• The Journal of Information Systems
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• v.30 no.1
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• pp.65-84
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• 2021

Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.

• The Journal of Internal Korean Medicine
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• v.43 no.4
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• pp.720-737
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• 2022

Objectives: To compare the effectiveness and safety of East Asian traditional medicine treatments (EATMT) versus conventional management in patients following heart valve replacement surgery. Methods: We searched several databases, including the Korean Studies Information Service System, PubMed, China National Knowledge Infrastructure, and Citation Information by NII. The search range included randomized controlled trials from each first issue until June 27, 2021. Two review authors independently extracted the data. We assessed the risk of systematic errors by evaluating risk domains using the "Risk of bias" tool. Results: We included 5 trials in the review. In the EATMT, the investigators reported significant improvements in reshaping of the heart structure: left ventricular end diastolic diameter (MD -4.43, 95% CI -6.06 to -2.79; 130 participants; 2 studies; high evidence). Comparisons with usual care revealed a significant decrease in gastrointestinal complications rate (OR 0.30, 95% CI 0.20 to 0.47; 503 participants; 2 studies; high evidence). We assessed 4 studies as having a low risk of bias and 1 study as having a high risk of bias. Conclusion: This systematic review suggests that East Asian traditional medicine interventions may be effective in preventing and alleviating complications, but we found evidence of important trade-offs between known benefits and known adverse effects in cardiac dysfunction and inflammation following heart valve replacement. Consequently, additional high-quality studies should be conducted.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of Broadcast Engineering
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• v.11 no.3 s.32
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• pp.363-378
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• 2006

The Digital Multimedia Broadcasting(DMB) system is developed to offer high quality audio-visual multimedia contents to the mobile environment. The system adopts MPEG-4 standard for the main video, audio and other media format. It also adopts the MPEG-4 scene description for interactive multimedia contents. The animated and interactive contents can be actualized by BIFS(Binary Format for Scene), the binary format for scene description that refers to the spatio-temporal specifications and behaviors of the individual objects. As more interactive contents are, the scene description is also needed more high bitrate. However, the bandwidth for allocating meta data such as scene description is restrictive in mobile environment. On one hand, the DMB terminal starts demultiplexing content and decodes individual media by its own decoder. After decoding each media, rendering module presents each media stream according to the scene description. Thus the BIFS stream corresponding to the scene description should be decoded and parsed in advance of presenting media data. With these reason, the transmission delay of BIFS stream causes the delay of whole audio-visual scene presentation although the audio or video streams are encoded in very low bitrate. This paper presents the effective optimization technique for adapting BIFS stream into expected MPEG-2 TS bitrate without any bandwidth waste and avoiding the transmission delay of the initial scene description for interactive DMB contents.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.228-232
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• 2006

Ontology를 이용한 분산 시각미디어 검색 프레임워크인 HERMES(The Retrieval Framework for Visual Media Service)[1][2]는 보다 정확한 시각미디어 정보를 제공하고 웹서비스(Web Services)를 적용하여 HERMES/Provider[1][2]의 자율성을 보장한다. 웹기반의 분산 환경에서 Visual Media Data에 대한 지능적인 검색을 위하여 Meta Data와 Ontology를 이용하고 이기종간 통신을 위한 웹서비스를 제공하는 HERMES/ Broker[1][2]에서 예상치 못한 문제가 발생할 경우 문제를 해결할 수 있는 방법이 제시되지 않았다. 일반적으로 웹 서비스를 제공하는 서버에서 발생되는 결함은 해당 웹 서비스를 이용하여 개발되는 어플리케이션의 갑작스런 중단이나 오류의 원인이 된다. 따라서 결함을 해결할 수 있는 대책이 필요하며 HERMES의 Broker 서버 또한 웹 서비스의 결함이 발생하더라고 이를 효과적으로 해결하여 클라이언트에게 웹 서비스를 정상적으로 제공할 수 있는 결함허용 시스템 도입이 매우 중요하다. 때문에 HERMES 프레임워크가 클라이언트에게 신뢰성과 안정성이 보장된 웹 서비스의 제공을 위해서 Broker 서버에서 발생할 수 있는 결함을 효과적으로 극복할 수 있는 메커니즘이 필요하다. 본 논문에서는 Broker 서버 에서 웹 서비스와 관련된 결함이 발생하더라고 올바르게 운영될 수 있으며 분산 이미지 검색 프레임워크인 HERMES의 구조적 특성에 적합한 결함허용 시스템 설계 기법을 제안하여 HERMES 프레임워크가 클라이언트에게 투명성 있는 서비스를 제공하고 높은 신뢰성과 안정성이 확보될 수 있도록 구성하고자 한다. Query 수행을 여러 서버로 분산처리하게 함으로써 성능에 대한 신뢰성을 향상 시킬 수 있는 Load Balancing System을 제안한다.할 때 가장 효과적인 라우팅 프로토콜이라고 할 수 있다.iRNA 상의 의존관계를 분석할 수 있었다.수안보 등 지역에서 나타난다 이러한 이상대 주변에는 대개 온천이 발달되어 있었거나 새로 개발되어 있는 곳이다. 온천에 이용하고 있는 시추공의 자료는 배제하였으나 온천이응으로 직접적으로 영향을 받지 않은 시추공의 자료는 사용하였다 이러한 온천 주변 지역이라 하더라도 실제는 온천의 pumping 으로 인한 대류현상으로 주변 일대의 온도를 올려놓았기 때문에 비교적 높은 지열류량 값을 보인다. 한편 한반도 남동부 일대는 이번 추가된 자료에 의해 새로운 지열류량 분포 변화가 나타났다 강원 북부 오색온천지역 부근에서 높은 지열류량 분포를 보이며 또한 우리나라 대단층 중의 하나인 양산단층과 같은 방향으로 발달한 밀양단층, 모량단층, 동래단층 등 주변부로 NNE-SSW 방향의 지열류량 이상대가 발달한다. 이것으로 볼 때 지열류량은 지질구조와 무관하지 않음을 파악할 수 있다. 특히 이러한 단층대 주변은 지열수의 순환이 깊은 심도까지 가능하므로 이러한 대류현상으로 지표부근까지 높은 지온 전달이 되어 나타나는 것으로 판단된다.의 안정된 방사성표지효율을 보였다. $^{99m}Tc$-transferrin을 이용한 감염영상을 성공적으로 얻을 수 있었으며, $^{67}Ga$-citrate 영상과 비교하여 더 빠른 시간 안에 우수한 영상을 얻을 수 있었다. 그러므로 $^{99m}Tc$-transierrin이 감염 병소의 영상진단에 사용될 수

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.549-564
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• 2017

Not only services that are provided by a single system have been various with the development of the Internet of Things and autonomous software but also new services that are not possible before are provided through collaboration between systems. The collaboration between autonomous systems is similar to the ecosystem configuration in terms of biological viewpoints. Thus, it is called the IT Ecosystem, and this concept has arisen newly in recent years. The IT Ecosystem refers to a concept that achieves a mission of each of a number of heterogeneous systems rather than a single system utilizing their own autonomy as well as achieving the objectives of the overall system simultaneously in order to meet a single common goal. In our previous study, we proposed architecture of elementary level and as well as basic several meta-models to implement the IT Ecosystem. This paper proposes comprehensive reference architecture framework to implement the IT Ecosystem by cleansing the previous study. Among them, a utility function based on cost-benefit model is proposed to solve the dynamic re-configuration problem of system components. Furthermore, a measure of using genetic algorithm is proposed as a solution to reduce the dynamic re-configuration overhead that is increased exponentially according to the expansion of the number of entities of components in the IT Ecosystem. Finally, the utilization of the proposed orchestration framework is verified quantitatively through probable case studies on IT Ecosystem for unmanned forestry management.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.94-101
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• 2000

User adaptive reception of broadcast programs includes the functionality such as the user adaptive filtering and browsing functionality. The user adaptive filtering means that the user can limit the list of programs to include only his/her favorite programs among hundreds of available programs. The user adaptive browsing means that the user can view a short summary of his/her selection in the way that he/she prefers. When the receiving system include the random access storage device, the automatic recording functionality of users favorite programs can be included. The user adaptive reception requires support from various meta-data such as user preference data and content description data. TV Anytime forum is a standardization effort to enable user adaptive TV reception, which means that the user can watch what s/he wants when s/he want in the way s/he wants. MPEG-7 includes not only the content description for broadcast applications but also other content descriptions such as structure information. This paper addresses the relationship between MPEG-7 and TV Anytime and investigates how MPEG-7 should be designed and be used to satisfy the requirements of the user adaptive reception of broadcast program.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.