• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Wind and Structures
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• 제9권3호
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• pp.231-243
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• 2006

An improvement to the spectral representation algorithm for the simulation of wind velocity fields on large scale structures is proposed in this paper. The method proposed by Deodatis (1996) serves as the basis of the improved algorithm. Firstly, an interpolation approximation is introduced to simplify the computation of the lower triangular matrix with the Cholesky decomposition of the cross-spectral density (CSD) matrix, since each element of the triangular matrix varies continuously with the wind spectra frequency. Fast Fourier Transform (FFT) technique is used to further enhance the efficiency of computation. Secondly, as an alternative spectral representation, the vectors of the triangular matrix in the Deodatis formula are replaced using an appropriate number of eigenvectors with the spectral decomposition of the CSD matrix. Lastly, a turbulent wind velocity field through a vertical plane on a long-span bridge (span-wise) is simulated to illustrate the proposed schemes. It is noted that the proposed schemes require less computer memory and are more efficiently simulated than that obtained using the existing traditional method. Furthermore, the reliability of the interpolation approximation in the simulation of wind velocity field is confirmed.

• 응용통계연구
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• 제25권6호
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.1-7
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• 2019

This paper presents CTP (Compact Translation Page), a compact representation of translation pages, for page mapping-based flash translation layers to improve RAM utilization and reduce the response time of solid state drives. CTP can store translation information twice in a translation page and the total number of translation pages stored in flash is reduced to half. Therefore, CTP halves the RAM size of the directory of translation pages and uses the saved RAM space for translation cache. CTP shows the best response time when compared to existing page mapping-based flash translation layers.

• 대한지리학회지
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• 제41권5호
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• pp.545-560
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• 2006

본 연구의 목적은 16세기에서 18세기에 이르기까지의 프랑스의 국가 형성과 지도학 발달의 관계를 고찰하는 것이다. 이를 위하여 먼저 영토의 방어와 확장을 목적으로 하는 군사지도의 발달을 살펴보았다. 둘째, 식민지 확장의 수단으로 사용되는 해외 지도의 발달과정을 고찰하였다. 세 번째로 국가 표상으로서의 국가 기본 지도의 발달과정을 살펴보았다. 네 번째로, 국민의식 함양을 목적으로 한 아틀라스의 발달과정을 고찰하였으며, 마지막으로 지도가 프랑스의 국가형성에 미친 영향에 대해 종합적으로 논의하였다.

• 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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• 제16권3호
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• pp.813-829
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• 2022

In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.

• 대한수학회지
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• 제16권2호
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• pp.107-111
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• 1980

• 한국생활과학회지
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• 제13권4호
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• pp.521-535
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• 2004

The purpose of this study is to investigate the formation of parental beliefs. For this study, 154 unmarried university students were asked to fill in a packet of survey questionnaire about parental beliefs, the internal representation of childhood, and communication with their parents. Data collected were analyzed using correlation and multiple regression by SPSS PC program. It was reported that the internal representation of parenting formed in childhood influences the development of rational authoritative beliefs, distancing beliefs, and direct authoritative beliefs. Also, the open communication with parents affected the distancing beliefs and rational authoritative beliefs.