• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.41-44
• /
• 2005

Cytokines play a crucial role in the immune and inflammatory responses. But because of the high evolutionary rate of these proteins, the similarity between different members of their family is very low, which makes the identification of novel members of cytokines very difficult. According to this point, a new bioinformatic strategy to identify novel cytokine of the short-chain and long-chain 4${\alpha}$ helix cytokine using hidden markov model (HMM) is proposed in the paper. As a result, two motifs were created on the two train data sets, which were used to search three different databases. In order to improve the result, a strict criterion is established to filter the novel cytokines in the subject proteins. Finally, according to their E-value, scores and the criterion, four subject proteins are predicted to be possible novel cytokines for each family respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3128-3149
• /
• 2018

A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts' preferences, aggregation of experts' opinions, and comparison of the alternatives. Experts' preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts' preferences with IVIFN is presented. In this approach, experts' preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach.

• ETRI Journal
• /
• v.27 no.6
• /
• pp.747-758
• /
• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.2
• /
• pp.131-138
• /
• 2009

Analytical modal verification is considered as the process to provide an acceptable description of the subject structure's behaviour. In general, results of original analytical model are different with actual structure results to uncertainty like non-linearity of material, boundary and modified shape, etc. In this paper, the dynamic model of glider's wing is correlated with static deformation and vibration test results by goal-attainment method, multi-objects optimization technique. The structural responses are predicted by using finite element method and optimization is carried out by using the SQP(sequential quadratic programming) method which is widely used in the constrained nonlinear optimization problem. The MAC(Modal Assurance Criterion) is used to modify the mode shapes and quantify the similarity.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.2
• /
• pp.174-180
• /
• 2013

This paper proposes a decision tree based context clustering algorithm for HMM-based speech synthesis systems using the cross likelihood ratio with a hierarchical prior (CLRHP). Conventional algorithms tie the context-dependent HMM states that have similar statistical characteristics, but they do not consider the statistical similarity of split child nodes, which does not guarantee the statistical difference between the final leaf nodes. The proposed CLRHP algorithm improves the reliability of model parameters by taking a criterion of minimizing the statistical similarity of split child nodes. Experimental results verify the superiority of the proposed approach to conventional ones.

• Korean Journal of Medicinal Crop Science
• /
• v.13 no.2
• /
• pp.115-120
• /
• 2005

Broadening the genetic base of Platycodon grandiflorum DC. cultivar to sustain improvement requires assessment of genetic diversity available in P. grandiflorum DC.. The objective of this study was to analyze the genetic variation, genetic relationship among 48 samples collected from East-Asian Area by means of RAPD-PCR (random amplified polymorphic DNA-polymerase chain reaction) markers. From the 18 primers tested, produced total 211 bands with an average of 11.7 bands per primer and obtained 103 polymorphic band with an average of 5.7 bands per primer,s revealed relatively high percentage of polymorphic bands (48.8%). The genetic similarities calculated from RAPD data varied from 0.688 to 0.994 and were clustered to six major groups on a criterion of 0.78 similarity coefficient. The present study has revealed the significant genetic similarity among the samples tested. The analysis of genetic relationships in P. grandiflorum using RAPD-PCR banding data can be useful for the breed improvement.

• Asian-Australasian Journal of Animal Sciences
• /
• v.27 no.2
• /
• pp.179-186
• /
• 2014

This study aimed to investigate the diversity of the Butyrivibrio group bacteria in goat rumen and its response to garlic oil (GO) supplementation as revealed by molecular analysis of cloned 16S rRNA genes. Six wethers fitted with ruminal fistulas were assigned to two groups for a cross-over design with 28-d experimental period and 14-d interval. Goats were fed a basal diet without (control) or with GO ruminal infusion (0.8 g/d). Ruminal contents were used for DNA extraction collected before morning feeding on d 28. A total bacterial clone library was firstly constructed by nearly full-length 16S rRNA gene cloned sequences using universal primers. The resulting plasmids selected by Butyrivibrio-specific primers were used to construct a Butyrivibrio group-specific bacterial clone library. Butyrivibrio group represented 12.98% and 10.95% of total bacteria in control and GO group, respectively. In libraries, clones were classified to the genus Pseudobutyrivibrio, Butyrivibrio and others within the family Lachnospiraceae. Additionally, some specific clones were observed in GO group, being classified to the genus Ruminococcus and others within the family Ruminococcaceae. Based on the criterion that the similarity was 97% or greater with database sequences, there were 29.73% and 18.42% of clones identified as known isolates (i.e. B. proteoclasticus and Ps. ruminis) in control and GO groups, respectively. Further clones identified as B. fibrisolvens (5.41%) and R. flavefaciens (7.89%) were specifically found in control and GO groups, respectively. The majority of clones resembled Ps. ruminis (98% to 99% similarity), except for Lachnospiraceae bacteria (87% to 92% similarity) in the two libraries. The two clone libraries also appeared different in Shannon diversity index (control 2.47 and GO group 2.91). Our results indicated that the Butyrivibrio group bacteria had a complex community with considerable unknown species in the goat rumen.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.5
• /
• pp.555-562
• /
• 2012

This paper presents a hybrid method for improving the recognition performance, which is based on the nonlinear histogram equalization, features extraction, and statistical correlation of images. The nonlinear histogram equalization based on a logistic function is applied to adaptively improve the quality by adjusting the brightness of the image according to its intensity level frequency. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to rapidly and accurately express the similarity between the images. The local features based on independent component analysis(ICA) that is used to calculate the NCC, is also applied to statistically measure the correct similarity in each images. The proposed method has been applied to the problem for recognizing the 30-face images of 40*50 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the preprocessing, or the methods of conventional and adaptively modified histogram equalization, respectively.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.1
• /
• pp.89-98
• /
• 2016

In this paper, we proposed an automated areal feature matching method based on geometric similarity without user intervention and is applied into areal features of many-to-many relation, for confusion of spatial data-sets of different scale and updating cycle. Firstly, areal feature(node) that a value of inclusion function is more than 0.4 was connected as an edge in adjacency matrix and candidate corresponding areal features included many-to-many relation was identified by multiplication of adjacency matrix. For geometrical matching, these multiple candidates corresponding areal features were transformed into an aggregated polygon as a convex hull generated by a curve-fitting algorithm. Secondly, we defined matching criteria to measure geometrical quality, and these criteria were changed into normalized values, similarity, by similarity function. Next, shape similarity is defined as a weighted linear combination of these similarities and weights which are calculated by Criteria Importance Through Intercriteria Correlation(CRITIC) method. Finally, in training data, we identified Equal Error Rate(EER) which is trade-off value in a plot of precision versus recall for all threshold values(PR curve) as a threshold and decided if these candidate pairs are corresponding pairs or not. To the result of applying the proposed method in a digital topographic map and a base map of address system(KAIS), we confirmed that some many-to-many areal features were mis-detected in visual evaluation and precision, recall and F-Measure was highly 0.951, 0.906, 0.928, respectively in statistical evaluation. These means that accuracy of the automated matching between different spatial data-sets by the proposed method is highly. However, we should do a research on an inclusion function and a detail matching criterion to exactly quantify many-to-many areal features in future.

• ETRI Journal
• /
• v.40 no.2
• /
• pp.227-236
• /
• 2018

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.