• Molecules and Cells
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• v.37 no.9
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• pp.691-698
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• 2014

SCYL1-BP1 is thought to function in the p53 pathway through Mdm2 and hPirh2, and mutations in SCYL1-BP1 are associated with premature aging syndromes such as Geroderma Osteodysplasticum; however, these mechanisms are unclear. Here, we report significant alterations in miRNA expression levels when SCYL1-BP1 expression was inhibited by RNA interference in HEK293T cells. We functionally characterized the effects of potential kernel miRNA-target genes by miRNA-target network and protein-protein interaction network analysis. Importantly, we showed the diminished SCYL1-BP1 dramatically reduced the expression levels of EEA1, BMPR2 and BRCA2 in HEK293T cells. Thus, we infer that SCYL1-BP1 plays a critical function in HEK293T cell development and directly regulates miRNA-target genes, including, but not limited to, EEA1, BMPR2, and BRCA2, suggesting a new strategy for investigating the molecular mechanism of SCYL1-BP1.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.3
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• pp.326-333
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• 2003

In the measurement using DMA and CPC in series, there is some time delay for particles classified in DMA to detect in CPC. During this time, the DMA time-response changes due to the velocity profile of sampling tube and the diffusion of particles in the volume that exists between the DMA exit and the detector of ultra-fine CPC. This is called mixing effect. In the accelerated measurement methods like the TSI -SMPS, the size distribution is obtained from the correlation between the time-varying electrical potential of the DMA and the corresponding particle concentrations sampled in DMA. If the DMA time -response changes during this delay time, this can cause the error of a size distribution measured by this accelerated technique. The kernel function considering this mixing effect using the residence time distribution is proposed by Russell et al. In this study, we obtained a size distribution using this kernel to compare to the result obtained by the commercial accelerated measurement system, TSI -SMPS for verification and considered the errors that result from the mixing effect with the geometric mean diameters of originally sampled particles, using virtually calculated responses obtained with this kernel as input data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• The Pure and Applied Mathematics
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• v.15 no.1
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• pp.1-17
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• 2008

Here, we consider the existence and uniqueness solution of nonlinear integral equation of the second kind of type Volterra-Hammerstein. Also, the normality and continuity of the integral operator are discussed. A numerical method is used to obtain a system of nonlinear integral equations in position. The solution is obtained, and many applications in one, two and three dimensionals are considered.

• Computers and Concrete
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• v.5 no.5
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.669-679
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• 2022

Purpose To evaluate the feasibility of pediatric low-dose facial CT reconstructed with filtered back projection (FBP) using adequate kernels. Materials and Methods We retrospectively reviewed the clinical and imaging data of children aged < 10 years who underwent facial CT at our emergency department. The patients were divided into two groups: low-dose CT (LDCT; Group A, n = 73) with a fixed 80-kVp tube potential and automatic tube current modulation (ATCM) and standard-dose CT (SDCT; Group B, n = 40) with a fixed 120-kVp tube potential and ATCM. All images were reconstructed with FBP using bone and soft tissue kernels in Group A and only bone kernel in Group B. The groups were compared in terms of image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Two radiologists subjectively scored the overall image quality of bony and soft tissue structures. The CT dose index volume and dose-length product were recorded. Results Image noise was higher in Group A than in Group B in bone kernel images (p < 0.001). Group A using a soft tissue kernel showed the highest SNR and CNR for all soft tissue structures (all p < 0.001). In the qualitative analysis of bony structures, Group A scores were found to be similar to or higher than Group B scores on comparing bone kernel images. In the qualitative analysis of soft tissue structures, there was no significant difference between Group A using a soft tissue kernel and Group B using a bone kernel with a soft tissue window setting (p > 0.05). Group A showed a 76.9% reduction in radiation dose compared to Group B (3.2 ± 0.2 mGy vs. 13.9 ± 1.5 mGy; p < 0.001). Conclusion The addition of a soft tissue kernel image to conventional CT reconstructed with FBP enables the use of pediatric low-dose facial CT protocol while maintaining image quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1569-1572
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• 2003

Meshfree approximations exhibit significant potential to solve partial differential equations. Meshfree methods have been successfully applied to various problems which the traditional finite element methods have difficulties to handle, including the quasi-static and dynamic fracture. large deformation problems, contact problems, and strain localization problems. A meshfree method based on the reproducing kernel particle approximation(RKPM) is applied to sheet metal forming analysis in this research. Metal forming examples, such as stretch forming and flanging operation, are analyzed to demonstrate the performance of the proposed meshfree method for largely deformed elasto-plastic material.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.65-78
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• 1999

We present a fast/parallel Poisson solver on disks, based on efficient evaluation of the exact solution given by the Newtonian potential and the Poisson integral. Derived from an integral formula-tion it is more accurate and simpler in parallel implementation and in upgrading to a higher order algorithm than an algorithm which solves the linear system obtained from a differential formulation.