• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.

• Journal of Families and Better Life
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• v.11 no.1
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• pp.107-118
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• 1993

A dimension of well-being economic security was analyzed and compared with economic adequacy. Again it was tested whether two indicators of economic security(short-term vs. long-term) yield same distribution across all household groups. Economic Security was defined as the household ability to sustain a given level of consumption in the case of economic emergency; specifically loss of income. Measure of 8 different kinds of economic security were constructed from household net worth including and excluding home equity. Data were taken from the 1988 U.S. Consumer Expenditure Survey and 2148 households were selected to test hypotheses concerning the economic security of American households Empirical results showed a very low level of economic security in general. The first hypothesis that distribution of economic adequacy and economic security are same across all population groups was rejected. On the average security measure rather than adequacy measure was favor to white female-headed households and households who have old and highly educated house-holder. The second hypothesis that the indicators of long-term and short-term economic security yield the same results across all household was not rejected. In general the level of economic security was relatively higher when long-term indicator was used than short-term indicator was however the direction and relative size of effect of income and each control variable was almost same.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.212-217
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• 2009

There are rule-based learning method and statistic based learning method and so on which represent learning method for hierarchy relation between domain term. In this paper, we propose to leveling and similarity measure using the extended AHP of fuzzy term in Information system. In the proposed method, we extract fuzzy term in document and categorize ontology structure about it and level priority of fuzzy term using the extended AHP for specificity of fuzzy term. the extended AHP integrates multiple decision-maker for weighted value and relative importance of fuzzy term. and compute semantic similarity of fuzzy term using min operation of fuzzy set, dice's coefficient and Min+dice's coefficient method. and determine final alternative fuzzy term. after that compare with three similarity measure. we can see the fact that the proposed method is more definite than classification performance of the conventional methods and will apply in Natural language processing field.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.759-762
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• 1999

The concrete structures possessing good structural integrity can face durability problems due to deteriorations of concrete structures under various environmental conditions. The durability problems weaken the structural integrity in the long run. Especially, the excessive use of sea sand causes serious reinforcement corrosion and carbonation in concrete structures. An accelerated test is often used to predict deterioration as a qualitative measure, but without long term exposure test results or understanding of the relationship between the accelerated test and the long term exposure test, the accelerated test result alone can not be used effectively as a quantitative measure. In this paper, a methodology is proposed to predict the long term deteriorations, based on the result of the short-term accelerated test, of concrete containing different contents of chloride ions. Then, the correlation between two results on the steel corrosion ratio and the carbonation depth is analyzed for concrete with different chloride contents.

• New & Renewable Energy
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• v.4 no.4
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.87-93
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• 2012

This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.

• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.253-270
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• 2021

Purpose - The main purpose of this study is to measure and evaluate the level of national liabilities that Korea's national finances can afford. Specifically, the concepts of national debt and national liability are clarified, and the appropriate level of national liabilities is measured in terms of short-term fiscal crisis, mid-to-long-term fiscal crisis, and GDP. Based on these measurements of fiscal crisis, this study would like to propose national fiscal management plans. Design/methodology/approach - In order to clearly recognize the difference between the national debt and the national liability, this study examines the data from 2013 to 2020. In addition, this study uses data from the national financial statements from 2013 to 2018 to measure the appropriate level of national liabilities in terms of fiscal crisis management. Findings - Short-term fiscal crises, measured by current ratios, will not occur. Nevertheless, in view of the cash flow compensation ratio, the short-term bankruptcy of the national finances of Korea depends on the re-borrowing of short-term borrowings and current and long-term borrowings. In addition, in order to manage the mid-to long-term financial crisis, it is necessary to pay attention to the liability growth rate rather than the liability size. Research implications or Originality - While previous studies focused on the appropriate level of national debt, this study was differentiated as a study focused on the level of national liability coverage. It is expected that the results of this study will be used to manage the national fiscal soundness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.467-474
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• 2005

This paper presents a systematic method to compute a reliability measure for a short term electrical load forecasting system using neuro-fuzzy models. It has been realized that the reliability computation is essential for a load forecasting system to be applied practically. The proposed method employs a local reliability measure in order to exploit the local representation characteristic of the neuro-fuzzy models. It, hence, estimates the reliability of each fuzzy rule learned. The design procedure of the proposed short term load forecasting system is as follows: (1) construct initial structures of neuro-fuzzy models, (2) store them in the initial structure bank, (3) train the neuro-fuzzy model using an appropriate initial structure, and (4) compute load prediction and its reliability. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results suggest that the proposed scheme extends the applicability of the load forecasting system with the reliably computed reliability measure.