• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.64-66
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• 2002

공간-시간 부호(Space-Time Code)는 다중 안테나 시스템에서 기존의 기술에 비해서 부가적인 대역폭이 필요 없이 부호화 이득을 얻을 수 있다. 지금까지 공간-시간 부호(Space-Time Code)는 다이버시티 이득의 관점에서는 신호행렬들의 차가 완전-계수(Full-Rank)를 가져야 하고, 코딩 이득의 관점에서는 신호행렬들의 차의 determinant 값이 최소값을 가져야 한다. 본 논문에서는 공간-시간 블록 부호 디자인(Space-Time Block Code) 관점에서 직교-디자인(Orthogonal-design) 즉, 최소거리가 5이면서 완전-계수(Full-Rank)인 디자인을 비교대상으로 완전-계수(Full-Rank)가 아니면서 최소거리가 5와7인두 부호에 관하여 연구되어졌다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.656-661
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• 2013

In Guided Scrambling (GS) encoding for the holographic storage, after scrambling augmented source word into codeword, the best codeword satisfying modulation constraint is determined. Modulation constraints considered in this paper are strength which is the minimum number of transition between '0' and '1' in each row and column of codeword array and the symbol balancedness of codeword array. In this paper, we show that GS encoding procedure can be formulated as an integer programming model and develop a fast neighborhood search heuristic for fast computation of control bits. In the simulation, we compared the performance of heuristic algorithm with the integer programming model for various array and control bit size combinations.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.508-513
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• 2010

본 논문에서는 정칙 이진 행렬을 이용하여 유전 알고리즘의 성능을 개선할 수 있는 메타 유전 알고리즘을 설계한다. 정칙 이진 행렬은 유전 알고리즘에서 사용되는 이진 인코딩에서의 기저 변환에 중요하게 쓰일 수 있다. 이 논문에서는 정칙 이진 행렬의 기저 변화를 위한 아이디어와 더불어 정칙 이진 행렬의 표현과 재조합 연산에 대한 아이디어를 제시했던 연구들을 소개하고, 메타 유전 알고리즘을 위한 변이와 초기 해집단 생성, 평가에 대한 방법론을 제시한다.

• Journal of Platform Technology
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• v.11 no.5
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• pp.23-36
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• 2023

This study aims to analyze the similarities and dissimilarities of various versions of <Heungbu-jeon> at both micro- and macro-levels using contents analysis techniques and the Hamming distance metrics. The 28 versions of <Heungbu-jeon> were segmented into 341 content units, and for each unit, the value of the content type was encoded. The dissimilarities between content types were compared among all versions by the content unit, respectively. The (dis-)similarities based on the content types of the 28 versions were aggregated and transformed into a distance matrix. The matrix was interpreted by multi-dimensional scaling, resulting into the two-dimensional coordinates. By visualizing the results by multi-dimensional scaling analysis, it was confirmed that the versions of <Heungbu-jeon> can be broadly divided into two groups. Hierarchical clustering and phylogenetic analysis were applied to analyze the clusters of the 28 versions, using the same distance matrix. The results showed that there are five clusters based on the micro-level analysis of (dis-)similarities within two major clusters. This study demonstrated the usefulness of applying digital humanities methods to encode the content of classical literary versions and analyze the data using clustering analysis techniques based on the (dis-)similarity of literary content.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.619-627
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• 2015

This paper presents a speech enhancement method using non-negative matrix factorization (NMF). In the training phase, each basis matrix of source signal is obtained from a proper database, and these basis matrices are utilized for the source separation. In this case, the performance of speech enhancement relies heavily on the basis matrix. The proposed method for which speech basis matrix is made a high reconstruction error for noise signal shows a better performance than the standard NMF which basis matrix is trained independently. For comparison, we propose another method, and evaluate one of previous method. In the experiment result, the performance is evaluated by perceptual evaluation speech quality and signal to distortion ratio, and the proposed method outperformed the other methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.15-24
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• 2015

Jacket matrices which are defined to be $m{\times}m$ matrices $J^{\dagger}=[J_{ik}^{-1}]^T$ over a Galois field F with the property $JJ^{\dagger}=mI_m$, $J^{\dagger}$ is the transpose matrix of element-wise inverse of J, i.e., $J^{\dagger}=[J_{ik}^{-1}]^T$, were introduced by Lee in 1984 and are used for Digital Signal Processing and Coding theory. This paper presents some square matrices $A_2$ which can be eigenvalue decomposed by Jacket matrices. Specially, $A_2$ and its extension $A_3$ can be used for modifying the properties of hyperbola and hyperboloid, respectively. Specially, when the hyperbola has n times transformation, the final matrices $A_2^n$ can be easily calculated by employing the EVD[7] of matrices $A_2$. The ideas that we will develop here have applications in computer graphics and used in many important numerical algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.373-376
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• 2011

얼굴 영상에서 구성요소(눈썹, 눈, 코, 입 등)의 존재에 따라 보는 사람의 얼굴 인식 정확도는 큰 영향을 받는다. 이는 인간의 뇌에서 얼굴 정보를 처리하는 과정은 얼굴 전체 영역 뿐만 아니라, 부분적인 얼굴 구성요소의 특징들도 고려함을 말한다. 비음수 행렬 분해(NMF: Non-negative Matrix Factorization)는 이러한 얼굴 영역에서 부분적인 특징들을 잘 표현하는 기저영상들을 찾아내는데 효과적임을 보여주었으나, 각 기저영상들의 중요도는 알 수 없었다. 본 논문에서는 NMF로 찾아진 기저영상들에 대응되는 인코딩 정보를 SLR(Sparse Logistic Regression)을 이용하여 성별 인식에 중요한 부분 영역들을 찾고자 한다. 실험에서는 주성분분석(PCA)과 비교를 통해 NMF를 이용한 기저영상 및 특징 벡터 추출이 좋은 성능을 보여주고, 대표적 이진 분류 알고리즘인 SVM(Support Vector Machine)과 비교를 통해 SLR을 이용한 특징 벡터 선택이 나은 성능을 보여줌을 확인하였다. 또한 SLR로 확인된 각 기저영상에 대한 가중치를 통하여 인식 과정에서 중요한 얼굴 영역들을 확인할 수 있다.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.293-296
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• 1999

본 논문에서는 위상 다중화 홀로그래픽 메모리 시스템에서 사용될 최적의 위상코드를 구현하기 위해 기존에 위상 다중화에 많이 사용되고 있는 Hadamard 행렬을 비롯한 여러 행태의 랜덤 위상코드들의 상호상관 값에 의한 Cross talk의 영향 및 신호대 잡음비, 그리고 어드레스 갯수를 비교 분석하였다.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.433-439
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• 2016

In this paper, we propose a optimized steganography how to improve the image degradation of the existing data hiding techniques. This method operates in the compressed domain(JPEG) of an image. Most of the current information concealment methods generally change the coefficients to hide information. And several methods have tried to improve the performance of a typical steganography method such as F5 including a matrix encoding. Those papers achieved the object of reducing the distortion which is generated as hiding data in coefficients of compressed domain. In the proposed paper we analyzed the effect of the quantization table for hiding the data in the compressed domain. As a result, it found that can decrease the distortion that occur in the application of steganography techniques. This paper provides a little (Maximum: approximately 6.5%) further improved results in terms of image quality in a data hiding on compressed domain. Developed algorithm help improve the data hiding performance of compressed image other than the JPEG.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.