• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.209-213
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• 2002

We study maximal second moment inequality and derive complete convergence for weighted sums of asymptotically almost negatively associated(AANA) random variables by applying this inequality. 2000 Mathematics Subject Classification : 60F05

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 춘계학술대회
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• pp.513-522
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• 2006

• 한국콘텐츠학회논문지
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• 제11권1호
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• pp.357-370
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• 2011

본 논문은 전략집단 이론을 활용하여 PP 사업자의 분석에 대한 현황, 성과와 한계를 살펴보고 개선방향을 모색했다. 기존 전략집단 개념을 활용한 PP사업자 분석의 한계는 전략집단의 도출이 통계결과에 따라 PP의 채널 특징만을 반영했다. 그렇기 때문에 전략 및 전략집단의 유형을 체계적이고 정교하게 분류하기 어려웠고 일반화가 어려운 것으로 나타났다. PP의 전략 변수 선정은 기업 및 사업부 수준(분석수준), 자원 및 경쟁범위와 관련된 변수를 사용했다. 향후에는 보다 적절한 절차에 따라 변수를 객관적으로 선정하여 자의성에 대한 논란을 피해야 할 것이다. PP 사업자의 전략집단 연구의 개선방향은 크게 다섯 가지로 요약되었다. 첫째, 전략 집단 분류 변수를 요인분석하고 몇 개의 요인으로 축약하여 분류기준이 되는 핵심 변수를 선정하는 것이 필요하다. 둘째, 산업 전문가들로부터 변수를 크로스 체크하여 일반화 가능성을 향상시켜야 한다. 셋째, 대리변수를 지양하고 PP 산업 특유의 속성을 반영할 수 있으며 경영자가 인지적 차원에서 지각할 수 있는 전략집단 모델을 연역적으로 개발해야 한다. 넷째, 전략집단의 분류 기준에 이동장벽과 분리기제 개념을 도입하고, 이를 통해 전략집단간 성과 차이를 규명하는 것이 바람직하다. 다섯째, PP 전략집단의 동태적 변화를 관찰하기 위한 종단적 연구가 시도되어야 한다.

• 가정과삶의질연구
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• 제10권1호통권19호
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• pp.27-42
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• 1992

This study focused on consumer information search activity and consumer's post-purchase satisfaction. For these purpose, a survey was conducted suing questionaires on 430 homemakers that lived in seoul. Statistics used for data were Frequency Distribution. Percentile, Mean, One-way AAANOVA., Scheffe-test, T-test, Pearson's correlation. Multiple Regression Analysis and Multiple Classification Analysis. The major findings were ; 1) The level of each amount information search was lower than average. And the level of consumer's post-purchase satisfaction was a little higher than average. 2) On amount of "noncommercial-personal" information search, the influencing variables were desire to seek information, education, brand royalty in turn. These three variables explained 7% of dependent variable's variance. 3) On amount of "noncommercial-media" information search, the influencing variables were desire to seek information, amount of internal information, education, occupational status in turn. These variables explained 14% of dependent variable's variance. 4) On amount of "commercial-personal" information search, the influencing variable was desire to seek information, and this variable explained 3.1% of dependent variable'a variance. 5) On amount of "commercial-media" information search, the influencing variables were desire to seek information, education, amount of internal information in turn. These three variables explained 12.1% dependent variable's variance. 6) Resulting from multiple classification analysis, influencing variables on consumer's post-purchase satisfaction were amount of noncommercial-media information search and printed media search, and brand royalty. These three variables explained 9% of dependent variable's variance. Furthermore, througout all the subareas of consumer's satisfaction, the amount of noncommercial-media information search was the most influencing variable.

• 품질경영학회지
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• 제47권3호
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.

• 한국산학기술학회논문지
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• 제21권6호
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• pp.374-382
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• 2020

본 연구에서 저장탄약 신뢰성평가(ASRP: Ammunition Stockpile Reliability Program)의 데이터 특성을 고려하여 입력변수를 줄이는 정규화기법을 제안함으로써 분류성능의 저하 없이 저장탄약 신뢰성분류 인경신경망모델의 학습 속도향상을 목표로 하였다. 탄약의 성능에 대한 기준은 국방규격(KDS: Korea Defense Specification)과 저장탄약 시험절차서(ASTP: Ammunition Stockpile reliability Test Procedure)에 규정되어 있으며, 평가결과 데이터는 이산형과 연속형 데이터가 복합적으로 구성되어 있다. 이러한 저장탄약 신뢰성평가의 데이터 특성을 고려하여 입력변수는 로트 추정 불량률(estimated lot percent nonconforming) 또는 고장률로 정규화 하였다. 또한 입력변수의 unitary hypercube를 유지하기 위하여 최소-최대 정규화를 2차로 수행하는 2단계 정규화 기법을 제안하였다. 제안된 2단계 정규화 기법은 저장탄약 신뢰성평가 데이터를 이용하여 비교한 결과 최소-최대 정규화와 유사하게 AUC(Area Under the ROC Curve)는 0.95 이상이었으며 학습속도는 학습 데이터 수와 은닉 계층의 노드 수에 따라 1.74 ~ 1.99 배 향상되었다.

• 전기학회논문지
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• 제64권7호
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• Journal of Information Processing Systems
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• 제7권4호
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.662-666
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• 2005

Using data gathered from expert opinion of plant and equipment professionals; this paper presents the key variables that may constitute a maintenance proficient plant operator. The Multivariate Discriminant Analysis (MDA) was applied to generate data and was tested for sensitivity analysis. Results showed that the MDA model was able to classify plant operators' proficiency at 94.10 percent accuracy and determined nine (9) key variables of a maintenance proficient plant operator. The key variables included: i) number of years of experience as equipment operator (PQ1); ii) eye-hand coordination (PQ9); iii) eye-hand-foot coordination (PQ10); iv) planning skills (TE16); v) pay/wage (MQ1); vi) work satisfaction (MQ4); vii) operator responsibilities as defined by management (MF1); viii) clear management policies (MF4); and ix) management pay scheme (MF5). The classification procedure of nine variables formed the general model with the equation viz: OMP (general) = 0.516PQ1 + 0.309PQ9 + 0.557PQ10 + 0.831TE16 + 0.8MQ1 + 0.0216MQ4 + 0.136MF1 + 0.28MF4 + 0.332MF5 - 4.387

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.621-621
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• 2004