• Journal of Microbiology
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• v.43 no.3
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• pp.277-284
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• 2005

The avermectins are composed of eight compounds, which exhibit structural differences at three positions. A family of four closely-related major components, A1a, A2a, B1a and B2a, has been identified. Of these components, B1a exhibits the most potent antihelminthic activity. The coexistence of the '1' components and '2' components has been accounted for by the defective dehydratase of aveAI module 2, which appears to be responsible for C22-23 dehydration. Therefore, we have attempted to replace the dehydratase of aveAI module 2 with the functional dehydratase from the erythromycin eryAII module 4, via homologous recombination. Erythromycin polyketide synthetase should contain the sole dehydratase domain, thus generating a saturated chain at the C6-7 of erythromycin. We constructed replacement plasmids with PCR products, by using primers which had been derived from the sequences of avermectin aveAI and the erythromycin eryAII biosynthetic gene cluster. If the original dehydratase of Streptomyces avermitilis were exchanged with the corresponding erythromycin gene located on the replacement plasmid, it would be expected to result in the formation of precursors which contain alkene at C22-23, formed by the dehydratase of erythromycin module 4, and further processed by avermectin polyketide synthase. Consequently, the resulting recombinant strain JW3105, which harbors the dehydratase gene derived from erythromycin, was shown to produce only C22,23-unsaturated avermectin compounds. Our research indicates that the desired compound may be produced via polyketide gene replacement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.51-58
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• 2020

The use of malware in cyber threats continues to be used in all ages, and will continue to be a major attack method even if IT technology advances. Therefore, researches for detecting such malicious codes are constantly tried in various ways. Recently, with the development of AI-related technology, many researches related to machine learning have been conducted to detect malware. In this paper, we propose a method to detect malware using machine learning. For machine learning detection, we create a feature around each call interval, ie Time Interval, in which API calls occur among dynamic analysis data, and then apply the result to machine learning techniques.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.519-524
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• 2021

Recently, the fourth industrial revolution has become an area of interest to many countries, mainly in major advanced countries. Artificial intelligence technology, the core technology of the fourth industrial revolution, is developing in a form of convergence in various fields and has a lot of influence on the edutech field to change education innovatively. This paper builds an experimental environment using the DTW speech recognition algorithm and deep learning on various native and non-native data. Furthermore, through comparisons with CNN algorithms, we study non-native speakers to correct them with similar pronunciation to native speakers by measuring the similarity of English pronunciation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.328-330
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• 2022

Currently, the Metaverse is introduced in various fields, and a virtual convergence economy that uses NFTs for content or item transactions is expected to develop into a 'metaverse environment'. The 'metaverse environment' will lead the changes in our society in the future and it will be fused with AI, big data, cloud, IoT, block chain, and next-generation network technology. However, personal information, device information, and behavior information provided by Metaverse users to use the service are subject to major attacks. Therefore, in order to provide a safe environment for users to use and to expand the business base of related companies, building a public-private cooperation system and developing a security guide are the leading tasks. Therefore, in this study, we compare and analyze metaverse features and technologies, and examine possible security threats and countermeasures.

• Educational Technology International
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• v.25 no.2
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• pp.331-368
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• 2024

This study aims to examine trends in personalized education in South Korea by analyzing major keywords from big news data using topic modeling techniques. To achieve this objective, we analyzed 19,874 news articles published in South Korea between January 2018 and October 2023. The keywords were categorized into three distinct time periods: January 2018 to December 2019 (Period 1), January 2020 to December 2021 (Period 2), and January 2022 to October 2023 (Period 3). The results reveal distinct keyword trends across the three periods. In Period 1, keywords such as "university," "junior college," "Seoul," and "Samsung Electronics" were prominent. In Period 2, "Corona," "Seoul," and "AI" emerged as significant terms. In Period 3, "government," "AI," "region," "students," and "youth" were identified. These findings indicate a focus on personalized education and competency development at various levels, including local, national, and institutional (universities and colleges). We can confirm the increasing prevalence of personalized education in response to the growing demand for digital and AI technologies, with numerous colleges nationwide promoting these initiatives at a national level. Additionally, the application of personalized education was observed as a measure to support underachieving students, addressing issues such as educational gaps and foundational education. This suggests a blend of both universal and specific approaches to personalized education. Based on these findings, the study recommends that to properly progress this idea, an elaborate theoretical framework that creates a balance between the pedagogical objective of satisfying the requirements of particular learners and adaptive learning technology would be needed.

• The Korean Nurse
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• v.20 no.3 s.111
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• pp.49-57
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• 1981

Current population trends, marked by an increasing accumulation of old members, must be followed by major adjustment in socioeconomic planning since our traditional family structure has been changing as a result of scientific and sociologic advances. Welf

• Current Optics and Photonics
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• v.2 no.1
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• pp.39-46
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• 2018

Visible-light communication (VLC) combined with advanced illumination can be expected to become an integral part of next-generation communication networks. One of the major concerns in VLC implementation is developing resource-allocation schemes in a multi-user scenario. However, the scheduling for heterogeneous quality of service (QoS) traffic has not been studied so far, for the indoor VLC downlink system. In this paper, we creatively introduce effective-bandwidth and effective-capacity theory into the multi-user scheduling (MUS) problem, to guarantee the user's statistical delay QoS. We also take account of the aggregate interference (AI) in the indoor VLC downlink system, and analyze its impact on the user-centric MUS problem for the first time. Simulations show that the AI has a nonnegligible influence on the scheduling result, and that the proposed scheduling scheme could guarantee the user's QoS requirement under the premise of ensuring sum capacity.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.575-579
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• 1990

This paper describes an algorithm that can efficiently analyze the multichannel ECG signal using the frame. The input is a set of significant features (points) which have been extracted from an original sampled signal by using the split-and-merge algorithm. A signal from each channel can be hierarchical ADN/OR graph on the basis of the priori knowledge for ECG signal. The search mechanisms with some heuristics and the mixed paradigms of data-driven hypothesis formation are used as the major control mechanisms. The mutual relations among features are also considered by evaluating a score based on the relational spectrum. For recognition of morphologies corresponding to OR nodes, an hypothesis modification strategy is used. Other techniques such as instance, priority update of prototypes, and template matching facility are also used. This algorithm exactly recognized the primary points and supporting points from the multichannel ECG signals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.285-288
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• 2006

Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build the prediction model for the customer profitability level. Experimental results show that our GA-optimized CBR approach outperforms other AI techniques for this mulriclass classification problem.