regard to nad3 (346 bp). Phylogenetic tree topologies generated using 4 analytical methods were identical and showed high confidence levels with bootstrap values of 1.00, 100%, 100%, and 100% for Bayesian inference (BI), maximum-likelihood (ML), neighbor-joining (NJ), and maximum parsimony (MP) methods, respectively. Representatives of Diphyllobothrium and Spirometra species formed a monophyletic group, and the sister-genera status between these species was well supported. Trapezoic proglottids in the posterior 1/5 region of an adult worm obtained from an experimentally infected cat were morphologically examined. The outer uterine loop of the uterus coiling characteristically consisted of 2 complete turns. The results clearly indicated that the examined Spirometra specimen from the USA matched to S. decipiens very well, and indicated possible presence of the life cycle of this species in this region.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.