• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.803-809
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• 2011

Properties of multivariate Shewhart and CUSUM charts for monitoring variance-covariance matrix, specially focused on correlation coefficient components, are investigated. The performances of the proposed charts based on control statistic Lawley-Hotelling $V_i$ and likelihood ratio test (LRT) statistic $TV_i$ are evaluated in terms of average run length (ARL). For monitoring correlation coe cient components of dispersion matrix, we found that CUSUM chart based on $TV_i$ gives relatively better performances and is more preferable, and the charts based on $V_i$ perform badly and are not recommended.

• ETRI Journal
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• v.40 no.3
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• pp.303-308
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• 2018

This paper proposes a simple yet accurate method for estimating the antenna correlation coefficient (ACC) of a high-order multiple-input multiple-output (MIMO) antenna. The conventional method employed to obtain the ACC from three-dimensional radiation patterns is costly and difficult to measure. An alternate method is to use the S-parameters, which can be easily measured using a network analyzer. However, this method assumes that the antennas are highly efficient, and it is therefore not suitable for lossy MIMO antenna arrays. To overcome this limitation, we define and utilize the non-coupled radiation efficiency in the S-parameter-based ACC formula. The accuracy of the proposed method is verified by the simulation results of a 4-port highly coupled lossy MIMO array. Further, the proposed method can be applied to N-port arrays by expanding the calculation matrix.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.80-88
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• 2005

This research describes an interactive process of analysing the demand factors for apartment on Cheonan area Using subjective statistical data for demand factor the process are categorized into main factors explained for the sensitiveness of correlation coefficient. This investigation is based on an analysis of the work of time series data One of the propose of this research is determining the correlation factors that can be effectively used in the model of forcasting. The results show a significant correlation coefficient on correlation matrix to iud the optimum correlation factors. The paper thus shows how to gain greater influntial factors on principal component analysis Consequently, this paper provides useful information about correlationship, but has limit of regional boundary for effectiveness.

• Speech Sciences
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• v.10 no.2
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• pp.249-258
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• 2003

This study attempted to examine a speaker identification method using difference sum and correlation coefficient determined from a pair of intensity level matrices of band-pass-filtered numeric sounds produced by ten female speakers of similar age and height. Subjects recorded three digit numbers at a quiet room at a sampling rate of 22 kHz on a personal computer. Collected data were band-pass-filtered at five different band ranges. Then, matrices of five intensity levels at 100 proportional time points were obtained. Pearson correlation coefficients and the sum of absolute intensity differences between a pair of given matrices were determined within and across the speakers. Results showed that very high correlation coefficient and small difference sum generally occurred within each speaker but some individual variation was also observed. Thus, the matrix pair with a higher coefficient and a smaller difference sum was averaged to form each individual's model. Comparison among the speakers yielded generally low coefficients and large differences, which suggests successful speaker identification, but among them there were a few cases with very high coefficients and small differences. Future studies will focus on finer band ranges and additional spectral parameters at some peak points of the intensity contour at a low frequency band.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.

• The Journal of Bigdata
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• v.2 no.1
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• pp.41-48
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• 2017

Recently, the popularity of the recommendation system and the evaluation of the performance of the algorithm of the recommendation system have become important. In this study, we used modeling and RMSE to verify the effectiveness of various algorithms in movie data. The data of this study is based on user-based collaborative filtering using Pearson correlation coefficient, item-based collaborative filtering using cosine correlation coefficient, and item-based collaborative filtering model using singular value decomposition. As a result of evaluating the scores with three recommendation models, we found that item-based collaborative filtering accuracy is much higher than user-based collaborative filtering, and it is found that matrix recommendation is better when using matrix decomposition.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.5
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• pp.375-382
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• 2018

To eliminate strong jamming signals, a radar acquires a relatively weak target signal by using a side-lobe canceller (SLC) algorithm. This paper presents a novel adaptive SLC algorithm that is applicable to a fully digital active array radar. First, a covariance matrix is obtained from the SLC beam. Then, an adaptive SLC coefficient is extracted after calculating the correlation matrix between the main beam signal and the SLC beam signal. Finally, the target signal is estimated and the jamming signal is removed through the operation with the main beam signal. The application results from simulated radar signals demonstrated that the proposed algorithm is effective in an SLC system. Moreover, we analyzed various considerations and improved systematic usability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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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.

• Tunnel and Underground Space
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• v.18 no.6
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• pp.408-417
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• 2008

Rock mass classification methods such as RMR and Q system have different characteristics each other in parameters considered and applications, and so it is very important to prescribe the relationship between parameters for the analysis of correlativity of these methods. With the Held data of RMR and Q estimation in road construction sites, the acquaintance relations between RMR and Q of rock mass classifications are analyzed. The correlation equations between parameters of RMR and Q, matrix of correlation coefficients and the generalized form of acquaintance relation matrix are derived. This acquaintance relation matrix can be further extended to the form of generalized acquaintance relation network, and could be used to analyze the correlativity and to enhance the utility of common rock mass classification methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.123-146
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• 1995

Several quality control schemes have been extensively compared using multivariate normal data sets simulated with various correlation structures. They include multiple univariate CUSUM charts, multivariate EWMA charts, multivariate CUSUM charts and Shewhart T$^{3}$ chart. This paper considers a new approach of the multivariate EWMA chart, in which the smoothing matrix has full elements instead of only diagonal elements. Performance of the schemes is measured by avaerage run length (ARL), coefficient of variation of run length (CVRL) and rank in order of signaling of off-target shifts in the process mean vector. The schemes are also compared by noncentrality parameter. The multiple univariate CUSUM charts are generally affected by the correlation structure. The multivariate EWMA charts provide better ARL performance. Especially, the new EWMA chart shows remarkable results in small shifts.