• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.205-208
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers devote their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new classification method for remote sensed image data that use rough set theory. Using indiscernibility relation of rough sets, we show that can classify image data very easily.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.243-251
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• 2000

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause the inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.201-204
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• 2002

Kansei design knowledge acquisition stage is a crucial stage in kansei designing process and kansei engineering (KE) methodology. In kansei engineering methodology, it is essential to extract design knowledge or rules on relationships between customer's kansei and product design element. We attempt to construct a more powerful melted for extracting the design rules from kansei expremental data. We constucted a kansei experiment concerning color kansei evaluation, and analyzed the sane data by both conventional quantification theory type I and rough set theory. Finally, we compared the effectiveness of both methods for extracting rules and examined the extensions of rough set theory in kansei engineering.

• 산업기술연구
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• 제31권B호
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• 산업경영시스템학회지
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• 제37권4호
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• pp.202-211
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• 2014

Pairs trading is a type of arbitrage investment strategy that buys an underpriced security and simultaneously sells an overpriced security. Since the 1980s, investors have recognized pairs trading as a promising arbitrage strategy that pursues absolute returns rather than relative profits. Thus, individual and institutional traders, as well as hedge fund traders in the financial markets, have an interest in developing a pairs trading strategy. This study proposes pairs trading rules (PTRs) created from a price ratio between securities (i.e., stock index futures) using rough set analysis. The price ratio involves calculating the closing price of one security and dividing it by the closing price of another security and generating Buy or Sell signals according to whether the ratio is increasing or decreasing. In this empirical study, we generate PTRs through rough set analysis applied to various technical indicators derived from the price ratio between KOSPI 200 and S&P 500 index futures. The proposed trading rules for pairs trading indicate high profits in the futures market.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• 소성∙가공
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• 제25권2호
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• pp.102-108
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• 2016

The aim of the current study was to develop an advanced prediction model for the slab width spread during hot rough rolling. Rough rolling consists of both vertical rolling using a set of profiled edger rolls and horizontal rolling using a set of plain work rolls. FE-simulations were performed to investigate the influences of process variables such as initial slab width, initial thickness, sizing draft, edger roll draft and work roll draft on the final slab width variation. From a statistical analysis of the simulation results, an advanced model, which can predict the slab width spread during the edger rolling and horizontal rolling, was developed. The experimental hot rolling trials showed that the newly developed model provided fairly accurate predictions on the slab width spread during hot rough rolling process using a profiled edger rolls.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.3-19
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• 1996

• 디지털융복합연구
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• 제11권10호
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• pp.387-394
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• 2013

데이터 마이닝은 예측이나 분석을 위해서 많은 양의 데이터에 존재하는 여러 가지의 관계를 추출하는 과정이라고 할 수 있다. 그러한 데이터에는 매우 많은 변수로 인한 차원의 증가로 인하여 계산상의 어려움이 수반되어지고 변수의 중복성과 중요도에 있어서 다양한 통계적 관계가 존재한다. 따라서 동일하거나 유사한 데이터를 같은 그룹으로 형성하는 클러스터 해석은 데이터 마이닝에서 필수적인 요소이다. 본 연구는 범주형 데이터의 분류에서 발생하는 불확실성의 처리를 위해 러프집합을 이용하여 정보 엔트로피를 이용한 새로운 척도를 정의하고 연구 대상에 대한 유사행동을 분석하는 시스템 구현에 그 의의가 있다. 데이터는 평택공업고등학교에서 채집되었고 이를 토대로 제안된 방법이 학생들의 유사행동에 대한 보다 정확한 결과를 보임을 알 수 있었다. 또한 속성의 개수가 10개 이상인 경우에 기본 방법과의 차이를 보이며 취업의사결정에서 학생들의 의식을 기존 방법보다 효과적으로 반영하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.252-255
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• 1997

In this paper, we will propose the methodology of data analysis using the fuzzy property set model. In real world, the data can be represented with the object. $\theta$. and the property, $\pi$, and its has-property relation, P. Then, the conceptual space can be defined with the chosen properties. Each object has a unique location in the conceptual space. In Fuzzy mode, the fuzzy property, and fuzzy conceptual space can be redefined. To analyze data using the fuzzy property set model, the rough set need to be defined in the fuzzy conceptual space.