• Asian-Australasian Journal of Animal Sciences
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• v.33 no.3
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• pp.382-389
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• 2020

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L_SB) and that for the production of cows (L_SC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L_DB) and cows (L_DC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.12
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• pp.837-842
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• 2014

A computer program is developed for the prediction of NO generation by the addition of water moisture and water electrolysis gas in LNG-fired turbulent reacting flow. This study is the first part to deal with the moisture effect on NO generation. In this study, parametric investigation has been made in order to see the reduction of thermal NO as a function of amount of moisture content in a LNG-fired flame together with the swirl and radiation effect. First of all, calculation results show that the flame separation together with the NO concentration separation are observed by the typical flow separation due to strong swirl flow. With a fixed amount of air, the increased amount of water moisture from 0 to 10% by 2% interval shows the decrease of NO concentration and flame temperature at exit are from $973^{\circ}C$ and 139 ppm to $852^{\circ}C$ and 71 ppm. The radiation effects on the generation on NO appears more dominant than swirl strength over the range employed in this study. However, for the strong swirl flow employed in this study, the flow separation cause the relatively high NO concentration observed near exit after peak concentration in the front side of the combustor.

• Journal of the Korea Concrete Institute
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• v.14 no.3
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• pp.291-298
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• 2002

Recently, construction of reinforced concrete tall buildings is widely increased according to the improvement of material quality and design technology. Therefore, differential shortenings of columns due to elastic, creep, and shrinkage have been an important issue. But it has been neglected to predict the Inelastic behavior of RC structures even though those deformations make a serious problem on the partition wall, external cladding, duct, etc. In this paper, analysis system for prediction and compensation of the differential column shortenings considering time-dependent deformations and construction sequence is developed using the objected-oriented technique. Developed analysis system considers the construction sequence, especially time-dependent deformation in early days, and is composed of input module, database module, database store module, analysis module, and analysis result generation module. Graphic user interface(GUI) is supported for user's convenience. After performing the analysis, the output results like deflections and member forces according to the time can be observed in the generation module using the graphic diagram, table, and chart supported by the integrated environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.782-785
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• 2011

Needs for MG(Microgrid) development are increasing all over the world as a solution to the problems including the depletion problem of energy resources, the growing demand for electric power and the climatic and environmental change. Especially Photovoltaic power is one of the most general renewable energy resources. However there is a problem of the uniformity of power quality because the power generated from solar light is very sensitive to climate fluctuation (variation of insolation and duration of sunshine, etc). As a solution to the above problem, ESS(Energy Storage System) is considered generally, but it has some limitations. To solve this problem this paper suggests a hybrid estimation method of photovoltaic power generation according to two climatic factors, i.e. insolation and sunshine. This result seems to help design the appropriate capacity of ESS and estimate the proper switching time between DC and AC power in the premises power system and thus maintain the uniformity of power quality.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.523-533
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• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.

• Journal of Life Science
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• v.33 no.1
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• pp.25-33
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• 2023

The fermentation process of kimchi, which is a traditional Korean food, influences the resulting compo- sition of microorganisms, such as the genera Leuconostoc, Weissella, and Lactobacillus. In addition, several factors, including the type of kimchi, fermentation conditions, materials, and ingredients, can influence the distribution of the kimchi microbial community. In this study, next-generation sequencing (NGS) of kimchi samples obtained from central (Gangwon-do and Gyeonggi-do) and southern (Jeolla-do and Gyeongsang-do) regions in Korea was performed, and the microbial communities in samples from the two regions were compared. Good's coverage prediction for all samples was higher than 99%, indicating that there was sufficient reliability for comparative analysis. However, in a α -diversity analysis, there was no significant difference in species richness and diversity between samples. The Firmicutes phylum was common in both regions. At the species level, Weissella kandleri dominated in central (46.5%) and southern (30.8%) regions. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify biomarkers representing the microbial community in each region. The LEfSe results pointed to statistically significant differences between the two regions in community composition, with Leuconostocaceae (71.4%) dominating in the central region and Lactobacillaceae (61.0%) dominating in the southern region. Based on these results, it can be concluded that the microbial communities of kimchi are significantly influenced by regional properties and that it can provide more useful scientific data to study the relationship between regional characteristics of kimchi and their microbial distribution.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.305-310
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• 2014

We called a current society the convergence generation. In information society, digital convergence means a service or new product which appeared through fusion of unit technologies in information and communication regions. The effects of convergence technologies and social phenomenons are visualized in overall regions of society such as economy, society, culture, etc. In 2011, The Government introduced "IT Convergence Technology Prediction Survey 2025". This report includes 10 ICT industries. In this paper, we described a smart city which was leading case in digital convergence and related with our life.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.2
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• pp.42-50
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• 2012

The goal of heat transfer studies is the accurate prediction of temperature and heat flux distribution on material boundaries. To this purpose, general-purpose computational fluid dynamics(CFD) code is used : FLUENT. Mass fluxes and pressure ratio are calculated for two types of nozzle. The comparative studies reveal that the computational results are in agreement with the experimental data. Also, heat transfer coefficients from FLUENT for one type of nozzle are very similar and agree well with the experimental data in the diverging part of the nozzle, but the calculated results are large in the converging part. The heat transfer coefficients from Bartz equation are over-predicted. We can consider various reasons for these differences, i.e., laminarization by the highly accelerated flow in the nozzle, turbulent flow model and grid generation.

• ETRI Journal
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• v.37 no.2
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• pp.283-294
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• 2015

A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.