• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5382-5403
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• 2018

In this paper, for a spectrum sensing purpose, we heuristically established a test statistic (TS) from a sample covariance matrix (SCM) for multiple antennas based cognitive radio. The TS is formulated as a scaled-energy which is calculated as a sum of scaled diagonal entries of a SCM; each of the diagonal entries of a SCM scaled by corresponding row's Euclidean norm. On the top of that, by combining theoretical results together with simulation observations, we have approximated a decision threshold of the TS which does not need prior knowledge of noise power and primary user signal. Furthermore, simulation results - which are obtained in a fading environment and in a spatially correlating channel model - show that the proposed method stands effect of noise power mismatch (non-uniform noise power) and has significant performance improvement compared with state-of-the-art test statistics.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1181-1191
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• 2023

This paper investigates Young inequalities for matrices, a problem closely linked to operator theory, mathematical physics, and the arithmetic-geometric mean inequality. By obtaining new inequalities for unitarily invariant norms, we aim to derive a fresh Young inequality specifically designed for matrices.To lay the foundation for our study, we provide an overview of basic notation related to matrices. Additionally, we review previous advancements made by researchers in the field, focusing on Young improvements.Building upon this existing knowledge, we present several new enhancements of the classical Young inequality for nonnegative real numbers. Furthermore, we establish a matrix version of these improvements, tailored to the specific characteristics of matrices. Through our research, we contribute to a deeper understanding of Young inequalities in the context of matrices.

• Journal of Information Management
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• v.39 no.3
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• pp.95-113
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• 2008

In todays environment in which scientific technologies are changing very fast than ever, companies have to monitor and search emerging technologies to gain competitiveness. Actually many nations try to do that. Most of them use Dephi approach based on experts review as a searching method. But experts review has been criticised for probability of inclination and its derivative problems in the sense that it is accomplished only by expert's subjectivity. To overcome such problems, we used Scientometric Method for identifying emerging technology that had been done by Delphi as a rule. We made three particular efforts in order to improve the Quality of the result. Firstly, we selected one alternative database between SCI and Scopus hoping to see evenly-distributing results in wide fields on the front burner. Secondly we used Fractional citation counting in counting citation number in the stage of linear regression analysis. Lastly, we verified Scientometric result with experts opinions to minimize probable errors in a Scientometric research. As a result, we derived 290 emerging technologies from Scientometric analysis with Scopus Database, and visualized them on 2-dimension map with data mining system named KnowledgeMatrix which was developed by KISTI.

• Journal of the Korean Society for information Management
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• v.6 no.2
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• pp.21-44
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• 1989

This study investigates author cocitation and author transition analysis which are two techniques to be used to construct author and concept networks by using a test collection of 4, 598 documents on the subject of chemistry. The author and concept networks can be used to understand the structual Knowledge of terms as well as intellectual structure of science. So, these networks could be basic data of knowledge base for subject literature.

• International Journal of Oral Biology
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• v.44 no.4
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• pp.125-129
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• 2019

Osteocalcin is the most abundant non-collagenous protein produced in bone. It has traditionally been regarded as a marker of bone turnover and is thought to act in the bone matrix to regulate mineralization. However, emerging knowledge regarding osteocalcin has expanded to include functions in energy metabolism, fertilization, and regulation of cognition. Fully carboxylated osteocalcin binds to hydroxyapatite, thereby modulating bone turnover, whereas undercarboxylated osteocalcin in the circulation binds to osteocalcin-sensing receptors and acts as a hormone that affects multiple physiological aspects. In this review, we summarize the current knowledge regarding the hormonal actions of osteocalcin in various organs and potential cellular downstream signaling pathway that may be involved.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.251-254
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• 2003

데이터는 어떤 특성을 나타내는 언어적 또는 수치적 값들의 표현이다. 이러한 데이터들을 목적에 따라 구성한 것이 정보이며, 문제 해결이나 패턴 분류, 또는 의사 결정을 위해 정보들간의 관계를 규칙으로 체계화하는 것이 지식이다. 현재 대부분의 산업 분야에서 시스템에 대한 이해를 높이고 시스템의 성능을 향상시키기 위해 지식을 추출하고, 적용시키는 작업들이 활발히 이루어지고 있다. 지식 정보의 추출은 지식의 획득, 표현, 구현의 단계로 구성되며 이렇게 추출된 지식 정보는 규칙으로 도출된다. 본 논문에서는 여러 산업 분야에 걸쳐 다양하게 적용되는 지식 정보 추출 방법들에 대해 그 영역별로 알아보고 여러 시험 데이터들과 실제 시스템에 클러스터링(CL), 입력공간 분할(ISP), 뉴로-퍼지(NF), 신경망(NN), 확장 행렬(EM) 등의 방법들을 적용시킨 결과들을 비교 분석하고자 한다.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.637-644
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• 1997

Sometimes, the first principal component may come logically from the established knowledge and premises. For example, for the high school students' test scores of Korean, English, Mathematics, Social Study, and Science, it is natural to define the first principal component as the average of all subject scores. In such cases, we need to respect both the background knowledge and the data exploration. The aim of this study is to find the remaining components in principal component analysis of multivariate data when the first principal component is defined a priori by the researcher. Moreover, we study related matrix decomposition and their application to the graphical display.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Journal of Sasang Constitutional Medicine
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• v.13 no.1
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• pp.17-23
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• 2001

Purpose This study was carried out ro apply the knowledge of bioinformatics to the quantification of constitutional information. Methods To objectify consitirutional knowledge, several uselful methods including Bayesian estimate, position specific score matrix, entropy, phylogenetic tree, simulated annealing were discussed. Results and Conclusion It is obvious that bioinformatic methods can be the most important tool for the objectification of constitutional medicine.