• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.589-600
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• 2009

This paper is ready to change a trend of a ultra high definition (UHD) video image, and it will contribute to improve the performance of the latest H.264 through the Uni-directional $8{\times}8$ intra-prediction idea which is based on developing a intra prediction compression. The Uni-directional $8{\times}8$ intra prediction is focused on a $8{\times}8$ block intra prediction using $4{\times}4$ block based prediction which is using the same direction of intra prediction. This paper describes that the uni-directional $8{\times}8$ intra-prediction gets a improvement around 7.3% BDBR only in the $8{\times}8$ block size, and it gets a improvement around 1.3% BDBR in the H.264 applied to the multi block size structures. In the case of a larger image size, it can be changed to a good algorithm. Because the video codec which is optimized for UHD resolution can be used a different block size which is bigger than before(currently a minimum of $4{\times}4$ blocks of units).

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.1
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• pp.76-82
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• 2002

In this study, the rotary bending fatigue test was carried out with low carbon steel(SM25C). The specimens were heat-treated in order to change the grain size, and investigated items are fatigue limit, small crack initiation, fatigue crack propagation behavior and possibility of fatigue life prediction according to the different grain size. The summarized result are as follows ; Fatigue limit of the smooth specimen was dependent upon the grain size. The fatigue crack initiation of the small grain size specimen was delayed more than that of the large grain size specimen. And the small cracks of small grain size specimen were distributed in the narrow region of the main crack circumference contrary to the large grain size specimen. The main crack was grown along the grain boundary having co-alliance with small cracks. The experiment material has quantitatively disclosed the possibility of fatigue life prediction because the fatigue crack propagation behavior is dependent upon the grain size.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Fire Science and Engineering
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• v.33 no.6
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• pp.9-19
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• 2019

In fire simulations for building fire safety evaluation, changes in the fire area and grid size can significantly influence the prediction results. Therefore, the effects of area changes of the fire source with identical maximum heat release rates on the prediction results of a compartment fire were investigated. The dependence of the prediction results on the grid size using the identical fire area was also examined. No significant changes were observed in the thermal and chemical characteristics of the fires with variable grid sizes, even though the fire area was changed when six or more grids were set based on the fire diameter. In addition, changes in the fire area caused significant differences in the prediction of major physical quantities associated with available safety egress time (ASET) within a compartment. However, the fire area changes did not considerably influence the overall fire characteristics outside the compartment after reaching a certain distance from the opening.

• Journal of Animal Science and Technology
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• v.62 no.4
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• pp.438-448
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• 2020

This study was performed to increase the accuracy of genomic estimated breeding value (GEBV) predictions for domestic pigs using single-breed and admixed reference populations (single-breed of Berkshire pigs [BS] with cross breed of Korean native pigs and Landrace pigs [CB]). The principal component analysis (PCA), linkage disequilibrium (LD), and genome-wide association study (GWAS) were performed to analyze the population structure prior to genomic prediction. Reference and test population data sets were randomly sampled 10 times each and precision accuracy was analyzed according to the size of the reference population (100, 200, 300, or 400 animals). For the BS population, prediction accuracy was higher for all economically important traits with larger reference population size. Prediction accuracy was ranged from -0.05 to 0.003, for all traits except carcass weight (CWT), when CB was used as the reference population and BS as the test. The accuracy of CB for backfat thickness (BF) and shear force (SF) using admixed population as reference increased with reference population size, while the results for CWT and muscle pH at 24 hours after slaughter (pH) were equivocal with respect to the relationship between accuracy and reference population size, although overall accuracy was similar to that using the BS as the reference.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1009-1012
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• 1999

An analytical performance model that can predict the performance of a superscalar processor employing multiple branch prediction is introduced. The model is based on the conditional independence probability and the basic block size of instructions, with the degree of multiple branch prediction, the fetch rate, and the window size of a superscalar architecture. Trace driven simulation is performed for the subset of SPEC integer benchmarks, and the measured IPCs are compared with the results derived from the model. As the result, our analytic model could predict the performance of the superscalar processor using multiple branch prediction within 6.6 percent on the average.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.96-99
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• 2007

The microstructure with fine and uniform AGS(austenite grain size) along thickness direction over no recrystallization temperature is strongly required for production of the high strength steels. The previous AGS prediction only based on the average strain improves to find the rolling conditions for accomplishment of the fine grain, but cannot find those for uniform grain. In this paper, an integrated mathematical model for prediction of the strain distribution along thickness direction is developed by carrying out finite element simulation for a series of rolling conditions. Also, the AGS distribution after rough rolling is predicted by applying the proposed model with AGS prediction model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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• pp.2837-2845
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• 1994

Several methods have been developed to predict on the remaining life of the old power plants. However, Larson-Miller parameter, one of existing creep life prediction methods, has not reflected the effect of material degradatioin and grain size. So this study has been carried out to research the effects of material degradation and austenite grain coarsening on the life prediction of 3.5Ni-Cr-Mo-V steel. An experimental result shows that carbide coarsening has no significant effects on the creep rupture life and the Larson-Miller parameter, but grain coarsening has an important influence on the creep ruptrure life and the Larson-Miller parameter. Therefore Larson-Miller constant, K should be determined to consider on the chemical composition and the grain size of materials.