• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.340-356
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• 2024

Bridging traditional experts' disciplinary boundaries is important for nuclear knowledge management systems. However, expert competences are often described in unstructured texts and require substantial human effort to link related competences across disciplines. The purpose of this research is to develop and evaluate a natural language processing approach, based on Latent Semantic Analysis, to enable the automatic linking of related competences across different disciplines and communities of practice. With datasets of unstructured texts as input training data, our results show that the algorithm can readily identify nuclear domain-specific semantic links between words and concepts. We discuss how our results can be utilized to generate a quantitative network of links between competences across disciplines, thus acting as an enabler for identifying and bridging communities of practice, in nuclear and beyond.

• ETRI Journal
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• v.40 no.2
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• pp.197-206
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• 2018

Multiple-input multiple-output (MIMO) systems offer significant enhancements in terms of their data rate and channel capacity compared to traditional systems. However, correlation degrades the system performance and imposes practical limits on the number of antennas that can be incorporated into portable wireless devices. The use of switched parasitic antennas (SPAs) is a possible solution, especially where it is difficult to obtain sufficient signal decorrelation by conventional means. The covariance matrix represents the correlation present in the propagation channel, and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the "water-pouring algorithm" to modify the covariance matrix, the channel capacity is significantly improved compared to traditional systems, which spread transmit power uniformly across all the antennas. A condition number is also proposed as a selection metric to select the optimal pattern configuration for MIMO-SPAs.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Korean journal of food and cookery science
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• v.28 no.3
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• pp.337-347
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• 2012

This study was performed through one-to-one interviews or group sessions with single un-married foreigners living and working in Korea. A total of 150 questionnaires were distributed, and 109 were analyzed. This demographic becomes immersed in local cuisine searching for new food dishes and experiences. These foreigners were an excellent test group who came from a non-Korean domestic cuisine background and who were subsequently exposed to Korean cuisine over an extended period during which changes occurred in their diets and food preferences. We found that the longer the foreigners stayed in Korea, the more they ate Korean food and adapted to traditional dishes such as Korean stews and hot-pots. In general, Chinese, Japanese, and South-east Asians regarded Korean food as nutritious, whereas people from a western or European background considered it health conscious food. In contrast, South-east Asians, in particular, found Korean food aroma unpleasant. This study describes how diets and views of Korean food evolved in foreigners with time by providing objective opinions and data on the acceptance of Korean food by foreigners and hints the direction in which Korean cuisine should be developed to further its globalization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1916-1921
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• 2008

This paper presents the temporal classification method based on data mining techniques for discovering knowledge from measured load patterns of distribution transformers. Since the power load patterns have time-varying characteristics and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Therefore, we propose a temporal classification rule for analyzing and forecasting transformer load patterns. The main tasks include the load pattern mining framework and the calendar-based expression using temporal association rule and 3-dimensional cube mining to discover load patterns in multiple time granularities.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1087-1089
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• 2003

The aim of this study is to evaluate the Pi-SAR data for land cover discrimination using a standard method. For this purpose, the original polarization and Pauli components of the Pi-SAR X-band and L-band data are used and the results are compared. As a method for the land cover discrimination, the traditional method of statistical maximum likelihood decision rule is selected. To increase the accuracy of the classification result, different spatial thresholds based on local knowledge are determined and used for the actual classification process. Moreover, to reduce the speckle noise and increase the spatial homogeneity of different classes of objects, a speckle suppression filter is applied to the original Pi-SAR data before applying the classification decision rule. Overall, the research indicated that the original Pi-SAR polarization components can be successfully used for separation of different land cover types without taking taking special polarization transformations.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.205-212
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• 2016

Recently, researches for the integration and standardization of clinical data in the Western medicine and Korean medicine is in progress. If an integration of similar clinical data as well as heterogeneous clinical data is possible based on one standardization, we can able to derive implicit medical knowledge from integrated clinical data. In this paper, we implemented Korean clinical database based on internationally known CDISC standardization to efficiently store Korean clinical data and constructed E-CRF system for convenient data input in clinical sites. Furthermore, we showed example of an integration of Korean clinical data from 4 clinical sites. The results of our study will help to establish the foundation for the extraction of implicit medical knowledge from integrated clinical data. Also, our results may support efficient management through data integration, prevention of repetitive or unnecessary clinical trials, facilitation of collaborative study and convenient research through the distribution of refined clinical data.

• The Korean Journal of Mycology
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• v.40 no.1
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• pp.39-43
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• 2012

The knowledge of native wild mushrooms traditionally using in Jeju Island, that have specific native environment and various biodiversity, was investigated. From Jeju and Seogwipo cities including 7 provinces 4 locals, so total 50 villages, data was collected in the entire area and voice of the 93 elderly people between the age of fifty and ninety were recorded. As a result, total 23 native wild mushrooms were used in which 2 species were poisonous mushrooms, 7 species were medicinal mushrooms, 2 species was used for folk religion, 2 species were not used for living but for attention and 12 species were edible mushrooms. Also, a total of 267 cases of traditional knowledge was collected, in which 197 cases were about edible mushrooms for 12 species, 43 cases were about poisonous mushrooms for 2 species, 16 cases were about medicinal mushrooms for 7 species. Interestingly, the fortune for agriculture was told depending on spore mass release of Cyathus stercoreus which grows in animal feces and compost. It is considered as the distinctive traditional knowledge of Jeju Island.