• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.619-632
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• 2022

In this study, we examine rough ${\Delta}\mathcal{I}$-statistical convergence for difference sequences as an extension of rough convergence. We investigate the set of rough ${\Delta}\mathcal{I}$-statistical limit points of a difference sequence and analyze the results with proofs.

• Korean Journal of Mathematics
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• v.32 no.1
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• pp.121-135
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• 2024

In this paper, we have presented rough ideal statistical convergence of sequence on neutrosophic normed spaces as a significant convergence criterion. As neutrosophication can handle partially dependent components, partially independent components and even independent components involved in real-world problems. By examining some properties related to rough ideal convergence in these spaces we have established some equivalent conditions on the set of ideal statistical limit points for rough ideal statistically convergent sequences.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.337-339
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• 1996

In this paper, we construct rough relational database model using approximation concepts of rough set. Also, we analyze the relation between objects, attributes and attribute values and, propose the method that can generate flexible retrieval results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.672-674
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• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.321-326
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• 1997

소프트웨어 재사용은 새로운 소프트웨어 개발에 소용되는 시간과 비용을 현저히 감소시켜 소프트웨어 개발환경과 생산성을 향상시키는 방법으로, 소프트웨어 위기를 해결하기 위한 중요한 방법이다. 그러나 소프트웨어 부품을 위한 형식 명세서(formal specification)의 부족, 소프트웨어 재사용에 대한 부정적 심리적인 효과 등의 이유 때문에 현실적으로 재사용이 잘 이루어지고 있지 않다. 이러한 문제들을 해결하기 위해서는 부품의 품질 보증에 관한 연구가 소프트웨어 재사용에 관한 연구 분야에서 최우선적으로 이루어져야 하지만, 기존의 연구들은 일반적으로 설정된 재사용 품질 기준을 표준으로 하였으므로, 사용자의 요구가 복잡하고, 다양화되면서 소프트웨어의 크기, 알고리즘과 구조의 복잡도는 증가있는 변화하는 환경에 능동적으로 대처하지 못하고 있다. 그러므로 본 연구에서는 새로운 부품의 삽입과 기존 부품들의 삭제, 분류 기준의 변경 등의 환경 변화에 능동적으로 대처할 수 있는 적응성이 있는 재사용성 결정 모델을 제안한다. 이 모델은 적응성 있는 재사용 결정 알고리즘을 찾기 위해서 데이터에 숨겨진 패턴들을 발견하는 효율적인 알고 ？遲\ulcorner 제공하는 Rough set 이론을 이용한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.175-178
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• 1997

최근들어 가전제품은 90년대를 전후로 고품질화, 고기능화, 다양화, 지능화로의 추세가 한층 가속화되도 있다. 즉 퍼지, 신경회로망, 카오스, 유전자 알고리즘등으로 대표되는 soft computing 기술을 적용하여 가전제품의 인공지능화를 추구해 왔으며 한편으로는 첨단이론을 적요안 가전제품의 수명은 점점 단축되고 있는 실정이다. 한편 환경보호에 대한 사회 전반적인 인식의 확대호 에너지 절약에 대한 관심이 고조되고 있다. 따라서 세탁기 사용에 있어서 세탁량을 정확히 감지하여 오감지로 인한 과도한 세탁수 사용을 방지할 수 있는 알고리즘을 개발하면 한정된 에너지를 절약하는데 큰 기여를 할 수 있다. Soft computing 기술의 하나인 Rough set 이론을 적용하여 세탁량(포량)감지 알고리즘개발에 관해 기술한다.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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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.