Research on Particle Swarm Image Threshold Segmentation Method Based on Rough Set

In order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.

Diffusion coupling model based on neural oscillation synchronization theory, which can extract different targets with different gray levels from one, has been an important way for realizing image segmentation. This paper describes the diffusion coupling model and its algorithm and process for image segmentation. Simulation programs for supplementing this algorithm were compiled based on MATLAB platform. Experimental results on synthetic images, infrared images and brain magnetic resonance images show that, by setting reasonable algorithm parameters, the target image extraction and segmentation of different elements can be achieved. This image segmentation algorithm exhibits wide applications in medical image diagnosis, satellite image processing, traffic control, biometrics and others.

The variable precision rough set (VPRS) model generalizes the Pawlak rough set model with variable parameters. The Bayesian rough set (BRS) model improves the VPRS model with non-parametric modification by using the prior probability as a reference. This paper presents two research results related to rough set model and rough vague sets. One is a modification to the Bayesian rough set model and the other is a modification to rough vague sets. First, the Bayesian rough set model is analyzed and discussed. Second, a modification to this model is proposed and verified. Finally, a modification to rough vague sets is presented and its related properties are discussed..

The Tsallis Entropy and the Segmentation of Images

Grade and precision are two important confidence levels. In this paper, the connection and combination between them are studied. The connection between graded approximations and variable precision approximations is studied, and the important formula of conversion is achieved. Approximation of intersection of grade and precision is defined based on logical ”and” operation of grade and precision, and its construction is achieved, which becomes important theory base for decreasing computing complexity and computing quantity of application algorithm of graded rough sets and variable precision rough sets. Graded rough sets and variable precision rough sets are united and included by the approximation of intersection of grade and precision. The study on combination and unity of graded rough sets and variable precision rough sets in this paper, has important value on both theory development and practice application.KeywordsArtificial IntelligenceRough SetsGraded Rough SetsVariable Precision Rough SetsConfidence LevelsGradePrecision

Variable precision rough sets (VPRS), as a extension of rough sets (RS) is adopted to reduce the redundant features for its ability of more useful information adopted compared with RS. The reduced features after VPRS are fed into the improved BP network proposed to inspect the defects of surface quality, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer.

The aim of feature selection (FS) is to select a small subset of most important and discriminative features. Many FS approaches based on rough set theory up to now, have employed reduct analysis using feature dependency measures. However the critical shortcoming for such approaches is that they are not able to manage useful information that may be destroyed by noise elements. Therefore several extensions to the original theory have been proposed. Three notable extensions are fuzzy rough set (FRS), variable precision rough set (VPRS), and tolerance rough set model (TRSM). Although successful, each of the extensions exhibits a critical shortcoming which makes that extension inapplicable in most of scenarios. For example, FRS is able to describe the existing dependencies between different attributes accurately, but its high run-times makes it inapplicable to larger datasets. As another example, VPR is very fast, but requires more information than contained within the data itself, which is inaccessible for most of the applications. This paper examines a rough set FS technique which uses a noise resistant dependency measure to quantify information that may be hidden due to the noise elements. Experimental results demonstrate that the use of this measure can result more discriminative reducts than those obtained using other RSFS approaches. Moreover, the proposed measure is as fast as VPRS and as accurate as FRS and TRSM, while it need no additional information other than contained within the data.

Mobile ad hoc networks are formed dynamically without any infrastructure and each node in the network is responsible for routing information. Rough set theory is a mathematical tool to deal with vagueness and uncertainty. Variable precision rough sets (VPRS) is a generalization of rough sets that allows for a controlled degree of misclassification. This paper proposes extensions in mobile ad hoc routing using VPRS. The performance of the proposed mobile ad hoc routing protocol is compared with that of an existing routing protocol.

Traditional rough set of noise data, the lack of adaptability, lack of flexibility or robustness, for the engineering data can not distinguish between equivalence classes of edge region of overlap with the collection, resulting in loss of many valuable engineering information. Strong noise in the actual engineering data over-fitting due to reduced ability to distinguish the object, its limitations limits its further application. In this paper, variable precision rough set model (VPRS, the variable precision Rough set Model), allows a certain threshold level of classification rate exists, in order to better solve the engineering data, no functional relationship between attributes of interference data classification problem, it Overcome the standard rough set model on the shortcomings of the data is too sensitive to noise, thereby enhancing the data analysis and processing robustness. Finally, simulation results show that using variable precision rough set theory to solve the data uncertainty of similar projects under the influence of the extraction is feasible.

Variable precision rough set model is the extension of rough set model, which introduce theβparameter based on the basic rough set theory, that is, allows a certain degree of misclassification rate existing. This paper presents the web mining algorithm based on variable precision rough set in e-commerce, analyzes the limitation of the reduction methods based on variable precision rough set theory. The experiment shows the CPU Time in the attribute numbers, indicating that VPRS is superior to normal algorithm in web mining in e-commerce. The experiments show the algorithm could flexibly and dynamically web mining and therefore provide a valuable addition to the field.

An expert system for automatically selecting stock portfolio is presented. The expert system involved Grey Relational Analysis (GRA) model, the VPRS-index clustering / classification method, and Variable Precision Rough Set (VPRS) theory. The GRA model is applied to consolidate the 53 financial indices into six financial ratios (Grey Relational Grades (GRGs)) for each stock item. The VPRS-index method is used to determine the optimal number of clusters per GRG. VPRS theory is then applied to identify the stocks within the -lower approximate sets. Finally, the GRGs of each candidate stock item are consolidated to a single GRG indicating the ability of the stock item to maximize the rate of return. The validity and effectiveness of the VPRS-index clustering / classification method is first evaluated prior to that of the expert system. After that, results of this study showed that this expert system yields a higher rate of return than those of several existing portfolio selection systems. Key words: Fuzzy C-Means, VPRS, VPRS-index method, classification, stock portfolio.

As an effective tool for knowledge acquisition, attribute reduction is one of the key issues in rough set theory. In current research, most researchers choose to reduce redundant attributes as many as possible through some attribution reduction algorithms. However, the misclassification cost induced by attribute reduction is ignored. Thus, it is worth studying that how to reduce redundant attributes based on preserving the misclassification cost. Firstly, in this paper, the degree of minimum misclassification is defined. Then, by introducing decision process into variable precision rough set, a new model which is based on minimum misclassification cost with variable precision rough set (VPRS) is proposed. Moreover, based on the minimum misclassification cost, a heuristic attribute reduction algorithm is proposed. Finally, the simulation result shows that a feasible and reliable set of attributes can be obtained with our algorithm. These results further enrich attribute reduction to effectively deal with the uncertain classification problems.

Research on infrared image enhancement and segmentation of power equipment based on partial differential equation

This paper introduces the definition of maximum entropy,the principle of onedimensional and 2-D maximum entropy.This paper solves the image segmentation problem by using the maximum entropy algorithm.The image gray value is divided into two regions of the background and the object.The key problem associated with this method is to find the optimal parameter extracting objects from background, the proposed method is used to segment image and ideal segmentation results can be obtained with less computation cost.Experimental simulation shows that the algorithm has a higher accuracy while preserving the edge infomation,and maximum entropy has been proven to be an efficient method for image segmentation.

Quantitative rough sets based on subsethood measures