• 제목/요약/키워드: Pattern Discovery

검색결과 149건 처리시간 0.02초

Design, Combinatorial Library Synthesis and Biological Evaluation of Nonpeptide Scaffold for Beta Turns

  • Im, I-Sak;Thomas R.Webb;Dona Chianelli;Kim, Yong-Chul
    • 대한약학회:학술대회논문집
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    • 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-1
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    • pp.91-91
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    • 2003
  • The beta-turn has been implicated as an important conformation for biological recognition of peptides or proteins. We adapted the concept of general Ca atom positioning from the cluster analysis and recombination of each ideal beta-turn conformation pattern by Garland and Dean (1. Computer-Aided Molecular Design, 1999, 13, 469) as one strategy of designing non-peptide beta-turn scaffolds. (omitted)

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핵연료 파손 예측을 위한 경험적 자료와 결정론적 모델의 접합 방법 (A Study on the Method of Combining Empirical Data and Deterministic Model for Fuel Failure Prediction)

  • Cho, Byeong-Ho;Yoon, Young-Ku;Chang, Soon-Heung
    • Nuclear Engineering and Technology
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    • 제19권4호
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    • pp.233-241
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    • 1987
  • 본 연구는 제한된 수의 핵연료의 경험적 파손자료로부터 핵연료 파손 확률을 현실적으로 예측하기 위해 결정론적 모델로부터의 파손화률 예측치와 실제 경험적 자료로부터의 파손 확률 예측치를 접합하는 방법을 시도하였다. 이 접합 방법에 의한 파손 화률 예측치는 결정론적 모델 또는 경험적 파손 자료로부터의 독립적인 예측치보다 신뢰도가 높다. 본 연구에서는 핵연료 성능 예측코드인 SPEAR의 방법론을 응용한 핵연료 파손 패턴의 체계적 발견법 (hierarchical pattern discovery)이 접합 모델에서의 결정론적 모델로부터의 예측치에 대한 가중치와 패턴 경계를 체계적으로 찾기 위해 고안되었다. 이 연구에서 개발된 접합 방법을 PROFIT모델과 경험적 파손자료를 이용하여 CANDU형 핵연료 재장전중 출력 상승에 의해 수반되는 핵연료파손 예측에 적응시켜 보았다.

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Overview of Fuzzy Associations Mining

  • Chen, Guoqing;Wei, Qiang;Kerre, Etienne;Wets, Geert
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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    • pp.1-6
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    • 2003
  • Associations, as specific forms of knowledge, reflect relationships among items in databases, and have been widely studied in the fields of knowledge discovery and data mining. Recent years have witnessed many efforts on discovering fuzzy associations, aimed at coping with fuzziness in knowledge representation and decision support processes. This paper focuses on associations of three kinds, namely, association rules, functional dependencies and pattern associations, and overviews major fuzzy logic extensions accordingly.

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연관규칙을 이용한 고객의 구매경향에 관한 연구 (A Study on Customer's Purchase Trend Using Association Rule)

  • 임영문;최영두
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2000년도 추계학술발표논문집
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    • pp.299-306
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    • 2000
  • General definition of data mining is the knowledge discovery or is to extract hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important work is to find association rules in data mining. The objective of this paper is to find customer's trend using association rule from analysis of database and the result can be used as fundamental data for CRM(Customer Relationship Management). This paper uses Apriori algorithm and FoodMart data in order to find association rules.

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다주파수 생체임피던스 저항을 이용한 당뇨병 환자의 허증 변증 예측 (Prediction of Deficiency Pattern in Diabetic Patients Using Multi-frequency Bioimpedance Resistance)

  • 김가혜;김슬기;차지윤;유호룡;김재욱
    • 동의생리병리학회지
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    • 제36권3호
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    • pp.94-99
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    • 2022
  • The discovery of biomarkers related to pattern identification (PI), the core diagnostic theory of Korean medicine (KM), is one of the methods that can provide objective and reliable evidence by applying PI to clinical practice. In this study, 40 diabetic patients and 41 healthy control subjects recruited from the Korean medicine clinic were examined to determine the human electrical response related to the deficiency pattern, a representative pattern of diabetes. Qi-Blood-Yin-Yang deficiency pattern scores, which are representative deficiency patterns for diabetes mellitus, were obtained through a questionnaire with verified reliability and validity, and the human electrical response was measured non-invasively using a bioimpedance meter. In ANCOVA analysis using gender as a covariate, the 5 kHz frequency resistance and 5-250 kHz frequency reactance were significantly lower in the diabetic group than in non-diabetic control group. In addition, the multiple regression analysis showed a positive correlation (R2=0.11~0.19) between the Yang deficiency pattern score and resistance value for the diabetic group; the correlation was higher at higher frequencies of 50kHz (R2=0.18) and 250kHz (R2=0.19) compared to 5kHz(R2=0.11). In contrast, there was no such significant association in the control group. It implies that bioimpedance resistance measured at finite frequencies may be useful in predicting Yang deficiency, which is closely related to diabetic complications by reflecting the decrease in body water content and metabolism. In the future, large-scale planned clinical studies will be needed to identify biomarkers associated with different types of PI in diabetes.

고객군의 지리적 패턴 발견을 위한 데이터마트 구현과 시각적 분석에 관한 연구 (Buying Pattern Discovery Using Spatio-Temporal Data Mart and Visual Analysis)

  • 조재희;하병국
    • 한국IT서비스학회지
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    • 제9권1호
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    • pp.127-139
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    • 2010
  • Due to the development of information technology and business related to geographical location of customer, the need for the storage and analysis of geographical location data is increasing rapidly. Geographical location data have a spatio-temporal nature which is different from typical business data. Therefore, different methods of data storage and analysis are required. This paper proposes a multi-dimensional data model and data visualization to analyze geographical location data efficiently and effectively. Purchase order data of an online farm products brokerage business was used to build prototype datamart. RFM scores are calculated to classify customers and geocoding technology is applied to display information on maps, thereby to enhance data visualization.

가중치 부여 부정 트리 패턴 추출 (Weighted Negative Tree Pattern Discovery)

  • 백주련;김진영
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
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    • pp.23-26
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    • 2019
  • 사물인터넷(IoT)은 지금의 우리가 살고 일하는 모든 방식을 변화시키고 있다. IoT를 통해 데이터를 생성하고 저장하고 연결된 장치와 상호작용하여 비즈니스는 물론 우리의 일상 생활을 개선하고 있는 것이다. 무수히 많은 센서들이 연결된 세상은 센서들에 의해 그 어느 때보다 거대한 양의 데이터들을 생산하고 있다. JSON, XML 같은 트리 구조의 데이터 타입은 대량 데이터 저장 전송 교환 등에 주요하게 사용되는데 이는 트리 구조가 이형 데이터 간의 유연한 정보 전송과 교환을 가능하게 하기 때문이다. 반면에, 효용성 높은 정보나 감추어져 있는 정보들을 트리 구조의 대량 데이터들로부터 추출하는 것은 일반 데이터 구조에 비해 훨씬 어려우며 더 난해한 문제들을 발생시킨다. 본 논문에서는 트리 구조의 대량 스트리밍 데이터로부터 가중치가 부여된 주요한 부정 패턴들을 추출하기 위한 방법을 공식화한다.

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Query Processing based Branch Node Stream for XML Message Broker

  • Ko, Hye-Kyeong
    • International journal of advanced smart convergence
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    • 제10권2호
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    • pp.64-72
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    • 2021
  • XML message brokers have a lot of importance because XML has become a practical standard for data exchange in many applications. Message brokers covered in this document store many users. This paper is a study of the processing of twig pattern queries in XML documents using branching node streams in XML message broker structures. This work is about query processing in XML documents, especially for query processing with XML twig patterns in the XML message broker structure and proposed a method to reduce query processing time when parsing documents with XML twig patterns by processing information. In this paper, the twig pattern query processing method of documents using the branching node stream removes the twigging value of the branch node that does not include the labeling value of the branch node stream when it receives a twig query from the client. In this paper, the leaf node discovery time can be reduced by reducing the navigation time of nodes in XML documents that are matched to leaf nodes in twig queries for client twig queries. Overall, the overall processing time to respond to queries is reduced, allowing for rapid question-answer processing.

Identification of Potential Prognostic Biomarkers in lung cancer patients based on Pattern Identification of Traditional Korean Medicine Running title: A biomarker based on the Korean pattern identification for lung cancer

  • Ji Hye Kim;Hyun Sub Cheong;Chunhoo Cheon;Sooyeon Kang;Hyun Koo Kim;Hyoung Doo Shin;Seong-Gyu Ko
    • 대한예방한의학회지
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    • 제27권2호
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    • pp.35-48
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    • 2023
  • Objective : We studied prognostic biomarkers discovery for lung cancer based on the pattern identification for the personalized Korean medicine. Methods : Using 30 tissue samples, we performed a whole exome sequencing to examine the genetic differences among three groups. Results : The exome sequencing identified among 23,490 SNPs germline variants, 12 variants showed significant frequency differences between Xu and Stasis groups (P<0.0005). As similar, 18 and 10 variants were identified in analysis for Xu vs. Gentleness group and Stasis vs. Gentleness group, respectively (P<0.001). Our exome sequencing also found 8,792 lung cancer specific variants and among the groups identified 6, 34, and 12 variants which showed significant allele frequency differences in the comparison groups; Xu vs. Stasis, Xu vs. Gentleness group, and Stasis vs. Gentleness group. As a result of PCA analysis, in germline data set, Xu group was divided from other groups. Analysis using somatic variants also showed similar result. And in gene ontology analysis using pattern identification variants, we found genes like as FUT3, MYCBPAP, and ST5 were related to tumorigenicity, and tumor metastasis in comparison between Xu and Stasis. Other significant SNPs for two were responsible for eye morphogenesis and olfactory receptor activity. Classification of somatic pattern identification variants showed close relationship in multicellular organism reproduction, anion-anion antiporter activity, and GTPase regulator activity. Conclusions : Taken together, our study identified 40 variants in 29 genes in association with germline difference of pattern identification groups and 52 variants in 47 genes in somatic cancer tissues.

스마트카드 빅데이터를 이용한 서울시 지하철 이동패턴 분석 (Discovery of Travel Patterns in Seoul Metropolitan Subway Using Big Data of Smart Card Transaction Systems)

  • 김관호;오규협;이영규;정재윤
    • 한국전자거래학회지
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    • 제18권3호
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    • pp.211-222
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    • 2013
  • 지리적으로 인접되어 있으면서 이동관점에서 같은 역할을 수행하는 Zone의 파악은 사람들의 이동흐름을 이해하고 도시개발 및 이동편의성 개선 등을 위한 중요한 정보로 활용된다. 그러나 기존의 연구는 특정 지점간의 이동과 Zone 발견을 개별적으로 수행하여, 거시적 관점에서의 이동패턴을 이해하는 데에는 한계가 존재한다. 따라서 본 연구에서는 스마트카드 전자거래 빅데이터로부터 Zone들을 발견하고 동시에 Zone들 간의 관계를 설명하는 클러스터링 기반의 이동패턴 분석기법을 제안한다. 또한, 설명력과 종속성 관점에서 이동패턴을 정량적으로 평가하는 지표를 제안한다. 제안된 분석기법을 이용하여 서울시 지하철에서 수집된 실 데이터를 분석하여 서울시에서의 이동패턴을 밝혀내고 시각화하였다.