• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.232-241
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• 2000

This study suggests an integrated method based on concept hierarchies, information gain, and rough set theory for efficient discovery rules from a large amount of data, and implements an optimal rules discovery system. Our approach applies attribute-oriented concept ascension technique to extract generalized knowledge from a database, knowledge reduction technique to remove superfluous attributes and attribute values, and significance of attributes to induce optimal rules. The system first reduces the size of database by removing the duplicate tuples through the condition attributes which have no influences on the decision attributes, and finally induces simplified optimal rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes. And we induce some decision rules from actual data by using the system and test rules to new data, and evaluate that the rules are well suited to them.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2365-2372
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• 1999

The reusability measurement model should satisfy the following conditions : 1) can insert and delete metrics and components easily, 2) can compare and evaluate components quantitatively on the basis of validation, 3) don't require certain preassumed knowledge, and 4) can compute significance of each measurement attribute objectively. Therefore, in this paper, we propose a new reusability measurement model that can satisfy the above requirements. Our model selects the appropriate measurement attributes and calculates the relative significance of them by using rough set. Then, in order to measure the reusability of component, it integrates the significance of attributes and the measured value of them by using fuzzy integral. Finally, we apply our model to the reusability measurement of the function-oriented components and validate our model through statistical technique.

• Journal of the Korean housing association
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• v.25 no.3
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• pp.105-114
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• 2014

Landscape zones have been designated as aesthetic old town districts across a wide range of Nakakyo-Ku and Shimokyo-Ku, city center of Kyoto, Japan. In these districts in which traditional structures and new buildings coexist, regulations of restriction on acts such as new building's heights, shapes, materials, and colors are carried out according to local governmental landscape ordinance based on Scenic Conservation Act. And yet, minimal fulfillment of the regulations according to different designer's subjective interpretation and principle of economy is rather creating abnormal shapes not harmonized with the traditional landscape. Thus, this study aims to extract combinations between form elements of middle and high rise apartment facade that affects 'harmony' and 'mismatch' in the districts by clarifying the social rules commonly implied based on intuitive judgments (sensibility evaluation) in which human experiential knowledge is involved. As research methods, the study first analyzes the form elements of the facade through a field survey, sets up a standard model through tasks of classification and segmentation and draws computer graphic images with 99 different patterns based on it. Based on these images, this study carries out sensibility evaluation and analyzes experimental data applying the rough set theory. As a result of the analysis, the combinations of form elements that affect harmony or mismatch act greatly when the colors and shapes of the pillars, positions and the patterns of the use of the first floor are combined.

• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.203-210
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• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.460-465
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• 2007

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.10
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• pp.788-793
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• 2017

This paper presents the verification of accuracies of theoretical models for calculating the microwave reflections from rough sea surfaces. First of all, the Pierson-Moskowitz ocean spectrum was used to generate the rough sea surfaces. Then the relationship between the significant wave heights, root-mean-square(RMS) heights and wind speed was derived by estimating the significant wave heights and RMS heights of the generated sea surfaces according to various wind speeds, and compared the derived relationship with other measurement data sets. The reflection coefficients of the sea surfaces were calculated by using a numerical method(the moment method). Then, the numerical results were compared with Ament model, PO(Physical Optics) model, GO(Geometrical Optics) model and B-M(Brown-Miller) model for various roughness conditions(wind speed) and incidence angles. It was found that the Ament model is not accurate except for a very low roughness conditions($kh_{rms}$<0.4, k is wavenumber and $h_{rms}$ is RMS height). It was also found that at incidence angles lower than $70^{\circ}$, the PO and the GO models agree well with the numerical results, while the B-M model agrees well with the numerical analysis results at incidence angles higher than $80^{\circ}$ for very rough sea surfaces with $kh_{rms}$>10.

• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.243-252
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• 2018

In a satellite gravimetry mission similar to GRACE, the precision of inter-satellite ranging is one of the key factors affecting the quality of gravity field recovery. In this paper, the impact of ranging precision on the accuracy of recovered geopotential coefficients is analyzed. Simulated precise orbit determination (POD) data and inter-satellite range data of formation-flying satellites containing white noise were generated, and geopotential coefficients were recovered from these simulated data sets using the crude acceleration approach. The accuracy of the recovered coefficients was quantitatively compared between data sets encompassing different ranging precisions. From this analysis, a rough prediction of the accuracy of geopotential coefficients could be obtained from the hypothetical mission. For a given POD precision, a ranging measurement precision that matches the POD precision was determined. Since the purpose of adopting inter-satellite ranging in a gravimetry mission is to overcome the imprecision of determining orbits, ranging measurements should be more precise than POD. For that reason, it can be concluded that this critical ranging precision matching the POD precision can serve as the minimum precision requirement for an on-board ranging device. Although the result obtained herein is about a very particular case, this methodology can also be applied in cases where different parameters are used.