• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.793-794
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• 2008

The block size of intra prediction mode can differentiate the texture area from the homogeneous area of image. This information can be used to enhance the size resolution of image. Specifically, in this paper, we apply the bicubic interpolation or the bilinear interpolation adaptively selected the intra prediction mode of the H.264 compression.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.36-41
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• 2020

In this paper, We need to change all print factors when which print scaffold with 400 ㎛ pore using FDM 3d printer. Therefore the print quantity is 10 billion times, So we are difficult to print on workplace. To solve the problem, we used the prediction model based machine learning regression. We preprocessed and learned the securing print condition data, and we produced different kinds of prediction models. We predicted the pore size of scaffolds not securing with new print condition data using prediction models. We have derived the print conditions that satisfy the pore size of 400 ㎛ among the predicted print conditions of pore size. We printed the scaffolds 5 times on the condition. We measured the pore size of the printed scaffold and compared the average pore size with the predicted pore size. We confirmed that error was less than 1%, and we were identify the model with the highest pore size prediction performance of scaffold.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.193-198
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• 2009

The H.264/ AVC video coding standard supports the intra prediction with various block sizes for luma component and a 8x8 block size for chroma components. This new feature of H.264/AVC offers a considerably higher improvement in coding efficiency compared to previous compression standards. In order to achieve this, H.264/AVC uses the Rate-distortion optimization (RDO) technique to select the best intra prediction mode for each block size, and it brings about the drastic increase of the computation complexity of H.264 encoder. In this paper, a fast block size decision algorithm is proposed to reduce the computation complexity of the intra prediction in H.264/AVC. The proposed algorithm computes the smoothness based on AC and DC coefficient energy for macroblocks and compares with the nonparametric criteria which is determined by considering information on neighbor blocks already reconstructed, so that deciding the best probable block size for the intra prediction. Also, the use of non-parametric criteria makes the performance of intra-coding not be dependent on types of video sequences. The experimental results show that the proposed algorithm is able to reduce up to 30% of the whole encoding time with a negligible loss in PSNR and bitrates and provides the stable performance regardless types of video sequences.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.

• Journal of Welding and Joining
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• v.24 no.1
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• pp.77-87
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• 2006

Considering ferrite grain size in the base metal, the prediction model for $A_{c3}$ temperature and prior austenite grain size at just above $A_{c3}$ temperature was proposed. In order to predict $A_{c3}$ temperature, the Avrami equation was modified with the variation of ferrite grain size, and its kinetic parameters were measured from non-isothermal data during continuous heating. From calculation using a proposed model, $A_{c3}$ temperatures increased with increasing ferrite grain size and heating rate. Meanwhile, by converting the phase transformation kinetic model that predicts the ferrite grain size from austenite grain size during cooling, a prediction model for prior austenite grain size at just above the $A_{c3}$ temperature during heating was developed.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.201-207
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• 2019

An anchorage system is essential for most reinforced concrete structures to connect building components. Therefore, the prediction of strength of the anchor is very important issue for safety of the structures themselves as well as structural components. The prediction models in existing design codes are, however, not applicable for large anchors because they are based on the small size anchors with diameters under 50 mm. In this paper, new prediction models for strength of a single anchor, especially the tensile strength of a single anchor, is developed from the experimental results with consideration of size effect. Size effect in the existing models such as ACI or CCD method is based on the linear fracture mechanics which is very conservative way to consider the size effect. Therefore, new models are developed based on the nonlinear fracture mechanics rather than the linear fracture mechanics for more reasonable prediction. New models are proposed by the regression analysis of the experimental results and it can predict the tensile strength of both small and large anchors.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.15-28
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• 2023

The compaction properties of the soil change depending on the physical properties, and are also affected by crushing of the particles. Since the particle size distribution of soil affects the engineering properties of the soil, it is necessary to analyze the material properties to understand the compaction characteristics. In this study, the size of each sieve was classified into four in the particle size analysis as a material property, and the compaction characteristics were evaluated by multiple regression and maximum dry unit weight. As a result of maximum dry unit weight prediction, multiple regression analysis showed R² of 0.70 or more, and DNN analysis showed R² of 0.80 or more. The reliability of the prediction result analyzed by DNN was evaluated higher than that of multiple regression, and the analysis result of DNN-T showed improved prediction results by 1.87% than DNN. The prediction of maximum dry unit weight using particle size distribution seems to be applied to evaluate the compacting state by identifying the material characteristics of roads and embankments. In addition, the particle size distribution can be used as a parameter for predicting maximum dry unit weight, and it is expected to be of great help in terms of time and cost of applying it to the compaction state evaluation.

• Structural Engineering and Mechanics
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• v.32 no.3
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• pp.459-475
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• 2009

This paper presents analytical methodologies for remaining life prediction of plain concrete structural components considering tension softening and size effects. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. Size effect has been accounted for by modifying the Paris law, leading to a size adjusted Paris law, which gives crack length increment per cycle as a power function of the amplitude of a size adjusted stress intensity factor (SIF). Details of tension softening effects and size effect in the computation of SIF and remaining life prediction have been presented. Numerical studies have been conducted on three point bending concrete beams under constant amplitude loading. The predicted remaining life values with the combination of tension softening & size effects are in close agreement with the corresponding experimental values available in the literature for all the tension softening models.

• International Journal of Concrete Structures and Materials
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• v.10 no.4
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• pp.451-460
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• 2016

An anchorage system is necessary in most reinforced concrete structures for connecting attachments. It is very important to predict the strength of the anchor to safely maintain the attachments to the structures. However, according to experimental results, the existing design codes are not appropriate for large anchors because they offer prediction equations only for small size anchors with diameters under 50 mm. In this paper, a new prediction model for breakout shear strength is suggested from experimental results considering the characteristics of large anchors, such as the prying effect and size effect. The proposed equations by regression analysis of the derived model equations based on the prying effect and size effect can reasonably be used to predict the breakout shear strength of not only ordinary small size anchors but also large size anchors.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.349-352
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.