• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.11
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• pp.955-961
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• 1990

Test statistics are obtained for detection of weak signals in signal-dependent noise using rank statistics. A generalized model is used in this paper in order to consider non-additivenoise as well as purely-additive noise. Locally optimum rank detectors for the model are shown to have similarity to locally optimum detectors and to be generalizations of these for the purely-additive noise model. A similar result is obtained for multi-input cases.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.159-168
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• 1999

We introduce a new conditional signed-rank test for the tree alternatives comparing several treatments with a control in the randomized block design. We demonstrate its performance by comparing with 3 classes of signed-rank tests proposed by Park et al.(1991) in some general situations. In most cases the proposed procedure is simpler to compute and has better power than others.

• Journal of Korean Society for Quality Management
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• v.19 no.2
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• pp.52-62
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• 1991

Consider a class of rank tests for comparing several treatments with a control and discuss some members among the class. New rank test based on orthogonal contrasts is proposed and compared with other well known tests. The approximate powers of the proposed test are also presented through the simulation studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1317-1322
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• 2021

Nonlocal similarity of natural images leads to the fact that a patch matrix whose columns are similar patches of the reference patch has a low rank. Images corrupted by additive white Gaussian noises (AWGN) make their patch matrices to have a higher rank. The noise in the image can be reduced by obtaining low rank approximation of the patch matrices. In this paper, an image denoising algorithm is proposed, which first constructs the patch matrices by combining the similar patches of each reference patch, which is a part of the noisy image. For each patch matrix, the proposed algorithm calculates its low rank approximate, and then recovers the original image by aggregating the low rank estimates. The simulation results using widely accepted test images show that the proposed denoising algorithm outperforms four recent methods.

• Journal of Korean Library and Information Science Society
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• v.25
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• pp.339-375
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• 1996

This study is to identified the motivation level which university librarians in Korea perceived as motivating them, and to examine relationship between those motivation level and the demographic characteristics of university librarians. The results of this study are summarized as follows : (1) The factors that the motivation level is higher than the average (3.000) are shown to be human relation (3.399), working itself (3.198), and supervision (3.050), but the other factors : recognition (2.995), communication (2.985), salary (2.892), job evaluation (2.622), job environment (2.619), and promotion (2.126) are shown to be lower than the average. (2) In the results of X$^{2}$ test about the differences of the motivation level among 6 demographic attributes of the population, the attributes shown a significant difference are as follows : Salary : rank ; working terms ; sex ; national university or private one ; qualification. Promotion : rank ; sex ; national university or private one ; qualification. Recognition : rank. Human relation : no significant differences among 6 attributes. Work itself : rank ; sex. Communication : rank ; working terms ; sex ; education background ; qualification. Job evaluation : sex ; education background ; qualification. Job environment : sex ; national university or private one. Supervision : rank ; working terms ; sex ; education background ; qualification.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.111-120
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• 2006

In a two-stage two-level balanced nested design, type I error rates for the parametric tests and the rank transformed tests for the main effects and the nested effects are in overall similar to each other. Furthermore, powers for the rank transformed statistic for the main effects and the nested effects in a two-stage two-level balanced nested design are generally superior to powers for the parametric statistic When the effect size and the sample size are increased, we can find that powers increase for the parametric statistic and the rank transformed statistic are dramatically improved. Especially for the case of the fixed effects in the asymmetric distributions such as an exponential distribution, powers for the rank transformed tests are quite high rather than powers for the parametric tests.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.1
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• pp.27-35
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• 2000

For a weighting method to be practically valid, it should produce weights which coincide with the relative importance of attributes perceived by the decision maker. In this paper, 'bootstrapping' is used to compare the practical validities of five weighting methods frequently used; the rank order centroid method, the rank reciprocal method, the rank sum method, the entropic method, and the geometric mean method. Bootstrapping refers to the procedure where the analysts allow the decision maker to make careful judgements on a series of similar cases, then infer statistically what weights he was implicitly using to arrive at the particular ranking. The weights produced by bootstrapping can therefore be regarded as well reflecting the decision maker's perceived relative importances. Bootstrapping and the five weighting methods were applied to a job selection problem. The results showed that both the rank order centroid method and the rank reciprocal method had higher level of practical validity than the other three methods, though a large difference could not be found either in the resulting weights or in the corresponding solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5888-5903
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• 2019

The Relational ranking method applies authority-based ranking in relational dataset that can be modeled as graphs considering also their tuples' values. Authority directions from tuples that contain the given keywords and transfer to their corresponding neighboring nodes in accordance with their values and semantic connections. From our previous work, ObjectRank extends to ValueRank that also takes into account the value of tuples in authority transfer flows. In a maked difference from ObjectRank, which only considers authority flows through relationships, it is only valid in the bibliographic databases e.g. DBLP dataset, ValueRank facilitates the estimation of importance for any databases, e.g. trading databases, etc. A relational keyword search paradigm Object Summary (denote as OS) is proposed recently, given a set of keywords, a group of Object Summaries as its query result. An OS is a multilevel-tree data structure, in which node (namely the tuple with keywords) is OS's root node, and the surrounding nodes are the summary of all data on the graph. But, some of these trees have a very large in total number of tuples, size-l OSs are the OS snippets, have also been investigated using ValueRank.We evaluated the real bibliographical dataset and Microsoft business databases to verify of our proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12A
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• pp.1296-1302
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• 2007

In this paper, we propose joint estimation methods of carrier offset and channel estimation for MB-OFDM system with low complexity. The proposed methods estimate the channel by using low-rank LMMSE channel estimation which reduces the system complexity by applying the optimal number of rank to evaluate the frequency offset and additionally using the simple algorithm using the auto-correlation property of the estimated channel. We simulate the proposed algorithms under the IEEE 802.15 TG3a UWB channel model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1007-1017
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• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.