• Journal of Animal Reproduction and Biotechnology
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• v.37 no.4
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• pp.292-297
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• 2022

Direct injection of CRISPR/Cas9 into zygotes enables the production of genetically modified nonhuman primates (NHPs) essential for modeling specific human diseases, such as Usher syndrome, and for developing novel therapeutic strategies. Usher syndrome is a rare genetic disease that causes loss of hearing, retinal degeneration, and problems with balance, and is attributed to a mutation in MYO7A, a gene that encodes an uncommon myosin motor protein expressed in the inner ear and retinal photoreceptors. To produce an Usher syndrome type 1B (USH1B) rhesus macaque model, we disrupted the MYO7A gene in developing zygotes. Identification of appropriately edited MYO7A embryos for knockout embryo transfer requires sequence analysis of material recovered from a trophectoderm (TE) cell biopsy. However, the TE biopsy procedure is labor intensive and could adversely impact embryo development. Recent studies have reported using cell-free DNA (cfDNA) from embryo culture media to detect aneuploid embryos in human in vitro fertilization (IVF) clinics. The cfDNA is released from the embryo during cell division or cell death, suggesting that cfDNA may be a viable resource for sequence analysis. Moreover, cfDNA collection is not invasive to the embryo and does not require special tools or expertise. We hypothesized that selection of appropriate edited embryos could be performed by analyzing cfDNA for MYO7A editing in embryo culture medium, and that this method would be advantageous for the subsequent generation of genetically modified NHPs. The purpose of this experiment is to determine whether cfDNA can be used to identify the target gene mutation of CRISPR/Cas9 injected embryos. In this study, we were able to obtain and utilize cfDNA to confirm the mutagenesis of MYO7A, but the method will require further optimization to obtain better accuracy before it can replace the TE biopsy approach.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1211-1222
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• 2022

Abnormal condition inevitably occurs during operation of water distribution system (WDS) and requires the isolation of certain areas using isolation valves. In general, the determination of the optimal location of isolation valves considered minimization of hydraulic failures as isolation of certain areas causes a change in hydraulic states (e.g., flow direction, velocity, pressure, etc.). Water quality failure can also be induced by changes in hydraulics, which have not been considered for isolation valve system design. Therefore, this study proposes a new isolation valve system design methodology to prevent unexpected water quality failure events. The new methodology considers flow direction change ratio (FDCR), which accounts for flow direction changes after isolation of the area, as a constraint while reliability is used as the objective function. The optimal design model has been applied to a synthetic grid network and the results are compared with the traditional design approach. Results show that considering FDCR can eliminate flow direction changes while average pressure and coefficient of variation of pressure, velocity, and hydraulic geodesic index (HGI) outperform compared to the traditional design approach. The proposed methodology is expected to be a useful approach to minimizing unexpected consequences by traditional design approaches.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.132-140
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• 2022

This study was conducted to identify the optimal lotion manufacturing conditions with decursin and decursinol angelate of Angelica gigas Nakai extraction. Lotion was confirmed that it had viscosity (5,208±112 cPs), assay (99.71±1.01%), and pH (5.62) for 3 months. The optimization of manufacturing conditions of mixing 4 for lotion formulation were made by 22+3 full factorial design. Mixing temperature (40-80℃) and mixing time (10-30 min) were used as independent variables with three responses(assay, pH, and weight variation) as critical quality attributes (CQAs). The model for assay and weight variation identified a proper fit having a determination coefficient of the regression equation (about 0.9) and a p-value less than 0.05. Estimated conditions for the optimal manufacturing process of lotion were 61.93℃ in mixing temperature and 15.85 min in mixing time. Predicted values at the mixing temperature (60℃) and mixing time (20 min) were 100.69% of assay, 5.57 of pH, and 98.07% of weight variation. In the verification of the actual measurement the obtained values showed 100.29±0.98% of assay, 5.57±0.02 of pH, and 98.27±0.89% of weight variation, respectively, in good agreement with predicted values.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.3-12
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• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• Progress in Medical Physics
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• v.33 no.4
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• pp.129-135
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• 2022

Purpose: A full-energy-peak (FEP) efficiency correction is required through a Monte Carlo simulation for accurate radioactivity measurement, considering the geometrical characteristics of the detector and the sample. However, a relative deviation (RD) occurs between the measurement and calculation efficiencies when modeling using the data provided by the manufacturers due to the randomly generated dead layer. This study aims to optimize the structure of the detector by determining the dead layer thickness based on Monte Carlo simulation. Methods: The high-purity germanium (HPGe) detector used in this study was a coaxial p-type GC2518 model, and a certified reference material (CRM) was used to measure the FEP efficiency. Using the MC N-Particle Transport Code (MCNP) code, the FEP efficiency was calculated by increasing the thickness of the outer and inner dead layer in proportion to the thickness of the electrode. Results: As the thickness of the outer and inner dead layer increased by 0.1 mm and 0.1 ㎛, the efficiency difference decreased by 2.43% on average up to 1.0 mm and 1.0 ㎛ and increased by 1.86% thereafter. Therefore, the structure of the detector was optimized by determining 1.0 mm and 1.0 ㎛ as thickness of the dead layer. Conclusions: The effect of the dead layer on the FEP efficiency was evaluated, and an excellent agreement between the measured and calculated efficiencies was confirmed with RDs of less than 4%. It suggests that the optimized HPGe detector can be used to measure the accurate radioactivity using in dismantling and disposing medical linear accelerators.

• Journal of the Korean earth science society
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• v.44 no.1
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• pp.79-89
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• 2023

The purpose of this study was to investigate the effects of engineering design-based ocean cleanup classes on STEAM attitudes and creative engineering problem-solving dispositions. Furthermore, during this process, we tried to determine interesting points that students encountered in engineering design-based classes. For this study, a science class with six lessons based on engineering design was developed and reviewed by a professor who majored in engineering design, along with five engineering design experts with a master's degree or higher. The subject of the class was selected as the design and implementation of scientific and engineering measures to reduce marine pollution based on the method implemented in an actual Ocean Clean-up Project. The engineering design process utilized the engineering design model presented by NGSS (2013), and was configured to experience redesign through the optimization process. To verify effectiveness, the STEAM attitude questionnaire developed by Park et al. (2019) and the creative engineering problemsolving propensity test tool developed by Kang and Nam (2016) were used. A pre and post t-test was used for statistical analysis for the effectiveness test. In addition, the contents of interesting points experienced by the learners were transcribed after receiving descriptive responses, and were analyzed and visualized through degree centrality analysis. Results confirmed that engineering design in science classes had a positive effect on both STEAM attitude and creative engineering problem-solving disposition (p< .05). In addition, as a result of unstructured data analysis, science and engineering knowledge, engineering experience, and cooperation and collaboration appeared as factors in which learners were interested in learning, confirming that engineering experience was the main factor.

• International Area Studies Review
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• v.15 no.1
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• pp.173-193
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• 2011

Most of the research regarding economic effects of policy loans has thus far been focused on whether policy loans can improve the financial status or the management performance of small and medium enterprises (SMEs). Unlike previous researches, this study implemented an empirical analysis focused on the contribution of policy loans to easing the liquidity restriction of investment. To analyze whether investment liquidity restriction can be alleviated or not, this study attempted an empirical analysis utilizing the nonlinear Euler equation induced through optimization of investment and GMM (generalized method of moments) as its analysis methodology. With the SMEs that received policy financing from the Small and medium Business Corporation (SBC) in 2004, this study analyzed three years of panel data before(2001~2003) and after(2004~2006) receipt of policy loans. According to the empirical results, it appears that policy loans had effects on resolving liquidity restriction of investment, implying that policy financing eases the liquidity restriction of SME investment and would contribute to the growth and development of SMEs. Further, I checked robustness of empirical results using Tobin's q model. The empirical results also support that policy loans help to resolve liquidity constraint. With these results, it is understood that the critical view to date, which has emphasized the ineffectiveness of policy financing due to it having no or insignificant economic effects, may be wrong.

• Information Systems Review
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• v.24 no.1
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• pp.39-57
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• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.