• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of Practical Agriculture & Fisheries Research
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• v.25 no.4
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• pp.138-147
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• 2024

Agrobacterium-mediated transformation (AMT) is a method that allows for the stable integration of DNA fragments into the plant genome. Transgenic plants generated through AMT typically exhibit a lower copy number of the transgene compared to those induced by particle bombardment. Furthermore, AMT offers a straightforward and efficient approach for generating transgenic plants. While the transformation efficiency of wheat is comparatively lower than that of other monocot plants such as Rice (Oryza sativa L.) and Maize (Zea mays L.), the cultivars 'Bobwhites' and 'Fielder' are commonly employed for wheat transformation. To date, there have been no reported instances of successful development of transgenic plants using Korean wheat varieties through AMT. This study aims to assess the transformation efficiency of 43 Korean wheat cultivars using the GUS assay, with the goal of identifying suitable Korean wheat cultivars for AMT. The pCAMBIA1301 vector, carrying the β-glucuronidase (GUS) gene, was incorporated into Agrobacterium strain EH105. Following the inoculation of Agrobacterium into immature embryos, GUS assays were conducted 'Saeol', 'Jopum', and 'Jonong' showed 100% (the number of embryos showing GUS spots/the number of embryos used for AMT) among 43 cultivars. In addition, cultivars with more than 70% were 'Saekeumgang', 'Jojung', 'Tapdong', 'Anbaek', 'Dabun', 'Sugang', 'Keumgang', 'Jeokjung', 'Seodun', 'Joeun', 'Dajung', and 'Baekjung'. It seems that the 15 cultivars above showed the possibility of using AMT. On the other hand, 'Yeonbaek', 'Goso', 'Baekgang', and 'Johan' showed less than 20% and GUS spots were not observed in 'Gru', 'Gobun', 'Milseong', and 'Shinmichal-1'. This study explores transient GUS expression in Korean wheat cultivars seven days after AMT. The observed initial high efficiency of transient transformation suggests the potential for subsequent stable transformation efficiency. Korean wheat cultivars demonstrating elevated transient transformation efficiency could serve as promising candidates for the development of stable transgenic wheat.