• 제목/요약/키워드: Similarity Criterion

검색결과 93건 처리시간 0.027초

Discovery of Novel 4${\alpha}$ helix Cytokine by Hidden Markov Model Analysis

  • Du, Chunjuan;Zeng, Yanjun;Zhu, Yunping;He, Fuchu
    • 한국생물정보학회:학술대회논문집
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    • 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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    • pp.41-44
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    • 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.

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Evaluation criterion for different methods of multiple-attribute group decision making with interval-valued intuitionistic fuzzy information

  • Qiu, Junda;Li, Lei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권7호
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    • pp.3128-3149
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    • 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.

Minimum Message Length and Classical Methods for Model Selection in Univariate Polynomial Regression

  • Viswanathan, Murlikrishna;Yang, Young-Kyu;WhangBo, Taeg-Keun
    • ETRI Journal
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    • 제27권6호
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    • pp.747-758
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    • 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).

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최적화 기법을 이용한 비행체 구조물 동특성 해석 모델의 최신화 연구 (A Study on Updating of Analytic Model of Dynamics for Aircraft Structures Using Optimization Technique)

  • 이기두;이영신;김동수
    • 한국항공우주학회지
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    • 제37권2호
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    • pp.131-138
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    • 2009
  • 해석용 모델의 검증이란 완성된 모델이 실제 제품의 특성을 반영하고 있는지에 대한 확인절차이다. 일반적으로 해석모델작성 시 형상의 단순화 및 비선형특성의 반영에 대한 한계 등으로 공학적 가정을 이용하므로 실제 구조와는 다른 물리적, 기계적 특성을 갖게 된다. 본 연구에서는 순차적 2차계획법(Sequential Quadratic Programming, SQP)을 이용하는 목표달성기법(Goal-Attainment Method)의 다목적 최적화 기법을 이용하여 활공체 날개의 정적 처짐과 고유진동수 차이를 최소화하는 방법으로 구조모델의 최신화를 수행하였으며, 모드형상의 일치성을 정량적으로 판단하기 위하여 Modal Assurance Criterion(MAC)를 이용하였다.

HMM 기반의 TTS를 위한 상호유사도 비율을 이용한 결정트리 기반의 문맥 군집화 (Decision Tree Based Context Clustering with Cross Likelihood Ratio for HMM-based TTS)

  • 정치상;강홍구
    • 한국음향학회지
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    • 제32권2호
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    • pp.174-180
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    • 2013
  • 본 논문은 HMM 기반의 TTS 시스템을 위하여 상호유사도 비율을 이용한 결정트리 기반의 문맥 군집화 알고리즘을 제안한다. 기존의 알고리즘들은 유사한 통계적 특성을 가지는 문맥종속 HMM을 하나로 묶고 있다. 그러나 기존의 알고리즘들은 결정트리의 나누어진 노드간의 통계적 유사도를 고려하지 않음으로 인하여 최종 노드 사이의 통계적인 차이를 보장하지 못한다. 제안한 알고리즘은 분리된 노드들 간의 통계적 유사도를 최소화하여 모델 파라미터의 신뢰도를 향상시킨다. 실험 결과를 통해 제안한 알고리즘이 기존의 알고리즘들에 비해 우수한 성능을 나타낸다는 것을 확인할 수 있다.

Genetic Diversity and DNA Polymorphism in Platycodon grandiflorum DC. Collected from East-Asian Area

  • Park, Chun-Geun;Yan, Zhi-Yi;Lee, Sang-Chul;Shon, Tae-Kwon;Park, Hee-Woon;Jin, Dong-Chun
    • 한국약용작물학회지
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    • 제13권2호
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    • pp.115-120
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    • 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.

Diversity of Butyrivibrio Group Bacteria in the Rumen of Goats and Its Response to the Supplementation of Garlic Oil

  • Zhu, Zhi;Hang, Suqin;Mao, Shengyong;Zhu, Weiyun
    • Asian-Australasian Journal of Animal Sciences
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    • 제27권2호
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    • pp.179-186
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    • 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.

비선형 평활화와 통계적 상관성에 기반을 둔 인식성능 개선 (An Improvement of Recognition Performance Based on Nonlinear Equalization and Statistical Correlation)

  • 신현수;조용현
    • 한국지능시스템학회논문지
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    • 제22권5호
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    • pp.555-562
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    • 2012
  • 본 논문에서는 영상의 비선형 평활화와 특징들의 통계적 상관성에 기반을 둔 조합형 인식성능 개선기법을 제안하였다. 여기서 비선형 평활화는 로지스틱 함수에 기반을 둔 히스토그램 재조정의 전처리 기법으로 영상의 밝기를 조정하여 화질을 개선하기 위함이다. 통계적 상관성은 정규상호상관계수에 의해 측정되며, 이는 유사도를 좀 더 빠르고 정확하게 측정하기 위함이다. 또한 독립성분분석에 의한 국부적인 특징들을 대상으로 정규상호상관을 계산함으로써 좀 더 정확한 유사도를 통계적으로 측정하기 위함이다. 제안된 기법을 30개 40*50픽셀의 명암도 변화를 가지는 얼굴영상들을 대상으로 실험한 결과, 전처리를 하지 않은 기법이나 기존 및 적응적 변형히스토그램 평활화에 의한 전처리 기법에 비해 각각 영상의 속성을 잘 반영한 우수한 인식성능이 있음을 확인하였다.

이종의 공간 데이터 셋의 면 객체 자동 매칭 방법 (Automated Areal Feature Matching in Different Spatial Data-sets)

  • 김지영;이재빈
    • 대한공간정보학회지
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    • 제24권1호
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    • pp.89-98
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    • 2016
  • 본 연구에서는 축척과 갱신 주기가 상이한 이종의 공간 데이터 셋을 융합하기 위하여 사용자의 개입을 최소화하면서 다대다 관계에도 적용이 가능한 기하학적 방법론 기반의 면 객체 자동 매칭 방법을 제안하였다. 이를 위하여 첫째, 포함함수가 0.4 이상인 객체(노드)는 인접행렬에서 에지로 연결되었고, 이들 인접행렬의 곱을 반복적으로 수행하여 다대다 관계를 포함하는 후보 매칭 쌍을 선정하였다. 다대다 관계인 면 객체들은 알고리즘으로 생성된 convex hull로 단일 면 객체로 변환하였다. 기하학적 매칭을 위하여, 매칭 기준을 설정하고, 이들을 유사도 함수를 이용하여 유사도를 계산하였다. 다음으로 변환된 유사도와 CRITIC 방법으로 도출된 가중치를 선형 조합하여 형상 유사도를 계산하였다. 마지막으로 훈련자료에서 모든 가중치에 대한 정확도와 재현율을 나타낸 PR 곡선의 교차점인 EER로 임계값을 선정하고, 이 임계값을 기준으로 매칭 유무를 판별하였다. 제안된 방법을 수치지도와 도로명 주소기본도에 적용한 결과, 일부 다대다 관계에서 잘못 매칭되는 경우를 시각적으로 확인할 수 있었으나, 통계적 평가에서 정확도, 재현율, F-measure가 각각 0.951, 0.906, 0.928로 높게 나타났다. 이는 제안된 방법으로 이종의 공간 데이터 셋을 자동으로 매칭하는데 그 정확도가 높음을 의미한다. 그러나 일부 오류가 발생한 다대다 관계인 후보 매칭 쌍을 정확하게 정량화하기 위해서 포함함수나 매칭 기준에 대한 연구가 진행되어야 할 것이다.

SDN-Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra-Dense Small Cell Networks

  • Yang, Guang;Cao, Yewen;Esmailpour, Amir;Wang, Deqiang
    • ETRI Journal
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    • 제40권2호
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    • pp.227-236
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    • 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.