• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1099-1104
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• 2007

The visual implementation of 3-dimensional national environment is focused by the requirement and importance in the fields such as, national development plan, telecommunication facility deployment plan, railway construction, construction engineering, spatial city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies national geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we first investigate the LiDAR based researches in advanced foreign countries, then we propose data a generation scheme and an algorithm for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.30-36
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• 2008

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we tested of the railway facilities using laser surveying system, then we propose data a generation of spatial images for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation. As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• Journal of Plant Biotechnology
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• v.44 no.1
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• pp.89-96
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• 2017

The CRISPR/Cas9 is a core technology that can result in a paradigm for breeding new varieties. This study describes in detail the sgRNA design, vector construction, and the development of a transgenic plant and its molecular analysis, and demonstrates how gene editing technology through the CRISPR/Cas9 system can be applied easily and accurately. CRISPR/Cas9 facilitates targeted gene editing through RNA-guided DNA cleavage, followed by cellular DNA repair mechanisms that introduce sequence changes at the site of cleavage. It also allows the generation of heritable-targeted gene mutations and corrections. Here, we present detailed procedures involved in the CRISPR/Cas9 system to acquire faster, easier and more cost-efficient gene edited transgenic rice. The protocol described here establishes the strategies and steps for the selection of targets, design of sgRNA, vector construction, and analysis of the transgenic lines. The same principles can be used to customize the versatile CRISPR/Cas9 system, for application to other plant species.

• Microbiology and Biotechnology Letters
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• v.47 no.4
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• pp.667-672
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• 2019

We compared two integration systems for stable expression of heterologous genes in Saccharomyces cerevisiae. A Candida glabrata-derived gene was used as the selective marker for the Cre/loxP system, and XYLP, XYLB, GRE3, and XYL2 genes were used as model heterologous genes and ligated into the universal pRS-CMT vector. The resulting pRS-XylP, pRS-XylB, pRS-Gre3, and pRS-Xyl2 plasmids were sequentially integrated into yeast chromosome VII by four integration processes (marker rescue and gene integration). The four introduced genes were successfully expressed. Further, the pRS-PBG2 plasmid harboring expression cassettes for the four genes was constructed for one-step integration. The four genes that were introduced were stably maintained as a gene cluster and were simultaneously expressed. The one-step integration was more effective for the simultaneous integration and expression of the four genes related to xylan/xylose metabolism. This method will enable the generation of a useful biosystem through appropriate use of gene integration methods.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.525-535
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• 2018

If the frequency of a particular class is excessively higher than the frequency of other classes in the classification problem, data imbalance problems occur, which make machine learning distorted. Corporate bankruptcy prediction often suffers from data imbalance problems since the ratio of insolvent companies is generally very low, whereas the ratio of solvent companies is very high. To mitigate these problems, it is required to apply a proper sampling technique. Until now, oversampling techniques which adjust the class distribution of a data set by sampling minor class with replacement have popularly been used. However, they are a risk of overfitting. Under this background, this study proposes ROSE(Random Over Sampling Examples) technique which is proposed by Menardi and Torelli in 2014 for the effective corporate bankruptcy prediction. The ROSE technique creates new learning samples by synthesizing the samples for learning, so it leads to better prediction accuracy of the classifiers while avoiding the risk of overfitting. Specifically, our study proposes to combine the ROSE method with SVM(support vector machine), which is known as the best binary classifier. We applied the proposed method to a real-world bankruptcy prediction case of a Korean major bank, and compared its performance with other sampling techniques. Experimental results showed that ROSE contributed to the improvement of the prediction accuracy of SVM in bankruptcy prediction compared to other techniques, with statistical significance. These results shed a light on the fact that ROSE can be a good alternative for resolving data imbalance problems of the prediction problems in social science area other than bankruptcy prediction.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.12
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• pp.1339-1349
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• 2008

In this paper, a nonlinear relationship between an input complex envelope and an output complex envelope of m-th harmonic zone is theoretically analyzed, and AM/$AM_m$ and AM/$PM_m$ are defined. A scheme to extract these characteristics from measured in-phase and quadrature-phase data is suggested. The proposed analysis is verified with a fundamental-fundamental and fundamental-third harmonic measurements for a InGaP power amplifier(PA). Based on the harmonic-band nonlinear analysis and extraction scheme, a new technique to send a signal in m-th harmonic band with a harmonic signal Linearization Digital Predistortion(DPD) scheme is presented. A numerical analysis and a Look-Up Table(LUT) based DPD algorithms to linearize output signal on m-th harmonic zone are developed. For a 16- and a 64-QAM input signals, a DPD for third harmonic signal linearization is implemented, and output spectrum and signal constellation are measured. The wholly distorted signals are linearized, and thus the measured Error Vector Magnitudes (EVM) are 6.4 % and 6.5 % respectively. The results show that a proposed scheme linearizes a nonlinearly distorted harmonic band signals. The proposed nonlinear analysis and predistortion scheme can be applied to multiband transmitter in next generation software defined radio(SDR)/cognitive radio(CR) wireless system.

• Reproductive and Developmental Biology
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• v.39 no.3
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• pp.59-67
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• 2015

Galactose-${\alpha}1,3$-galactose (${\alpha}1,3$-Gal) epitope is synthesized at a high concentration on the surface of pig cells by ${\alpha}1,3$-galactosyltransferase gene (GGTA1). The ${\alpha}1,3$-Gal is responsible for hyperacute rejection in pig-to-human xenotransplantation. The generation of transgenic pigs as organ donors for humans is necessary to eliminate the GGTA1 gene that synthesize $Gal{\alpha}$(1,3)Gal. To prevent hyperacute graft rejection in pig-to-human xenotransplantation, previously, we developed ${\alpha}1,3$-galactosyltransferase gene-knock-out somatic cell by homologous recombination. In this study, we established cell lines of ${\alpha}1,3$-GT knock-out expressing hDAF and hHT gene from minipig fibroblasts to apply somatic cell nuclear transfer. The hDAF and hHT mRNA were expressed in the knock-in somatic cells and ${\alpha}1,3$-GT mRNA was suppressed. However, the knock-in somatic cells were increased resistance to human serum-mediated cytolysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.42-48
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• 2008

Next generation wireless communication systems require RF transceivers that enable multiband/multimode operations. Polar transmitters are known as good candidates for high data rate systems such as EDGE (Enhanced Data Rates for GSM Evolution), WCDMA (Wideband Code Division Multiple Access), and WLAN (Wireless Local Area Network) because they can obtain high efficiency by using efficient switched-mode RF power amplifiers. In this paper, we investigate the performance of a simple peak windowing scheme for the OFDM (Orthogonal frequency Division Multiplexing) polar transmitter, which requires no change of a receiver structure or no additional information transmission. The approach we employed is to apply the peak windowing scheme to the amplitude modulated signals of the polar transmitter to reduce the PAPR (Peak-to-Average Power Ratio). The BER (Bit Error Rate) and EVM (Error Vector Magnitude) performances are measured for various window types and lengths. The simulation results demonstrate that the proposed algorithm mitigates out-of-band distortion introduced by clipping along with PAPR reduction.