Different Levels Of Fuzziness Research Articles

Regression Discontinuity Designs for Time-to-Event Outcomes: An Approach using Accelerated Failure Time Models

Abstract Regression discontinuity designs (RDDs) have been developed for the estimation of treatment effects using observational data, where a treatment is administered using an externally defined decision rule, linked to a continuous assignment variable. Typically, RDDs have been applied to situations where the outcome of interest is continuous and non-temporal. Conversely, RDDs for time-to-event outcomes have received less attention, despite such outcomes being common in many applications. We explore RDDs for a time-to-event outcome subject to right censoring. An accelerated failure time (AFT) approach is used to establish a treatment effect estimate for a fuzzy RDD (where treatment is not always strictly applied according to the decision rule). This estimation approach is robust to different levels of fuzziness and unobserved confounding, assessed using simulation studies and compares favourably to established structural AFT models. A motivating example is presented in which models are fitted to estimate the effect of metformin on mortality and cardiovascular disease rate using real observational data from UK Primary Care.

Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach

Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. This paper presents an entropy c -means (ECM), a method of fuzzy clustering that simultaneously optimizes two contradictory objective functions, resulting in the creation of fuzzy clusters with different levels of fuzziness. This allows ECM to identify clusters with different degrees of overlap. ECM optimizes the two objective functions using two multiobjective optimization methods, nondominated sorting genetic algorithm II (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). We also propose a method to select a suitable tradeoff clustering from the Pareto front. Experiments on challenging synthetic datasets as well as real-world datasets show that ECM leads to better cluster detection compared to the conventional fuzzy clustering methods as well as previously used multiobjective methods for fuzzy clustering.

Objective Fuzziness Assessment of Multi-Colored Fabrics Using 3D Images

Abstract A quantitative evaluation method based on a three-dimensional (3D) laser-scanning technique is presented for the objective assessment of fabric fuzziness. Fabric fuzziness, referring to severity of protruding fibers on a fabric surface, has a direct effect on fabric serviceability and needs to be evaluated routinely for quality assurance. In this research, the characterization of fabric fuzziness was realized by using a customized laser scanning system that can produce the 3D surface image of a fabric in a high resolution, and by analyzing the parameters of roughness amplitude distributions extracted from the 3D image. Multi-colored fabrics with different levels of fuzziness were first visually evaluated to obtain the subjective fuzziness grades in reference to a set of expert-rated cotton samples. Then, the fabrics were scanned by the laser system and the surface roughness parameters were calculated from the 3D images for the regression analysis with the subjective fuzziness grades. The result of multivariable regression analysis indicated that the fuzziness grade has a strong correlation with the roughness parameters, and can be estimated automatically after a fabric is scanned. The system demonstrated its fuzziness ratings are invariant to fabrics’ colors, prints, and structures.

Modeling fuzzy information in fuzzy extended entity-relationship model and fuzzy relational databases

Computer applications in non-traditional area have put requirement on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. Information in real world application is often vague or ambiguous. Currently few researches have been done in modeling fuzziness in conceptual data model and design of fuzzy databases. In this paper, based on fuzzy set theory, different levels of fuzziness are introduced into the EER (enhanced/extended entity-relationship) model and the corresponding representations are given. The EER model is then extended to fuzzy EER model. The formal mapping of the fuzzy EER model into the fuzzy relational database schema is proposed in the paper.

Incorporating fuzzy information into the formal mapping from web data model to extended entity-relationship model

XML eXtensible Markup Language has been the de-facto standard of information representation and exchange over the Web. The mapping from XML model to conceptual databases benefits XML data integration at a high level of data abstraction. Fuzziness is inherent in the many real-world applications. Fuzzy XML model has been developed for managing fuzzy data on the Web. This paper concentrates on formal mapping from the fuzzy XML model to the Extended Entity-Relationship EER model. For this purpose, the paper introduces different levels of fuzziness into EER model and presents the corresponding graphical representations, with the result that EER model can model fuzzy information. The formal mapping from the fuzzy XML model to the fuzzy EER model is then investigated. With the fuzzy XML and EER models, fuzzy information can be modeled at two kinds of data models. More important, the fuzzy XML model can be translated into the fuzzy EER model automatically. The formal mapping methods proposed in the paper are demonstrated with examples.

Modeling fuzzy information in UML class diagrams and object-oriented database models

Conceptual data modeling has become essential for non-traditional application areas. Some conceptual data models have been proposed as tools for database design and object-oriented database modeling. Information in real-world applications is often vague or ambiguous. Currently, a little research is underway on modeling the imprecision and uncertainty in conceptual data modeling and the conceptual design of fuzzy databases. The unified modeling language (UML) is a set of object-oriented modeling notations and a standard of the object management group (OMG) with applications to many areas of software engineering and knowledge engineering, increasingly including data modeling. This paper introduces different levels of fuzziness into the class of UML and presents the corresponding graphical representations, with the result that UML class diagrams may model fuzzy information. The fuzzy UML data model is also formally mapped into the fuzzy object-oriented database model.

Fuzzy information modeling in UML class diagram and relational database models

Computer applications in non-traditional area have put requirement on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. On one hand, the Unified Modeling Language (UML) can be applied for many areas of software and knowledge engineering, and is being applied to data modeling. On the other hand, information in real world application is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data model and design of databases with imprecision and uncertainty. In this paper, based on fuzzy set and possibility distribution theory, different levels of fuzziness are introduced into the UML class diagram model and the corresponding formal representations are given. The UML class diagram model is then extended to fuzzy UML data model. The formal mapping of the fuzzy UML data model into the fuzzy relational database schema is proposed in the paper.

FUZZY TECHNIQUES IN OBJECT BASED MODELING

Inexact knowledge engineering information plays vital role in decision making capabilities within latest areas of information technology. However, this information is often vague or ambiguous and very difficult to represent in implementing the application softwares, but this problem can be handled by modeling imprecision and uncertainty in conceptual data models. In this paper, we have investigated the methods of designing and implementing fuzzy information with different object modeling techniques to handle uncertain data. Also, we have described the different levels of fuzziness with all object modeling representations.

Evaluating fabric fuzziness using laser range sensing

This paper introduces a high-resolution 3-D scanning system for objective evaluation of fabric fuzziness using laser range-sensing technology. This system consists of a laser range sensor, a 2-D mechanical stage, and a computer with dedicated software. The paper covers the process of surface digitization from 3-D scanning, image-processing techniques for surface feature description, and methods for characterization of fabric fuzziness. It also reports a preliminary test on a set of fabrics that were treated in different laundering cycles to gain different levels of fuzziness. The test demonstrates that this system has great potential for discriminating among fabric fuzziness levels in a quantitative and reliable manner.

A Conceptual Design Methodology for Fuzzy Relational Databases

Computer applications in the nontraditional area have put requirements on conceptual data modeling. Some conceptual data models, being the tools of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and design of databases. In this paper, based on fuzzy set and possibility distribution theory, different levels of fuzziness will be introduced into the IFO data model, and the corresponding graphical representations are given. IFO data model is then extended to the fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.