• 제목/요약/키워드: consistency of derived data

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다중축척 공간 데이터베이스의 데이터 갱신 (Data Update on Multi-Scale Databases)

  • 권오제;강혜경;이기준
    • Spatial Information Research
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    • 제12권3호
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    • pp.239-249
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    • 2004
  • 다중축척 데이터베이스는 동일한 공간을 다른 축척으로 표현하는 공간데이터베이스의 집합이다. 이 다중축척 데이터베이스는 소축척의 정밀한 공간데이터베이스로부터 유도될 수 있다. 본 논문은 유도된 다중축척 데이터베이스의 갱신문제를 다루고자 한다. 다중축척 데이터베이스의 갱신은 수정된 원시데이터로부터 직접 유도된 데이터뿐만 아니라 갱신되지 않은 원시데이터로부터 유도된 데이터들까지 갱신해야 한다. 이것은 현재 데이터 갱신방법들을 다중축척 데이터베이스에 그대로 적용할 수 없는 이유가 되는데, 현재 방법들은 수정된 원시데이터로부터 직접 유도된 데이터만을 갱신하기 때문이다. 이 다중축척 데이터베이스의 갱신관리는 공간 데이터베이스 관리시스템(혹은 GIS)에서 제공되어야 할 중요한 기능이다. 이 논문에서는 다중축척 데이터 베이스 갱신을 위한 규칙 및 알고리즘을 제안하고, 이것을 ESRI의 ArcObject를 이용하여 개발한 프로토타입을 소개한다. 본 연구에서 제공하는 갱신방법은 다중축척 데이터베이스들간의 일관성을 유지시켜주고, 유도된 다중축척 데이터베이스의 무결성을 보장해 줄 수 있다는 점에서 의의가 있다.

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Estimation of Bivariate Survival Function for Possibly Censored Data

  • Park Hyo-Il;Na Jong-Hwa
    • Communications for Statistical Applications and Methods
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    • 제12권3호
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    • pp.783-795
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    • 2005
  • We consider to obtain an estimate of bivariate survival function for the right censored data with the assumption that the two components of censoring vector are independent. The estimate is derived from an ad hoc approach based on the representation of survival function. Then the resulting estimate can be considered as an extension of the Susarla- Van Ryzin estimate to the bivariate data. Also we show the consistency and weak convergence for the proposed estimate. Finally we compare our estimate with Dabrowska's estimate with an example and discuss some properties of our estimate with brief comment on the extension to the multivariate case.

Consistency check algorithm for validation and re-diagnosis to improve the accuracy of abnormality diagnosis in nuclear power plants

  • Kim, Geunhee;Kim, Jae Min;Shin, Ji Hyeon;Lee, Seung Jun
    • Nuclear Engineering and Technology
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    • 제54권10호
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    • pp.3620-3630
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    • 2022
  • The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

다중축척 공간 데이터베이스의 축소연산자를 위한 위상관계 일관성 평가 (The Consistency Assessment of Topological Relationships For a Collapse Operator in Multi-Scale Spatial Databases)

  • 강혜경;이기준
    • 정보처리학회논문지D
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    • 제12D권6호
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    • pp.837-848
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    • 2005
  • 다중축척 공간데이터베이스란 동일한 현실 공간을 여러 축척으로 저장한 것으로, 기존에 구축된 원시 공간 데이터베이스로부터 유도될 수 있다. 이 유도과정에서 원시 데이터베이스에 있던 기하 및 위상관계는 변형이 되고, 이 관계 변형은 유도된 데이터베이스의 무결성을 보장하지 못하는 원인이 되므로, 유도과정이 수행된 후에는 반드시 유도된 데이터베이스와 원시 데이터베이스의 관계 일관성을 조사해야한다. 이 논문에서는 원시 데이터베이스와 유도된 다중축척 데이터베이스간의 위상 관계 일관성을 평가하는 방법을 제시하겠다. 특히, 2차원 공간객체가 1차원으로 축소되었을 때 위상관계의 일관성을 평가하는 방법을 제한할 것이며, 이 평가 방법들의 구현에 대해서 기술하고, 사례를 이용하여 구현결과를 기술하겠다.

CONSISTENT AND ASYMPTOTICALLY NORMAL ESTIMATORS FOR PERIODIC BILINEAR MODELS

  • Bibi, Abdelouahab;Gautier, Antony
    • 대한수학회보
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    • 제47권5호
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    • pp.889-905
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    • 2010
  • In this paper, a distribution free approach to the parameter estimation of a simple bilinear model with periodic coefficients is presented. The proposed method relies on minimum distance estimator based on the autocovariances of the squared process. Consistency and asymptotic normality of the estimator, as well as hypotheses testing, are derived. Numerical experiments on simulated data sets are presented to highlight the theoretical results.

A Change-point Estimator with Unsymmetric Fourier Series

  • Kim, Jaehee
    • Journal of the Korean Statistical Society
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    • 제31권4호
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    • pp.533-543
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    • 2002
  • In this paper we propose a change-point estimator with left and right regressions using the sample Fourier coefficients on the orthonormal bases. The window size is different according to the data in the left side and in the right side at each point. The asymptotic properties of the proposed change-point estimator are established. The limiting distribution and the consistency of the estimator are derived.

Validation of the Nurses' Involvement in Dying Patients and Family Care-Korean Version

  • Kim, Mi Yeon;Lee, Hanna;Lee, Inyoung;Lee, Mirim;Cho, Haeryun
    • Journal of Hospice and Palliative Care
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    • 제23권4호
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    • pp.228-240
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    • 2020
  • Purpose: The purpose of this study was to test the validity of the Korean version of the Nurses' Involvement in Dying Patients and Family Care (NIDPFC) instrument. Methods: Data were collected from 410 registered nurses at a university hospital, general hospitals, and a convalescent hospital. Data were collected from June 23 to July 17, 2020. Internal consistency reliability, construct validity, and criterion validity were examined using the SPSS and AMOS software. Results: Of the 35 preliminary items of the instrument, 24 items were finally selected after evaluating the content validity, analyzing the items, and assessing construct validity. The following four factors were derived: "burden" (seven items), "deep involvement" (eight items), "resilience" (five items), and "empathy" (four items), with a cumulative explanatory variance of 55.2%. For criterion validity, a significant positive relationship was found between the NIDPFC and attitudes toward caring for the dying. For internal consistency reliability, the Cronbach's α was 0.82. Conclusion: The validity and reliability of the NIDPFC were verified. Therefore, the NIDPFC is an effective instrument to use in further studies.

계층분석법을 이용한 강의평가 요인도출과 우선순위분석 (Factor Derivation of Course Evaluation and Priority Analysis Using Analytic Hierarchy Process)

  • 안수현;이상준
    • 실천공학교육논문지
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    • 제14권3호
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    • pp.513-522
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    • 2022
  • 강의평가는 대학교육의 질을 향상시키고 수업을 개선하는데 유용한 정보로 활용된다. 본 연구는 강의평가를 구성하는 요인을 탐색하고자 선행연구와 FGI를 통해 구성요인을 도출하고 계층분석법(AHP: Analytic Hierarchy Process)을 통해 요인간 상대적 중요도 및 우선순위를 파악하였다. 이를 위해 5개의 구성요인과 15개의 평가항목을 도출하였다. 강의평가 요인개발의 전문성과 공정성을 확보하기 위해 학생과 교원을 대상으로 설문을 실시하여 총 20부의 유효한 자료를 수집하였고, 일치도 검증을 완료한 자료를 토대로 각 평가항목의 가중치를 산출하였다. 분석 결과 강의평가 요인구성에 있어서 학생은 수업 내용, 수업 방법, 수업 운영, 수업 평가, 수업 계획 순으로, 교원은 수업 내용, 수업 운영, 수업 방법, 수업 평가, 수업 계획 순으로 중요하다고 평가하였다. 본 연구 결과를 바탕으로 대학교육의 질 관리 차원에서 강의평가의 효율성과 신뢰성 향상을 위해 다양한 분석과 연구가 있기를 기대한다.

에너지 시뮬레이션을 위한 서울의 표준 외기 온도 및 습도 데이터 (Standard Weather Data of Seoul for Energy Simulation)

  • 김성실;김영일
    • 설비공학논문집
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    • 제14권11호
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    • pp.897-906
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    • 2002
  • Standard temperature and absolute humidity weather correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a 10-year period from 1991 to 2000. The mathematical equations can generate the daily and yearly variations of outdoor weather data with consistency unlike the measured data which may show abnormal behavior, Considering that each hour of the day follows a certain yearly pattern, the correlations are developed for each hour. The derived 24 simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.

The Effect of Bias in Data Set for Conceptual Clustering Algorithms

  • Lee, Gye Sung
    • International journal of advanced smart convergence
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    • 제8권3호
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    • pp.46-53
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    • 2019
  • When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.