• Journal of KIISE:Computer Systems and Theory
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• v.31 no.3_4
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• pp.187-194
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• 2004

Shear-Warp volume rendering has many advantages such as good image Quality and fast rendering speed. However in the interactive classification environment it has low efficiency of memory access since preprocessed classification is unavailable. In this paper we present an algorithm using the spacial locality of memory access in the interactive classification environment. We propose an extension model appending a rotation matrix to the factorization of viewing transformation, it thus performs a scanline-based rendering in the object and image space. We also show causes and solutions of three problems of the proposed algorithm such as inaccurate front-to-back composition, existence of hole, increasing computational cost. This model is efficient due to the spacial locality of memory access.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.3
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• pp.523-529
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• 2020

Foot drop is a common symptom in stroke patients due to central nervous system (CNS) damage, which causes walking disturbances. Functional electrical stimulation (FES) is an effective rehabilitation method for stroke patients with CNS damage. Aim of this study was to determine the effectiveness of 6 weeks FES walking training based lower limb muscle synergy of stroke patients. Lower limb muscle synergies were extracted from electromyography (EMG) using a non-negative matrix factorization algorithm (NMF) method. Cosine similarity and cross correlation were calculated for similarity comparison with healthy subjects. In both stroke patients, the similarity of leg muscle synergy during walking changed to similar to that of healthy subjects due to a decrease in foot drop during. FES walking intervention influenced the similarity of muscle synergies during walking of stroke patients. This intervention has an effective method on foot drop and improving the gait performance of stroke patients.

• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.127-138
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• 2018

This study measured indoor and outdoor levels of hydrocarbon volatile organic compounds (VOCs), such as benzene, toluene, ethylbenzene, and xylene isomers (collectively referred to as BTEX), as well as 13 carbonyl compounds, at 20 homes in Seoul, South Korea. Along with the sampling of BTEX and carbonyls, indoor concentrations of the air pollutants nitrogen oxide (NO) and carbon dioxide ($CO_2$) were also measured at each home. These measurements were used to understand the characteristics of BTEX and carbonyls by calculating the various ratios and correlation coefficients between measured contaminant levels. We found that carbonyls were mostly originated from indoor sources, while BTEX were originated from both indoor and outdoor sources. A high correlation between indoor levels of NO and BTEX indicated that traffic emissions were also an important sources of BTEX.

• Bulletin of the Korean Mathematical Society
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• v.46 no.2
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• pp.331-346
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• 2009

Let ${\alpha}$ = (${\alpha}_1,\;{\alpha}_2$,...) and ${\beta}$ = (${\beta}_1,\;{\beta}_2$,...) be two sequences with ${\alpha}_1$ = ${\beta}_1$ and k and n be natural numbers. We denote by $A^{(k,{\pm})}_{{\alpha},{\beta}}(n)$ the matrix of order n with coefficients ${\alpha}_{i,j}$ by setting ${\alpha}_{1,i}$ = ${\alpha}_i,\;{\alpha}_{i,1}$ = ${\beta}_i$ for 1 ${\leq}$ i ${\leq}$ n and $${\alpha}_{i,j}=\{{\alpha}_{i-1,j-1}+{\alpha}_{i-1,j}\;if\;j{\equiv}$$2,3,4,..., k + 1 (mod 2k) $$\{{\alpha}_{i-1,j-1}-{\alpha}_{i-1,j}\;if\;j{\equiv}$$ k + 2,..., 2k + 1 (mod 2k) for 2 ${\leq}$ i, j ${\leq}$ n. The aim of this paper is to study the determinants of such matrices related to certain sequence ${\alpha}$ and ${\beta}$ and some natural numbers k.

• Structural Engineering and Mechanics
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• v.23 no.6
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• pp.675-689
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• 2006

In structural analysis of tall buildings the traditional primary loading analysis approach that assumes all the loads are simultaneously applied to the fully built structure has been shown to be unsuitable by many researches. The construction sequential analysis that reflects the fact of the level-by-level construction of tall buildings can provide more reliable results and has been used more and more. However, too much computational cost has prevented the construction sequential analysis from its application in CAD/CAE software for building structures, since such an approach needs to deal with systematic changing of resultant stiffness matrices following level-by-level construction. This paper firstly analyzes the characteristics of assembling and triangular factorization of the stiffness matrix in the finite element model of the construction sequential analysis, then presents a fast construction sequential analysis strategy and a corresponding step-by-step active column solver by means of improving the existing skyline solver. The new strategy avoids considerably repeated calculation by only working on the latest appended and modified part of resultant stiffness matrices in each construction level. Without any simplification, the strategy guarantees accuracy while efficiency is greatly enhanced. The numerical tests show that the proposed strategy can be implemented with high efficiency in practical engineering design.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.23-29
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• 2011

There have been many studies on red tide due to increasing damage from red tide on fishing and aquaculture industry. However, internal study of automatic red tide image classification is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies used a few type of red tide algae for image classification. In this paper, we proposed the red tide image recognition method using semantic features of NMF and roundness of image objects.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.88-93
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• 2016

This paper presents a method of constructing the highly performance switching circuit(HPSC) over finite fields. The proposed method is as following. First of all, we extract the input/output relationship of linear characteristics for the given digital switching functions, Next, we convert the input/output relationship to Directed Cyclic Graph using basic gates adder and coefficient multiplier that are defined by mathematical properties in finite fields. Also, we propose the new factorization method for matrix characteristics equation that represent the relationship of the input/output characteristics. The proposed method have properties of generalization and regularity. Also, the proposed method is possible to any prime number multiplication expression.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1236-1239
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• 2002

When we see an object, we usually can say what it is easily even for the case where the object isn't shown in the frontal view. However, it is difficult to believe that all views of every object we have ever seen are fully memorized in our brain. Possibly, when an object is shown, we have some typical views of the object in our brain through our past experience and reconstruct the view to recognize what the presented object is. Non-negative Matrix Factorization (NMF) is one of the methods to extract the basis images from sample data set. The prominent feature of this method is that the reconstructed image is obtained by only additions of the basis images with suitable positive weights. So NMF can be seen more biologically plausible method than any other feature extraction methods such as Vector Quantization (VQ) and principal Component Analysis (PCA). In this paper, we adopt NMF to extract the aspect features from the set of images, which consists of various views of a given object. Some experiments are shown how much well NMF can extract the aspect features than any other methods such as VQ and PCA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6116-6132
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• 2017

The proxy re-encryption allows an intermediate proxy to convert a ciphertext for Alice into a ciphertext for Bob without seeing the original message and leaking out relevant information. Unlike many prior identity based proxy re-encryption schemes which are based on the number theoretic assumptions such as large integer factorization and discrete logarithm problem. In this paper, we first propose a novel identity based proxy re-encryption scheme which is based on the hardness of standard Learning With Error(LWE) problem and is CPA secure in the standard model. This scheme can be reduced to the worst-case lattice hard problem that is able to resist attacks from quantum algorithm. The key step in our construction is that the challenger how to answer the private query under a known trapdoor matrix. Our scheme enjoys properties of the non-interactivity, unidirectionality, anonymous and so on. In this paper, we utilize primitives include G-trapdoor for lattice and sample algorithms to realize simple and efficient re-encryption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.