Fuzzy Nearness Research Articles

Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

In computer-based search systems, similarity plays a key role in replicating the human search process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The search for images consists of establishing a correspondence between the available images and those sought by the user, by measuring the similarity between the images. In fact, image search per content is generally based on the similarity between the visual characteristics of the images. The distance function used to evaluate the similarity between images depends not only on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval system. In this article, we first constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of the characteristics extracted from the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, which is noted type-2 fuzzy nearness measure (IT2FNM). By analogy to type-2 fuzzy logic, and motivated by a near sets theory, we advanced a new fuzzy similarity measure (FSM) noted as IT2FNM. Then, we propose three new IT2FSMs and provide mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e., reflexivity, transitivity, symmetry, and overlapping). The experimental results generated using three image databases show consistent and significant results.

Dynamic Background Subtraction Using Histograms Based on Fuzzy C-Means Clustering and Fuzzy Nearness Degree

Background subtraction has been widely used in the detection of a moving object from a still scene. Due to the uncertainty in the classification of the pixels in the foreground and background, we propose a novel fuzzy approach for background subtraction using fuzzy histograms based on fuzzy c-means clustering and the fuzzy nearness degree, called FCFN. In this method, the temporal characteristics of the pixels are described by a fuzzy histogram using the fuzzy c-means algorithm. The segmentation threshold is adaptively calculated according to the distribution of the fuzzy nearness degree of the individual pixel. Fuzzy adaptive background maintenance is adopted in the background update framework. The performance of the FCFN is evaluated against several state-of-the-art methods in the complex dynamic scenes. The experimental results demonstrate that the proposed method doubles the improvements in performance than the classic fuzzy background modeling methods and outperforms most state-of-the-art methods.

Development of the performance prediction algorithms for cloud services

Main stages of data center service performance prediction were discussed, specifically data monitoring and gathering, calculation and prediction of key indexes and performance index prediction. It was proposed to build data center service performance prediction algorithm based on an analysis of the service transactions index, service resource occupancy index and service performance index. Prediction of the indexes is based on chaotic time series analysis that was used to estimate service transactions index time series trend, the radar chart method to calculate the service resource occupancy index value and weighted average method to calculate service performance index. For performance prediction, it is proposed to use a fuzzy judgment matrix with the service transactions index and service resource occupancy index as input values. It was taken into consideration that service transactions index is usually represented by nonlinear time series and thus the index time series parameters had to be predicted by chaos theory and for the calculation of this index, the estimation procedure of Lyapunov exponent value can be used. The radar chart demonstrates service resource occupancy index estimation of shared storage, mobile storage, memory, computational capability and network bandwidth. The prediction technique was based on the fuzzy nearness category that use input values of transactions index and dynamic changes of the service resource occupancy index.

Assessment approach to stage of lean transformation cycle based on fuzzy nearness degree and TOPSIS

This paper presents an assessment method to measure the lean transformation (LT) stage of an LT enterprise. Although there are many assessment tools to measure the various aspects of lean practices in enterprises, there is none to measure the stage of LT using the enterprise transformation characteristics from enterprise level. In this paper, the characteristic metrics and characteristic model of LT cycle were extracted from the basic capacity, process power and transformation results. Then, an assessment approach based on fuzzy nearness degree and TOPSIS is proposed to determine the stage of LT. Finally, an example is shown to highlight the procedure of the proposed method. This paper shows that the proposed model is very well suited as an assessment tool for enterprises in the manufacturing industry and other industries to evaluate the LT stage.

A method for grounding grid corrosion rate prediction

Involved in a variety of factors, prediction of grounding grid corrosion complex, and uncertainty in the acquisition process, we propose a combination of EAHP (extended AHP) and fuzzy nearness degree of effective grounding grid corrosion rate prediction model. EAHP is used to establish judgment matrix and calculate the weight of each factors corrosion of grounding grid; different sample classification properties have different corrosion rate of contribution, and combining the principle of close to predict corrosion rate.The application result shows, the model can better capture data variation, thus to improve the validity of the model to get higher prediction precision.

An Improved Fuzzy Evaluation Method on Assimilability of Complex Product Design Scheme

To effectively evaluate and analyze assimilability scheme of complex product, an improved evaluation method is proposed. It sets up three levels in the evaluation: scheme level, craft level and component level, giving evaluation indexes that can influence assimilability of complex product at every level. A corresponding evaluation index system is established. Based on the calculation of fuzzy distances and fuzzy nearness between different evaluation indexes, weighs of information entropy of indexes are gained. Then the levels of current complex product assembling scheme can be acquired. At last, specific project example is conducted to analyze and verify the model and the algorithm. The effectiveness and feasibility of the model and the algorithm are proven.

Fault Diagnosis of Gear Based on the Variable Fuzzy Similarity Nearness

In order to improve the validity and accuracy of fault diagnosis on mechanical equipment, this study applies fuzzy nearness approach by adjusting the traditional formulations. Besides, it points out the disadvantages in fault diagnosis of traditional nearness approach based on experimental data of gear transmission. A fuzzy nearness functionbased model is constructed to execute the fault diagnosis with the assistance of fuzzy nearness principles and fuzzy mathematical membership degree. Following that, a experimental data-based numerical example is implemented, which demonstrates that the adjusted fuzzy nearness approach performs better than the traditional one in diagnosing gear fault effectively and meeting the practical requirements of gear transmission fault diagnosis.

Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach

To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster’s rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness.

White Matter Lesions Change Detection in MR Images Based on Fuzzy Nearness and Non-Subsampled Shear Waves

White Matter Lesions Change Detection in MR Images Based on Fuzzy Nearness and Non-Subsampled Shear Waves

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

Reduction of Uncertainties for Damage Identification of Bridge Based on Fuzzy Nearness and Modal Data

To avoid the false results of deterministic identification methods induced by uncertainties, a fuzzy nearness‐based method is proposed for the damage identification of bridge. An improved index based on ratios of modal shape components is used as identification measurements. The knowledge base for damage identification is established through corresponding relationship between fuzzified measurements and damage severities. The damage condition of test samples can be assessed based on approaching principle through fuzzy nearness with rules in knowledge base. A numerical analysis on a multigirder bridge considering uncertainty is presented to demonstrate the effectiveness of the proposed method. The results indicate that the fuzzy nearness‐based method can achieve an accurate identification with success rate up to 93.75%. Antinoise analysis and the ability for dealing with incomplete information reveal its robustness.

Supercritical Heat Transfer Correlation Select Study Based on Fuzzy Nearness Principle

Supercritical heat transfer correlations are obtained under the certain experimental conditions.The experimental parameters and its application scope is different under different experimental conditions, such as pressure, mass flow density, heat flux, tube diameter, temperature and so on. Supercritical heat transfer correlations are generally chosen in line with the correlation experimental conditions. However, some parameters are calculated under some conditions, under which we can not achieve good results, and at the same time it is prone to increase the deviation. We can choose the best experimental correlation of different experimental conditions based on the fuzzy nearness principles. The existent similarity degree mathematical model is limitated when calculating some similarity parameters.Using new mathematical calculation model resolves this problem, and we can get better experimental correlation for different supercritical heat transfer conditions with the relational selection.

Object-Oriented Land Cover Image Classification System

In order to develop a new object-oriented image classification method with fuzzy support vector machines for land cover, an effective fuzzy membership as a function of fuzzy nearness is used for reducing the effect of outliers in sample sets. Firstly, according to the spatial and spectral characteristics of different targets on rectified image, the number of objects was automatically determined by using mean shift algorithm, in which local objects were picked up with arbitrary shapes and unique mode labeling. Then, a comparison to other object-oriented methods, which were standard support vector machines (SVM) and K nearest neighbor (KNN), without such pre-processing was successively validated. Finally, the comparison was also made between the traditional pixel-based algorithm and the proposed approach. A high precision object-oriented recognition system is established for remote sensing images. Experimental results indicate the proposed method is much more accurate than those traditional pixel-based algorithms and object-oriented algorithms without pre-processing in the study region.

Mature Cluster’s Risk Evaluation

In view of the adverse effect of declining cluster, a method of analyzing and assessing declining risks of mature cluster is studied. Through analyzing causes of declining risks, an evaluation index system for declining risks is built. According to the index system, the characteristic patterns of different risk levels are described. Owing to the complexity and uncertainty of cluster system, a TOPSIS comprehensive evaluation method based on fuzzy nearness degree is proposed to assess declining risks.

Resemblance is a nearness

In (Fuzzy Sets and Systems 133 (2) (2003)) the authors claim that the transitivity condition usually used in the definition of a fuzzy equivalence of fuzzy equality is not suitable in a lot of real situations. The fuzzy relation of a resemblance in a metric space is defined in a different way in (Fuzzy Sets and Systems 133 (2) (2003)). The present paper shows that the same relation (called fuzzy nearness) as in (Fuzzy Sets and Systems 133 (2) (2003)) has been used by several authors in several works to study mainly properties of real function in more general context. A brief review of a variety of results achieved using the relation of a fuzzy nearness is provided.

Nearness relations in environmental space

The notion of proximity is a fundamental component of any comprehensive ontology of space. This paper presents an experiment with human subjects concerning the vague spatial relation 'near' in environmental space. After the topic is introduced and relevant previous work surveyed, the experiment is described. Three approaches to experimental analysis are presented and discussed: nearness neighbourhoods as regions with broad boundaries, fuzzy nearness and distance measures, and four-valued logic. Issues discussed in further detail are the truth gap-truth glut hypotheses regarding the psychology of vague predicates, and formal properties of the three-valued nearness relation. The conclusion is drawn that formal theories can usefully underpin and be applied to reasoning with vague spatial notions, such as nearness.

Research on the fuzzy and dynamic monitoring of the diesel engine operating conditions

In this paper the fuzzy and dynamic monitoring of mechanical equipment is studied using fuzzy techniques. Generally there are two types of condition monitoring: one is that the standard syndrome can be established in advance and, therefore, the fault samples can be identified by this; the other is that the standard syndrome is difficult to establish in advance, and information only about the operation condition of the mechanical equipment is needed. For the former the measure of fuzzy nearness can be used without using a series of weights for the characteristic parameters; for the latter the fuzzy cluster can be used without the standard syndrome. The vibration signal samples obtained from the 190A diesel engine block are identified and classified by the fuzzy measures. The results show that the fuzzy nearness and fuzzy cluster are practical and work well.

Fuzzy nearness structure

Abstract In this paper the notion of fuzzy nearness structure is introduced and some investigations are made towards the unification of several structures: namely, fuzzy topological structure, fuzzy proximity structure, fuzzy contiguity structure and fuzzy uniform structure.