• Korean Journal of Mathematics
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• v.7 no.1
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• pp.71-77
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• 1999

We show that if the stable rank of $B^{\alpha}$ is one, then the stable rank of B is less than or equal to the order of G for any action of a finite group G. Also we give a short proof to the known fact that if the action of a finite group on a $C^*$-algebra B is saturated then the canonical conditional expectation from B to $B^{\alpha}$ is of index-finite type and the crossed product $C^*$-algebra is isomorphic to the algebra of compact operators on the Hilbert $B^{\alpha}$-module B.

• The korean journal of orthodontics
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• v.38 no.3
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• pp.159-174
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• 2008

Objective: The purpose of this study was to investigate whether cortical punching could stimulate the expression of OPG, RANK, and RANKL during tooth movement by immunohistochemistry. Methods: 34 sprague-dawley rats (15 weeks old) were allocated into 3 groups: TMC group (experimental group; Tooth Movement with Corticotomy, n = 16), TM group (control group; Tooth Movement only group, n = 16), and non-treatment group (n = 2). 20 gm of orthodontic force was applied to rat incisors by inserting elastic bands. The duration of force application was 1, 4, 7 and 14 days. A microscrew (diameter 1.2 mm) was used for cortical punching of the palatal side of the upper incisors in the TMC group. Results: Distributions of OPG, RANK, and RANKL were evaluated by immunohistochemistry. OPG, RANK and RANKL were observed on experimental and control groups. On the compression side, the degree of the expression of OPG decreased in both groups. The expression of RANK was most prominent in the experimental group of day 4. The expression of RANKL was most intensive and extensive in the experimental group of day 7. However, the expression of OPG was decreased in the experimental and control groups compared to the non treatment group. The expression of OPG, RANK and RANKL after force application were decreased at day 14. Conclusions: These findings suggested that cortical punching might stimulate remodeling of alveolar bone during a 2 week period of tooth movement without any pathologic change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1383-1390
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• 2012

In this paper, we propose a robust pupil detection method using rank order filter and pixel value difference in facial image. We have detected the potential pupil candidates using rank order filter. Many false pupil candidates found at eyebrow are removed using the fact that the pixel difference is much at the boundary between pupil and sclera. The rest pupil candidates are grouped into pairs. Each pair is verified according to geometric constraints such as the angle and the distance between two candidates. A fitness function is obtained for each pair using the pixel values of two pupil regions, we select a pair with the smallest fitness value as a final pupil. The experiments have been performed for 400 images of the BioID face database. The results show that it achieves more than 90% accuracy, and especially the proposed method improves the detection rate and high accuracy for face with spectacle.

• The Pure and Applied Mathematics
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• v.21 no.2
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• pp.121-128
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• 2014

The Boolean rank of a nonzero m $m{\times}n$ Boolean matrix A is the least integer k such that there are an $m{\times}k$ Boolean matrix B and a $k{\times}n$ Boolean matrix C with A = BC. In 1984, Beasley and Pullman showed that a linear operator preserves the Boolean rank of any Boolean matrix if and only if it preserves Boolean ranks 1 and 2. In this paper, we extend this characterization of linear operators that preserve the Boolean ranks of Boolean matrices. We show that a linear operator preserves all Boolean ranks if and only if it preserves two consecutive Boolean ranks if and only if it strongly preserves a Boolean rank k with $1{\leq}k{\leq}min\{m,n\}$.

• 한국연소학회:학술대회논문집
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• 2013.06a
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• pp.129-132
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• 2013

The low rank coal combustion and biomass-coal co-firing characteristics were reviewed on this study for the power plant construction. The importance of using low rank coal(LRC) for power plant is increasing gradually due to power generation economy and biomass co-firing is also concentrated as power source because it has carbon neutral characteristics to reduce green-house effect. The combustion characteristics of low rank coal and biomass for a 310MW coal firing power plant and a 100MW biomass and coal co-firing power plant were studied to apply into actual power plant design and optimized the furnace and burner design.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Biomedical Science Letters
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• v.23 no.4
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• pp.295-302
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• 2017

Mononuclear osteoclast precursors derived from hematopoietic progenitors fuse together and then become multinucleated mature osteoclasts by macrophage-colony stimulating factor (M-CSF) and receptor activator of nuclear factor-${\kappa}B$ ligand (RANKL). Especially, the binding of RANKL to its receptor RANK provides key signals for osteoclast differentiation and bone-resorbing function. RANK transduces intracellular signals by recruiting adaptor molecules such as TNFR-associated factors (TRAFs), which then activate mitogen activated protein kinases (MAPKs), Src/PI3K/Akt pathway, nuclear factor-${\kappa}B$ (NF-${\kappa}B$) and finally amplify NFATc1 activation for the transcription and activation of osteoclast marker genes. This review will briefly describe RANKL-RANK signaling pathways and key molecules critical for osteoclast differentiation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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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.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.