• Steel and Composite Structures
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• v.44 no.1
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• pp.49-63
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• 2022

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2835-2841
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• 2024

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.1-14
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• 2006

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.

• Journal of Technology Innovation
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• v.21 no.2
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• pp.199-223
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• 2013

Recently, R&D policy has importantly emphasized the creation of economic values added through the research performance developed by public research institutes. However, the research performance hasn't been still used and diffused effectively in Korea (Republic of Korea). This empirical study analyzes the effects of performance management & application capabilities and activities on technology transfer from public research institutes in Korea. Our dataset consists of total 84 Korean universities and government-funded research institutes in 2011. Performance management & application capabilities include dedicated organization, researcher-to-professional ratio, technology transfer and commercialization budget, and performance management & application activities include regular conduct of 3P analysis, pre-adjustment, post-management. The results show that performance management & application capabilities (except researcher-to-professional ratio) and activities are positively related to technology transfer. The results of this study contribute to the establishment of R&D policy to promote management & application of the research performance.

• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• Geomechanics and Engineering
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• v.14 no.3
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• pp.211-224
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• 2018

In this paper, a predictive method accounting for the scaling effects of rockfill materials in the numerical deformation analysis of rockfill dams is developed. It aims to take into consideration the differences of engineering properties of rockfill materials between in situ and laboratory conditions in the deformation analysis. The developed method is based on the modification of model parameters used in the chosen material model, which is, in this study, an elasto-plastic model with double yield surfaces, i.e., the modified Hardening Soil model. Datasets of experimental tests are collected from previous studies, and a new dataset of the Nam Ngum 2 dam project for investigating the scaling effects of rockfill materials, including particle size, particle gradation and density, is obtained. To quantitatively consider the influence of particle gradation, the coarse-to-fine content (C/F) concept is proposed in this study. The simple relations between the model parameters and particle size, C/F and density are formulated, which enable us to predict the mechanical properties of prototype materials from laboratory tests. Subsequently, a 3D finite element analysis of the Nam Ngum 2 concrete face slab rockfill dam at the end of the construction stage is carried out using two sets of model parameters (1) based on the laboratory tests and (2) in accordance with the proposed method. Comparisons of the computed results with dam monitoring data indicate that the proposed method can provide a simple but effective framework to take account of the scaling effect in dam deformation analysis.

• Journal of Preventive Medicine and Public Health
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• v.39 no.2
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• pp.177-183
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• 2006

Objectives : In this study we examined the association between the genetic markers ACE (A-240T, C-93T, I/D, A2350G), AGT (M235T), AT1R (A1166C), CYP11B2 (T344C, V386A), REN (G2646A), ADRB2 (G46A, C79G, T47C, T1641), GNB3 (C825T) and ADD1 (G460W) and the presence of essential hypertension in adolescents. Methods : The Kangwha Study is an 18-year prospective study that is aimed at elucidating the determinants of the blood pressure level from childhood to early adulthood. For this study, we constructed a case-control dataset of size of 277 and 40 family trios data from the Kangwha Study. For this purpose, we perform a single locus-based case-control association study and a single locus-based TDT (transmission/disequilibrium test) study. Results : In the case-control study, the single locus-based association study indicated that the ADD1 (G460W) (p=0.0403), AGT (M235T) (p=0.0002), and REN (G2646A) (p=0.0101) markers were significantly associated with the risk of hypertension. These results were not confirmed on the TDT study. This study showed that genetic polymorphisms of the ADD1, AGT and REN genes might be related to the hypertension in Korean adolescents. Conclusions : This study provided useful information on genetics markers related to blood pressure. Further study will be needed to confirm the effect of the alpha adducin gene, the angiotensinogen gene and the renin gene on essential hypertension.

• Oncology Letters
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• v.41 no.6
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• pp.3464-3474
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• 2019

The EF-hand calcium binding protein tescalcin (TESC) is highly expressed in various human and mouse cancer tissues and is therefore considered a potential oncogene. However, the underlying mechanism that governs TESC expression remains unclear. Emerging evidence suggests that TESC expression is under epigenetic regulation. In the present study, the relationship between the epigenetic modification and gene expression of TESC in gastric cancer was investigated. To evaluate the relationship between the methylation and expression of TESC in gastric cancer, the methylation status of CpG sites in the TESC promoter was analyzed using microarray with the Illumina Human Methylation27 BeadChip (HumanMethylation27_270596_v.1.2), gene profiles from the NCBI Dataset that revealed demethylated status were acquired, and real-time methylation-specific PCR (MSP) in gastric cancer cells was conducted. In the present study, it was demonstrated that the hypermethylation of TESC led to the downregulation of TESC mRNA/protein expression. In addition, 5-aza-2c-deoxycytidine (5'-aza-dC) restored TESC expression in the tested gastric cancer cells except for SNU-620 cells. ChIP assay further revealed that the methylation of the TESC promoter was associated with methyl-CpG binding domain protein (MBD)1, histone deacetylase (HDAC)2, and Oct-1 and that treatment with 5'-aza-dC facilitated the dissociation of MBD1, HDAC2, and Oct-1 from the promoter of TESC. Moreover, silencing of TESC increased MBD1 expression and decreased the H3K4me2/3 level, thereby causing transcriptional repression and suppression of cell survival in NCI-N87 cells; conversely, overexpression of TESC downregulated MBD1 expression and upregulated the H3K4me2 level associated with active transcription in SNU-638 cells. These results indicated that the differential expression of TESC via the modification status of the promoter and histone methylation controled cell survival in gastric cancer cells. Overall, the present study provided a novel therapeutic strategy for gastric cancer.