• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1257-1260
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• 2005

This paper is related to optimization in the image data format, which can make a great effect in performance of data compression and is based on the wavelet transform and JPEG2000. This paper established a criterion to decide the data format to be used in wavelet transform, which is on the bases of the data errors in frequency transform and quantization. This criterion has been used to extract the optimal data format experimentally. The result were (1, 9) of 10-bit fixed-point format for filter coefficients and (9, 7) of 16-bit fixed-point data format for wavelet coefficients and their optimality was confirmed.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.1
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• pp.28-43
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• 1988

The purpose of this research is to produce bin weather data for Seoul from Standard Weather Data. The intended use of these data is for input to recently developed models for simplified energy calculations and for generating variable-base degree-day information. The data produced under this study include $3^{\circ}C$ bin data covering the full range of dry-bulb temperatures with mean coincident wet-bulb and daytime coincident solar radiation, wet-bulb bins down to freezing temperature, wind speed bins with prevailing directions, and heating and cooling degree hours to nine different temperature bases. All of these data are tabulated in six separate time periods and total daily categories for monthly and annual periods.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.937-942
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• 2008

In this paper, new methodology is proposed to estimate the cooling load using design parameters of building and predicted weather data. Only two parameters such as maximum and minimum temperature are necessary to obtain hourly distribution of cooling load for the next day. The maximum and minimum temperature that are used for input parameters can be obtained from forecasted weather data. Benchmarking building(research building) is selected to validate the performance of the proposed method, and the estimated cooling loads in hourly bases are calculated and compared with the measured data for benchmarking building. The estimated results show fairly good agreement with the measured data for benchmarking building.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.27-41
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• 2016

Named entity recognition is to classify elements in text into predefined categories and used for various departments which receives natural language inputs. In this paper, we propose a method which can generate named entity training corpus automatically using knowledge bases. We apply two different methods to generate corpus depending on the knowledge bases. One of the methods attaches named entity labels to text data using Wikipedia. The other method crawls data from web and labels named entities to web text data using Freebase. We conduct two experiments to evaluate corpus quality and our proposed method for generating Named entity recognition corpus automatically. We extract sentences randomly from two corpus which called Wikipedia corpus and Web corpus then label them to validate both automatic labeled corpus. We also show the performance of named entity recognizer trained by corpus generated in our proposed method. The result shows that our proposed method adapts well with new corpus which reflects diverse sentence structures and the newest entities.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.521-528
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• 2002

We propose a new approach to the feature selection for the classification of handwritten characters using two-dimensional(2D) wavelet packet bases. To extract key features of an image data, for the dimension reduction Principal Component Analysis(PCA) has been most frequently used. However PCA relies on the eigenvalue system, it is not only sensitive to outliers and perturbations, but has a tendency to select only global features. Since the important features for the image data are often characterized by local information such as edges and spikes, PCA does not provide good solutions to such problems. Also solving an eigenvalue system usually requires high cost in its computation. In this paper, the original data is transformed with 2D wavelet packet bases and the best discriminant basis is searched, from which relevant features are selected. In contrast to PCA solutions, the fast selection of detailed features as well as global features is possible by virtue of the good properties of wavelets. Experiment results on the recognition rates of PCA and our approach are compared to show the performance of the proposed method.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.4
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• pp.379-385
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• 1998

The Holzworth's method was applied to estimate the daily maximum mixing height (DMMH) in Pohang, Osan and Kwangju areas. The data-base were established with meteorological data collected at air bases in these areas during the period 1983∼1992. It was investigated the seasonality, monthly trends and occurrence frequencies of the estimated DMMH data in each area. The estimated mean DMMH were found in the range of 1,100 m (winter) to 1,450m (spring). These mean DMMH data showed a typical seasonality in which higher values are commonly seen during spring and fall, while lower values during summer and winter seasons. An occurrence of estimated mean DMMH which in the range of 1,000∼2,000m altitude was appeared to be about 60%.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.1-20
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• 2001

The amount of financial information in sophisticated large data bases is huge and makes interfirm performance comparisons very difficult or at least very time consuming. The purpose of this paper is to investigate whether neural networks in the form of self-organizing maps (SOM) can be successfully employed to manage the complexity for competitive financial benchmarking. SOM is known to be very effective to visualize results by projecting multi-dimensional financial data into two-dimensional output space. Using the SOM, we overcome the problems of finding an appropriate underlying distribution and the functional form of data when structuring and analyzing a large data base, and show an efficient procedure of competitive financial benchmarking through clustering firms on two-dimensional visual space according to their respective financial competitiveness. For the empirical purpose, we analyze the data base of annual reports of 100 Korean listed companies over the years 1998, 1999, and 2000.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.255-256
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• 2016

In this paper, we analyze the operations and/or status of our structure which builds the management structure measuring integrated monitoring system based on linked open data in a short term or long term bases. We have applied a novel analyzing method of linked open data to expect what movements can be occurred in the structure, and we improve the monitoring system using an integrated design to solve the drawbacks of conventional types of monitoring. And collecting data through cloud and their reliability can be proved by evaluation of soundness of data amount and their confidence.

• ETRI Journal
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• v.42 no.2
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• pp.239-246
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• 2020

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.