• 한국군사과학기술학회지
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• 제17권5호
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• pp.611-619
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• 2014

Combining similar data provides larger data sets through conducting test for homogeneity of several samples under various production processes or samples from different LOTs. The test for homogeneity has been applied to either variable or attribute data, and for variable data set physical homogeneity has been tested without consideration of the specification to the set. This paper proposes a method for test of homogeneity based on quality level through using both variable data and the specification. Quality-based test for homogeneity as a way of combining data is implemented by test for coefficient of variation in the proposed method. The method was verified through the application to the data set in open literature. And possibility to combine performance data for various types of thermal battery was discussed in order to estimate operation reliability.

• 전자공학회논문지S
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• 제35S권11호
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• pp.1-11
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• 1998

본 논문에서는 ML(maximum likelihood) 원리와 디지탈 통신 시스템의 고유 특성인 유한 알파벳 특성(FAP: finite alphabet properties)에 근거한 블라인드 최대비 결합(MRC: maximal ratio combining)을 위한 간단한 방법들을 제안한다. 이 방법들은 아주 작은 길이의 데이터를 가지고도 채널 파라미터들을 정확하게 추정할 수 있기 때문에, 이 방법들을 사용하면 거의 완벽한 최대비 결합을 수행할 수 있다. 이 방법들은 교번투영 기법(alternating projection technique)을 이용하여 다이버시티 가지들에 대한 채널 파라미터와 데이터 시퀀스를 동시에 추정한다. 두 가지 다른 JC-DSE (joint combining and data sequence estimation)방법과 PC-BPE (pre-combining and blind phase estimation) 방법이 제안되며, 전영역 최적화를 보장할 수 있도록 하는 효율적인 초기화 방법도 제시된다. 모의실험 결과들을 통하여, 제안된 두가지 방법의 심볼 오류율과 채널 파라미터의 추정 정확도에 관한 성능을 보여준다.

• 한국컴퓨터정보학회논문지
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• 제23권7호
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• pp.99-104
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• 2018

In this paper, we compare Fisher's continuous method with an exact discrete analog of Fisher's continuous method from permutation tests for combining p values. The discrete analog of Fisher's continuous method is known to be adequate for combining independent p values from discrete probability distributions. Also permutation tests are widely used as alternatives to conventional parametric tests since these tests are distribution-free, and yield discrete probability distributions and exact p values. In this paper, we obtain permutation p values from discrete probability distributions using Mann-Whitney test data sets (real data and hypothetical data) and combine p values by the exact discrete analog of Fisher's continuous method.

• Journal of Information Technology Applications and Management
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• 제9권2호
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.35-42
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• 2017

The TDD LTE MU-MIMO HARQ system is designed and implemented using GPU based on 3GPP Rel.10 standard. The system consists of the DU part of the base station and the terminal using the general computer based on the GeForce GTX TITAN graphics card provided by NIVIDIA, and constructed the part of the RU using USRP N210 provided by Ettus. In the implementation part, SDR standard is applied, so that various communication standards can be compatible with software. The retransmission is implemented by combining the previous data with the retransmission data using Chase Combining among HARQ methods. In order to confirm that the retransmission was successful, the performance evaluation used LLR constellation. First, if there is an error in the data, the LLR value is not distributed at the corresponding position. in this case, a retransmission is performed to chase combine the previously stored error data and retransmitted data. As a result, the LLR value was distributed at the position of the corresponding LLR value per bit. Through this, it can be confirmed that error - free data is formed by using Chase Combining after retransmission.

• 응용통계연구
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• 제25권5호
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• pp.821-827
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• 2012

In this study, we propose a new nonparametric test procedure for the multivariate data. In order to accommodate the generalized alternatives for the multivariate case, we construct test statistics via-values with some useful combining functions. Then we illustrate our procedure with an example and compare efficiency among the combining functions through a simulation study. Finally we discuss some interesting features related with the new nonparametric test as concluding remarks.

• 한국컴퓨터정보학회논문지
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• 제25권7호
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• pp.175-182
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• 2020

연속형 분포로부터 얻은 독립적인 p값들을 통합하는 Fisher의 고전적인 방법은 널리 사용되고 있지만 이산형 확률분포로부터 얻은 p값들을 통합하기에는 적절하지 않다. 대신에 유사 Fisher의 통합방법이 이산형 확률분포의 p값들을 통합하는 대안으로 사용된다. 본 논문에서는 첫째, 여러 표본들의 위치검정(Fisher-Pitman 검정과 Kruskal-Wallis 검정) 데이터와 관련된 이산형 확률분포로 부터 퍼뮤테이션 방법에 의해 p값들을 구하고, 둘째로 이 p값들을 유사 Fisher의 통합방법을 이용하여 통합한다. 그리고 Fisher의 고전적인 방법과 유사 Fisher의 통합방법의 결과를 비교한다.

• 물과 미래
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• 제25권1호
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• pp.93-100
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• 1992

Abstract A study on the Derivation of the Unit Hydrograph using Multiple Regression Moe이. The purpose of this study is to deriver an optimal unit hydrograph suing the multiple regression model, particularly when only small amount of data is available. The presence of multicollinearity among the input data can cause serious oscillations in the derivation of the unit hydrograph. In this case, the oscillations in the unit hydrograph ordinate are eliminated by combining the data. The data used in this study are based upon the collection and arrangement of rainfall-runoff data(1977-1989) at the Soyang-river Dam site. When the matrix X is the rainfall series, the condition number and the reciprocal of the minimum eigenvalue of XTX are calculated by the Jacobi an method, and are compared with the oscillation in the unit hydrograph. The optimal unit hydrograph is derived by combining the numerous rainfall-runoff data. The conclusions are as follows; 1)The oscillations in the derived unit hydrograph are reduced by combining the data from each flood event. 2) The reciprocals of the minimum eigen\value of XTX, 1/k and the condition number CN are increased when the oscillations are active in the derived unit hydrograph. 3)The parameter estimates are validated by extending the model to the Soyang river Dam site with elimination of the autocorrelation in the disturbances. Finally, this paper illustrates the application of the multiple regression model to drive an optimal unit hydrograph dealing with the multicollinearity and the autocorrelation which cause some problems.

• 대한산업공학회지
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• 제27권1호
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권7호
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• pp.1293-1310
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• 2011

A simple modification of well known Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is proposed to exploit cooperative diversity. Instead of selecting a single cluster-head, we propose M cluster-heads in each cluster to obtain a diversity of order M. The cluster-heads gather data from all the sensor nodes within the cluster using same technique as LEACH. Cluster-heads transmit gathered data cooperatively towards the destination or higher order cluster-head. We propose a code combining based cooperative diversity protocol which is similar to coded cooperation that maximizes the performance of the proposed cooperative LEACH protocol. The implementation of the proposed cooperative strategy is analyzed. We develop the upper bounds on bit error rate (BER) and frame error rate (FER) for our proposal. Space time block codes (STBC) are also a suitable candidate for our proposal. In this paper, we argue that the STBC performs worse than the code combining cooperation.