• Research in Plant Disease
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• v.16 no.3
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• pp.238-246
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• 2010

In 2006 fall, a preliminary survey of viruses in two important medicinal plants, Cynanchum wilfordii and C. auriculatum, was conducted on the experimental fields at the Agricultural Research and Extension Services of Chungbuk province in Korea. On each experimental fields, percentage of virus infection was ranged from 20 to 80%, and especially an average of disease incidence propagated by roots was twice higher than that by seeds. The various symptoms were observed in Cynanchum spp. plants, such as mosaic, mottle, necrosis, yellowing, chlorotic spot and malformation etc. In electron microscopic examination of crude sap extracts, filamentous rod particles with 390-730 nm were observed in most samples. The virus particles were purified from the leaves of C. wilfordii with typical mosaic symptom, and the viral RNA was extracted from this sample containing 430-845 nm long filamentous rod. To identify the viruses, reverse transcription followed by PCR with random primers was carried out. The putative sequences of P3 and coat protein of potyvirus were obtained. From a BLAST of the two sequences, they showed 26-38% and 62-72% identities to potyviruses, respectively. In SDS-PAGE analysis, the subunit of coat protein was approximately 30.3 kDa, close to the coat protein of potyvirus. In bioassay with 21 species in 7 families, Chenopodium quinoa showed local lesion on inoculated leave and chlorotic spot on upper leave, but the others were not infected. RT-PCR detection using specific primer of C. wilfordii and C. auriculatum samples, all of 24 samples with virus symptom was positive, and five out of seven samples without virus symptom were also positive. On the basis of these data, the virus could be considered as a new member of potyvirus. We suggested that the name of the virus was Keunjorong mosaic virus (KjMV) after the common Korean name of C. wilfordii.

• Economic and Environmental Geology
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• v.45 no.4
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• pp.385-396
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• 2012

The geochemical high-grade uranium anormal zone has been reported in the Shinbo mine and its eastern areas, Jinan-gun, Jeollabuk-do located in the southwestern part of Ogcheon metamorphic zone, Korea. In this paper is reported the time-relationship between deformation and growth of metamorphic minerals in the eastern area of Shinbo mine, which consists of the Precambrian metasedimentary rocks (quartzite, metapelite, metapsammite) and the age-unknown pegmatite and Cretaceous porphyry which intrude them, and is considered the relative mineralization time on the basis of the previous research's result. The D1 deformation formed the straight-type Si internal foliation which is defined mainly as the arrangement of elongate quartz, biotite, opaque mineral in andalusite porphyroblast. The D2 deformation, which is defined by the microfolding of Si foliation, formed S2 crenulation cleavage. It can be divided into two sub-phases, early crenulation and late crenulation. The former occurs as the curvetype Si foliation in the mantle part of andalusite. The latter occurs as S1-2 composite foliation which warps around the andalusite. The andalusite porphyroblast began to grow under non-deformation condition after the formation of S1 foliation which corresponds to the straight-type Si foliation. It continued to grow before the late crenulation phase. The age-unknown pegmatite intruded after the D2 deformation and grew the fibrous sillimanite which random masks the S1-2 composite foliation. The D3 deformation formed F3 fold which folded the S1-2 composite foliation, D2 crenulation, fibrous sillimanite. It means that the intrusion of pegmatite related to the growth of the fibrous sillimanite took place during the inter-tectonic phase of D2 and D3 deformations. The retrograde metamorphism is recognized by the chloritization of biotite and two-way cleavage lamellae which is parallel to the S1-2 composite foliation and the F3 fold axial surface in the andalusite porphyroblast. It occurred during the D2 late crenulation phase and D3 deformation. In considering of the previous research's result inferring the most likely candidate for the uranium source rock as pegamatite, it indicates that the age-unknown pegmatite intruded during the inter-tectonic phase of D2 and D3 deformations, i.e. during the retrograde metamorphism related to the uplifting of crust, and formed the uranium ore zone around the Shinbo mine.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.283-298
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• 2022

In this study, we investigated the mineral geochemistry of the albite-spodumene pegmatite, associated exogreisen, and wall rock from the Boam Li deposit, Wangpiri, Uljin, Gyeongsangbuk-do, South Korea. The paragenesis of the Boam Li deposit consists of two stages; the magmatic and endogreisen stages. In the magmatic stage, pegmatite dikes mainly composed of spodumene, albite, quartz, and K-feldspar intruded into the Janggun limestone formation. In the following endogreisen stage, the secondary fine-grained albite along with muscovite, apatite, beryl, CGM(columbite group mineral), microlite, and cassiterite were precipitated and partly replaced the magmatic stage minerals. Exogreisen composed of tourmaline, quartz, and muscovite develops along the contact between the pegmatite dike and wall rock. The Cs contents of beryl and muscovite and Ta/(Nb+Ta) ratio of CGM are higher in the endogreisen stage than the magmatic stage, suggesting the involvement of the more evolved melts in the greisenization than in the magmatic stage. Florine-rich and Cl-poor apatite infer that the parental magma is likely derived from metasedimentary rock (S-type granite). P₂O₅ contents of albite in the endogreisen stage are below the detection limit of EDS while those of albite in the magmatic stage are 0.28 wt.% on average. The lower P₂O₅ contents of the former albite can be attributed to apatite and microlite precipitation during the endogreisen stage. Calcium introduced from the adjacent Janggun formation may have induced apatite crystallization. The interaction between the pegmatite and Janggun limestone is consistent with the gradual increase in Ca and other divalent cations and decrease in Al from the core to the rim of tourmaline in the exogreisen.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.