• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.595-601
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• 2003

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

• Proceedings of the KIEE Conference
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.144-146
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• 2006

The Data Matrix of two-dimensional bar codes is a new technology capable of holding relatively large amounts of data compared to the conventional one-dimensional bar code which is just a key that can access detailed information to the host computer database. A secret key is used to prevent a watermark from malicious attacks. We encoded copyright information into a Data Matrix bar code for encoding process and it was spread a pseudo random pattern using owner key. We embedded a randomized watermark into the image using watermark's embedding position, pattern generated with a secret key. The experimental results have shown that the proposed scheme has good quality and is very robust to various attacks, such as JPEG compression and noise. Also the performance of the proposed scheme is verified by comparing the copyright information with the information which is extracted from a bar code scantier.

• Journal of Communications and Networks
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• 제12권3호
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• pp.240-245
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• 2010

Jacket matrices, motivated by the complex Hadamard matrix, have played important roles in signal processing, communications, image compression, cryptography, etc. In this paper, we suggest a novel approach to design a simple class of space-time block codes (STBCs) to reduce its peak-to-average power ratio. The proposed code provides coding gain due to the characteristics of the complex Hadamard matrix, which is a special case of Jacket matrices. Also, it can achieve full rate and full diversity with the simple decoding. Simulations show the good performance of the proposed codes in terms of symbol error rate. For generality, a kind of quasi-orthogonal STBC may be similarly designed with the improved performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Information Systems Review
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• 제2권2호
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• pp.193-199
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• 2000

• Journal of the Korea Institute of Information and Communication Engineering
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• 제13권12호
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• pp.2731-2736
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• 2009

In this paper, a new implementation method of OFDM (orthogonal frequency division multiplexing) system with an orthogonal basis matrix is developed mathematically. The basis matrix is based on the Haar basis but has an appropriate form for modulation of multiple subchannel signals of OFDM. It is verified that the new basis matrix can be expanded with a simple recursive algorithm. The order of synthesis matrix in the transmitter is increased by the factor of two with every expansion. Demodulation in the receiver is carried out by its inverse matrix, which can be generated recursively with the orthogonal basis matrix. It is shown that perfect reconstruction of original signals is possibly achieved in the proposed OFDMsystem.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제13권12호
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• pp.2603-2608
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• 2009

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the clustering method based NMF(non-negative matrix factorization) and refinement of documents in cluster by using coherence of cluster. The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4603-4624
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• 2016

In this paper, a new reversible watermarking algorithm with adaptive embedding threshold matrix is proposed. Firstly, to avoid the overflow and underflow, two flexible thresholds, TL and TR, are applied to preprocess the image histogram with least histogram shift cost. Secondly, for achieving an optimal or near optimal tradeoff between the embedding capacity and imperceptibility, the embedding threshold matrix, composed of the embedding thresholds of all blocks, is determined adaptively by the combination between the composite chaos and the average energy of Integer Wavelet Transform (IWT) block. As a non-liner system with good randomness, the composite chaos is suitable to search the optimal embedding thresholds. Meanwhile, the average energy of IWT block is calculated to adjust the block embedding capacity, and more data are embedded into those IWT blocks with larger average energy. The experimental results demonstrate that compared with the state-of-the-art reversible watermarking schemes, the proposed scheme has better performance for the tradeoff between the embedding capacity and imperceptibility.

• Journal of Information Technology Applications and Management
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• 제9권2호
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• pp.57-70
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• 2002

In this paper, we propose matrix analysis methods for analyzing national R&D information creation. In order to analyze R&D information creation at national level, it is necessary to analyze whether information is created systematically for each technical category and for each information type. In this paper, ‘uniformity’and ‘concentration’criterions are proposed to check national R&D information creation and we provide formulas to measure the criterions. The criterions are applied to domestic information creation in information and communication domain to show the utilization of the proposed method.