• Tribology and Lubricants
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• v.30 no.1
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• pp.59-63
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• 2014

Laser Surface Texturing(LST) is a surface engineering process used to improve tribological characteristics of materials by creating patterned microstructures on the mechanical contact surface. In LST technology, a pulsated laser beam is used to create arranged dimples on a surface by a material ablation process, which can improve such as load capacity, wear resistances, lubrication lifetime, and reduce friction coefficients. In the present study, the effect of multi-scale LST on lubricant regime was investigated. A pulsed Nd:YAG laser was applied on the bearing steel(AISI 52100) to create arranged dimples. To optimize the surface texturing effect on friction, multi-scale texture dimples with some specific formula arrays were fabricated by combining circles, ellipses and the laser ablation process. The tribological testing of multi-scale textured surface was performed by a flat-on-flat unidirectional tribometer under lubrication and the results compared with that of the non-textured surface. Through an increase in sliding speed, the beneficial effect of multi-scale LST performance was achieved. The multi-scale textured surface had lower friction coefficient performances than the non-textured surface due to the hydrodynamic lubrication effect.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.400-407
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• 2019

In this paper, the shadow detection and reconstruction method are proposed using intrinsic image, which does not change the essential characteristics under the influence of various illuminance, and multi-scale gamma correction. The shadow detection was estimated by the pixel change information between a grayscale and an intrinsic image of the color image, and the brightness of the image were adjusted by gamma correction in the shadow restoration process. Multi-scale gamma correction is performed for each channel of a color image due to the fact that the saturation can be changed by nonlinear adjustment to individual pixel values. Multi-scale gamma values are estimated based on the information of the crossed edge between shadows and non-shadowed regions in the color image, as a result, the shadows are reconstructed by correcting different region features with multi-scale gamma values. Experimental results show that the proposed method effectively reconstructs shadows in a single natural image.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.177-180
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• 2005

The specified tensile strength provided by the manufacturer is determined on the basis of the reliable lower limit ($X-3{\sigma}$ : X=average tensile strength, $\sigma$=standard deviation) obtained from the material test results. Most of these data, however, are based on the test results of 1 layer of FRP sheet. Also, the partial strength reduction factor for strengthening RC members with FRP is based on the small-scale model tests. But, the failure mechanisms of small-scale model tests are reported to be different from those of the full-scale tests. This paper present the test results of full-scale RC beams strengthened with multi-layer GFRP sheets up to 3 layer as well as the material tests. From the material tests, it was observed that the average tensile strengths of GFRP sheets are decreased as the number of layer are increased. Also the premature debonding failure of RC beams strengthened with multi-layer GFRP sheets are observed in inverse proportion to the number of GFRP sheets

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

• Coupled systems mechanics
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• v.8 no.2
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• pp.99-109
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• 2019

This work investigates the accuracy and performance of a $FE^2$ multi-scale implementation used to predict the behavior of composite materials. The equations are formulated assuming the small deformations solid mechanics approach in non-linear material models with hardening plasticity. The uniform strain boundary conditions are applied for the macro-to-micro transitions. A parallel algorithm was implemented in order to solve large engineering problems. The scheme proposed takes advantage of the domain decomposition method at the macro-scale and the coupling between each subdomain with a micro-scale model. The precision of the method is validated with a composite material problem and scalability tests are performed for showing the efficiency.

• Nuclear Engineering and Technology
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• v.37 no.6
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• pp.511-524
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• 2005

Thermalhydraulic reactor simulation of tomorrow will require a new generation of codes combining at least three scales, the CFD scale in open medium, the component scale and the system scale. DNS will be used as a support for modelling more macroscopic models. NEPTUNE is such a new generation multi-scale platform developed jointly by CEA-DEN and EDF-R&D and also supported by IRSN and FRAMATOME-ANP. The major steps towards the next generation lie in new physical models and improved numerical methods. This paper presents the advances obtained so far in physical modelling for each scale. Macroscopic models of system and component scales include multi-field modelling, transport of interfacial area, and turbulence modelling. Two-phase CFD or CMFD was first applied to boiling bubbly flow for departure from nucleate boiling investigations and to stratified flow for pressurised thermal shock investigations. The main challenges of the project are presented, some selected results are shown for each scale, and the perspectives for future are also drawn. Direct Numerical Simulation tools with Interface Tracking Techniques are also developed for even smaller scale investigations leading to a better understanding of basic physical processes and allowing the development of closure relations for macroscopic and CFD models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• Microbiology and Biotechnology Letters
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• v.20 no.3
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• pp.324-328
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• 1992

In order to develop more economic processes, continuous ethanol fermentation from starchy raw materials in a pilot scale multi-stage CSTR was investigated. Ethanol fermentation could be successfully operated for 30 days with naked barley and 60 days with cassava, respectively. Starchy raw materials used for this study were ground and passed through a 20-mesh sieve for low temperature cooking. Under the optimized conditions, the overall productivity of cassava was $1.27g/{\ell}{\cdot}h$ with an ethanol concentration of 9.51% (v/v), which was higher about 2 times than that obtained from a conventional batch system in industrial scale.

• Journal of the Korean Home Economics Association
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• v.33 no.4
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• pp.49-64
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• 1995

The purpose of this study was to develop the Daily Hassles Scale for children in Korea. The subject were 444 children of 184 fourth graders and 260 sixth graders selected form five elementary schools in Seoul(217 male and 227 female). A questionnaire consisting of 90-item daily hassles scale, demographic questions, and some additional questions was used as a methodological instrument. statistics used for data analysis were X2, cramer's V, factor analysis, multi-regression, Pearson's r, Cronbach's α. The major findings of this study were as follows. 1) 87 items of the 90-item scale were acceptible through item discriminant method. The discriminant coefficients of the items(Cramer's V) ranged form .28 to .73. 2) 6 factors(parents, home environment, friends, studies, teachers & school, the surroundings) were extracted from factor analysis. Multi-regression analysis conducted to reduce the length of scale have drawed 42 items for 'the Daily Hassles Scale for Children in Korea'. The correlation between this scale and the Quality of Life Scale(Olson & Barnes, 1982) was conducted to test the criterion-related validity, and the coefficient was significant(r=-.52, p<.001).3) Finally, reliability coefficients(Cronbach'α) of this scale was. 85.