Fuzzy Framework Research Articles

Regionalization of evapotranspiration using fuzzy dynamic clustering approach. Part 1: Formation of regions in India

AbstractDelineation of homogeneous reference evapotranspiration (ET0) regions is essential for different applications in hydro‐meteorology. In conventional regionalization approaches, lumped (time‐invariant) statistics such as mean, median or interquartile range of different hydrometeorological variables are often considered as attributes to delineate regions. Information on temporal dynamics of those variables is not utilized (as it is lost in lumped statistics), which if accounted could yield better regions. To address this, a new regionalization approach is presented in fuzzy framework in this Part 1 of a two‐part series. In the proposed approach, information on temporal dynamics of predictor climate variables influencing ET0 is used for regionalization, and the delineated regions are subsequently validated for homogeneity using the predictand (ET0) related information. Effectiveness of the approach is demonstrated through a case study on India, which yielded 18 regions. They are shown to be statistically more homogeneous in ET0 when compared to the existing agro‐ecological zones and regions formed using global fuzzy c‐means clustering method. The homogeneous ET0 regions were found to be different from homogeneous actual evapotranspiration (ETa) regions delineated over India using the fuzzy dynamic clustering approach. Various applications of the homogeneous ET0 regions formed using the proposed approach are presented in Part 2 of this series.

FSIFT-PLV: An emerging phase synchrony index

Quantifying phase synchrony between EEG signals is of great importance. Most common issues in calculating phase synchrony are including: presence of noise, volume conductance, common reference, and sample size bias, as well as small delays or time jitters. In this paper we propose a new bivariate synchrony measure for EEG, called fuzzy scale invariant feature transform phase locking value (FSIFT-PLV). FSIFT-PLV uses a phase estimation approach based on reduced interference Rihaczek distribution. Phase of signals are calculated as a time-frequency map. For each pair of signals, scale invariant feature transform (SIFT) descriptors are extracted from these maps. Then, our proposed fuzzy framework is used for quantifying similarity between maps, which is a synchrony measure in [0,1]. Parameters of the fuzzy system are optimized using particle swarm optimization approach.FSIFT-PLV, is applied to synthetic data generated using Rossler oscillator. FSIFT-PLV outperforms reduced interference Rihaczek distribution based-PLV, ImC, PLI, wPLI, dwPLI and ciPLV in capturing actual coupling strength, and also the presence of noise, common reference, volume conductance, time-delays and sample size bias.FSIFT-PLV is applied to a 4-class cognitive EEG database. Three nodal centrality features are extracted from connectivity networks. Repeated measure ANOVA (with Bonferroni post-test) is used to assess features. Results demonstrate superiority of features generated based on FSIFT-PLV at the confidence level of 99 %, comparing to state of the art approaches including: reduced interference Rihaczek distribution based-PLV, ImC, PLI, wPLI, dwPLI and ciPLV.

The Application of Adaptive Neuro-Fuzzy Inference System and Fuzzy Delphi Technique to Assess Socio-Economic Impacts of Construction of Rural Roads

Abstract One of the key elements for rural development is the connectivity using proper roads, which enhances the passage for economic and social utilities with overall socio-economic development. Socio-economic impact assessment (SEIA) forms one of the significant measures to evaluate the outcome received through infrastructure development in rural areas. SEIA modeling under computational intelligence coupled with fuzzy framework provides significant ground to deal with both qualitative and quantitative data. This study proposes a novel methodology by using Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy Delphi method (FDM) to evaluate socio-economic impacts. The effectiveness of the methodology is presented through a case study for 27 habitations connected with all-weather rural roads constructed under Pradhan Mantri Gram Sadak Yojana (PMGSY) scheme in Jhunjhunu district of Rajasthan State, India. 33 key-parameters under five different criteria are considered for SEIA. For a comprehensive view of the impacts, the results are depicted using ArcGIS tool.

Business continuity-inspired fuzzy risk assessment framework for hospital information systems

ABSTRACT Organizations use information systems to automate their processes. Similar to other types of information systems, hospital information systems face a variety of risks (i.e. potential hazards to human health). For responding to such risks, a practical fuzzy risk assessment framework is developed under the business continuity management concepts. The proposed framework benefits from a fuzzy multi-criteria decision-making method and a fuzzy inference system to quantify and analyze the uncertain information gathered from experts. A procedure for developing suitable business continuity plans is also presented. Finally, the applicability of the proposed framework is demonstrated through a real case study.

Unsupervised Multiregion Partitioning of Fully Polarimetric SAR Images With Advanced Fuzzy Active Contours

This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods.

Robust Stabilization and Control of Takagi–Sugeno Fuzzy Systems with Parameter Uncertainties and Disturbances via State Feedback and Output Feedback

A large portion of the frameworks in the business contain extraordinary nonlinearity and vulnerabilities, which are difficult to outline and control using general nonlinear frameworks. To vanquish these kind of inconveniences, distinctive plans have been created in the last two decades, among which a well-known procedure is Takagi–Sugeno fuzzy control. In this paper, this work presents strong adjustment and control of Takagi–Sugeno (T–S) fuzzy frameworks with parameter vulnerabilities and aggravations. At first, Takagi and Sugeno (T–S) fuzzy model is utilized to speak to a nonlinear framework. In light of this T–S fuzzy model, fuzzy controller configuration plans for state input, and yield criticism is likewise created. At that point, vital conditions are inferred for strong adjustment insight of Lyapunov asymptotic security and are communicated in the Linear Matrix Inequalities (LMIs). The proposed framework is implemented in the working stage of MATLAB and the simulation results are given to represent the efficacy of the proposed strategies.

A fuzzy clustering approach for fuzzy data based on a generalized distance

Most of the distances used in case of fuzzy data are based on the well-known Euclidean distance. In detail, a fuzzy number can be characterized by centers and spreads and the most common distances between fuzzy numbers are essentially defined as a weighted sum of the squared Euclidean distances between the centers and the spreads. In the multivariate case the Euclidean distance does not take into account the correlation structure between variables. For this reason, the Mahalanobis distance has been introduced which involves the corresponding covariance matrix between the variables. A generalization of that distance to the fuzzy framework is proposed. It is shown to be useful in different contexts and, in particular, in a clustering approach. As a result, non-spherical clusters, that generally are not recognized by means of Euclidean-type distances, can be recognized by means of the suggested distance. Clustering applications are reported in order to check the adequacy of the proposed approach.

Mamdani-Fuzzy Framework for Academic Staff Selection and Placement in Nigerian Universities

Selection and placement of appropriate personnel for the right job leads to great success in any organization. However, this is one of the most important activities carried out by Human Resource (HR). Minimizing imprecision and subjective value judgment in personnel selection processes were taken into consideration in this research by developing personnel selection and placement framework using the Mamdani- fuzzy model. This research work is aimed at developing Mamdani- fuzzy framework for academic staff selection and placement. A model with three levels has been developed to manage the database, and the necessary conditions required from applicants for selection and placement, and the consideration of individual temperament was paramount. Tools: Java script and HTML (for the front end) PHP and MySQL (for the database storage as back end). Experimental results using fuzzy classification membership function defined by the truth value of a fuzzy propositional function would also be used as part of the analysis and design. When the need arise, MathLab would be employed for some analysis and simulations. A graphical user interface (GUI) would be developed for all the relevant forms in order to effectively interact with the users of the system

A fuzzy framework for prioritization and partial selection of security requirements in software projects

Resource limitations in software projects rarely allow for the security requirements to be fully realized. As such, Prioritization and Selection (PAS) techniques are used to find an optimal subset of the requirements. Consequently, some of the secur

Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making

The framework of T-spherical fuzzy set is a generalization of fuzzy set, intuitionistic fuzzy set and picture fuzzy set having a great potential of dealing with uncertain events with no limitation. A T-spherical fuzzy framework can deal with phenomena of more than yes or no type; for example, consider the scenario of voting where one’s voting interest is not limited to “in favor’’ or “against’’ rather there could be some sort of abstinence or refusal degree also. The objective of this paper is to develop some correlation coefficients for T-spherical fuzzy sets due to the non-applicability of correlations of intuitionistic fuzzy sets and picture fuzzy sets in some certain circumstances. The fitness of new correlation coefficients has been discussed, and their generalization is studied with the help of some results. Clustering and multi-attribute decision-making algorithms have been proposed in the environment of T-spherical fuzzy sets. To demonstrate the viability of proposed algorithms and correlation coefficients, two real-life problems including a clustering problem and a multi-attribute decision-making problem have been solved. A comparative study of the newly presented and pre-existing literature is established showing the superiority of proposed work over the existing theory. Some advantages of new correlation coefficients and drawbacks of the pre-existing work are demonstrated with the help of numerical examples.

A Framework for Optimal Attribute Evaluation and Selection in Hesitant Fuzzy Environment Based on Enhanced Ordered Weighted Entropy Approach for Medical Dataset.

Background:In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Extensive research that focuses on incorporating vagueness in the form of fuzzy sets, fuzzy rough sets and hesitant fuzzy sets (HFS) has been in past decades. Objective: The paper aims to develop an enhanced entropy based on the clustering technique for calculating the weights of the attributes to finally generate appropriately clustered attributes. Material and Methods: Finding optimal attributes to make a decision has always been a matter of concern for the researchers. Metrics used for optimal attribute generation can be broadly classified into mutual dependency, similarity, correlation and entropy based metrics in fuzzy domain .The experimentation has been carried out on ECG dataset in a hesitant fuzzy framework with four attributes. Results: We propose a novel correlation based on an algorithm that takes entropy based weighted attributes as input which effectively generates a relevant and non-redundant set of attributes. We have also derived correlation coefficient formulas for HFSs and applied them to clustering analysis under framework of hesitant fuzzy sets. The results show the comparison of the proposed mathematical model with the existing similarity based on algorithms. Conclusion: The selection of optimal relevant attributes certainly highlights the robustness and efficacy of the proposed approach. The entire experimentation and comparative results help us conclude that selection of optimal attributes in hesitant fuzzy domain certainly prove to be a powerful tool in order to express uncertainty in the process of data acquisition and classification.

Altered dynamics may drift pathological fibrillization in membraneless organelles

Protein phase transition can generate non-membrane bound cellular compartments, which can convert from liquid-like to solid-like states. While the molecular driving forces of phase separation have been largely understood, much less is known about the mechanisms of material-state conversion. We apply a recently developed algorithm to describe the weak interaction network of multivalent motifs, and simulate the effect of pathological mutations. We demonstrate that linker dynamics is critical to the material-state of biomolecular condensates. We show that linker flexibility/mobility is a major regulator of the weak, heterogeneous meshwork of multivalent motifs, which promotes phase transition and maintains a liquid-like state. Decreasing linker dynamics increases the propensity of amyloid-like fragments via hampering the motif-exchange and reorganization of the weak interaction network. In contrast, increasing linker mobility may compensate rigidifying mutations, suggesting that the meshwork of weak, variable interactions may provide a rescue mechanism from aggregation. Motif affinity, on the other hand, has a moderate impact on fibrillization. Here we demonstrate that the fuzzy framework provides an efficient approach to handle the intricate organization of membraneless organelles, and could also be applicable to screen for pathological effects of mutations.

Impact of rural road construction on the local livelihood diversification: evidence from Pradhan Mantri Gram Sadak Yojana in Jhunjhunu district, India

Rural roads contribute significantly to the socio-economic and cultural development of rural habitations/villages. Improved road infrastructure facilitates rural population to look for work beyond their respective communities. They fuel the development of non-farm markets and create possible livelihood diversification opportunities for rural people, thereby decreasing their liability to economic distresses. The developing countries like India have been introducing various schemes to improve the status of their citizens. In 2000, Government of India launched Pradhan Mantri Gram Sadak Yojana (PMGSY) with a broad objective of improving the mobility of the rural population to achieve sustainable development in a phased manner. This article proposes a comprehensive approach to investigate the impact of the development of rural road infrastructure on livelihood diversification of the target population at the habitation level by employing fuzzy framework. It also accounts econometric modeling to assess the influence of diversification and control variables on household income. The effectiveness of the proposed model is illustrated by taking a case study of the construction of PMGSY roads in Jhunjhunu district of Rajasthan state, India. The findings reveal that newly constructed roads have been used prominently for agricultural activities. However, there is minimal growth in other occupations leading to low diversification of livelihood due to unawareness and lack of resources. It is, therefore, necessary that the policy makers should focus to implement reliable schemes to maximize livelihood diversification in a sustainable way. Thus, rural population can be served in an effective manner by promoting both farm and non-farm activities.

Solving linear equations by fuzzy quasigroups techniques

We deal with solutions of classical linear equations a·x=b and y·a=b, applying a particular lattice valued fuzzy technique. Our framework is a structure with a binary operation · (a groupoid), equipped with a fuzzy equality. We call it a fuzzy quasigroup if the above equations have unique solutions with respect to the fuzzy equality. We prove that a fuzzy quasigroup can equivalently be characterized as a structure whose quotients of cut-substructures with respect to cuts of the fuzzy equality are classical quasigroups. Analyzing two approaches to quasigroups in a fuzzy framework, we prove their equivalence. In addition, we prove that a fuzzy loop (quasigroup with a unit element) which is a fuzzy semigroup is a fuzzy group and vice versa. Finally, using properties of these fuzzy quasigroups, we give answers to existence of solutions of the mentioned linear equations with respect to a fuzzy equality, and we describe solving procedures. Quasigroups and other related structures are an algebraic tool successfully applied up to now in coding theory and cryptology. In our work we propose related applications.

Fuzzy clustering of fuzzy data based on robust loss functions and ordered weighted averaging

In many real cases the data are not expressed in term of single values but are imprecise. In all these cases, standard clustering methods for single-valued data are unable to properly take into account the imprecise nature of the data. In this paper, by considering the Partitioning Around Medoids (PAM) approach in a fuzzy framework, we propose a fuzzy clustering method for imprecise data formalized in a fuzzy manner. In particular, in order to neutralize the negative effects of possible outlier fuzzy data in the clustering process, we proposed a robust fuzzy c-medoids clustering method for fuzzy data based on the combination of Huber's M-estimators and Yager's OWA (Ordered Weighted Averaging) operators. The proposed method is able to smooth the influence of anomalous data by means of a suitable parameter, the so-called typicality parameter, capable to tune the influence of the outliers. The performance of the proposed method has been shown by means of a simulation study, composed of experiments on: (i) simple two-dimensional dataset, (ii) benchmark datasets and (iii) the fuzzy-art-outliers dataset. The comparison made with the robust clustering methods known from the literature indicates the competitiveness of the introduced method to others. An application of the suggested method to a real dataset is also provided and the results of the method has been compared with other clustering methods suggested in the literature. In the application, the comparative assessment has shown the informational gain (in term of additional information) of the proposed method vs the other robust methods.

An interactive multi-objective fuzzy linear programming model for hub location problems to minimize cost and delay time in a distribution network

Hub location models are used to design a distribution network. Hubs are particular facilities that work as connecting, and dispatching points in distribution systems. This study presents a new multi-objective mixed-integer linear programming model for a multi-capacity p-hub median problem under uncertainty. Since delivery time is one of the essential factors considered in the design of a distribution network, the presented model aims to minimise the shipment and establishment cost, as well as delay time of a distribution network in a supply chain where demand, transportation time and establishment cost are uncertain. To cope with these uncertainties and the multiple objectives in the model, we applied an interactive fuzzy framework. Furthermore, the viability and effectiveness of the model and the framework are explored with a number of sensitivity and business analyses.

A hybrid fuzzy framework for face detection and recognition using behavioral traits

Face recognition integrated with emotions and behavior can be a salient feature for identification of antisocial/criminal people. In this paper, a hybrid Fuzzy based Behavior Prognostic System for Disparate traits (FBPSDs) has been proposed. FBPSDs can be used in an emergency situation to identify and extract mischievous person(s) involved in any swindling act. The technique uses face recognition to identify the contextual faces. Further, fuzzy rules have been formulated to extricate more features for the culprit identification. The proposed framework has three stages: face detection, feature extraction and behavior analysis. Face detection for the proposed FBPSDs is further implemented using three techniques Haar Cascade, Skin color based and HOG methods on Labeled Faces in the Wild (LFW) and Facial dataset (Face 94, Face 95 and Face 96). Testing accuracy of 91%, 50%, 92% (LFW) and 91%, 60%, 91.5% (facial dataset) is achieved. Result analysis and evaluation prove that HOG method is the most acceptable and appropriate method for face detection.

Fuzzy reliability-based optimization of a hydropower reservoir

Abstract Reservoir operation modeling and optimization are inevitable components of water resources planning and management. Determination of reservoir operating policy is a multi-stage decision-making problem characterized by uncertainty. Uncertainty in inflows and power demands lead to varying degrees of the working of a reservoir from one period to another. This transition, being ambiguous in nature, can be addressed in a fuzzy framework. The different working states of the reservoir are described as fuzzy states. Based on the degree of success in meeting the power demand and randomness associated with inflows, hydropower production is considered as a random fuzzy event. This paper examines the scope of profust reliability theory, a theory used in the reliability analysis of manufactured systems, in the performance optimization of a hydropower reservoir system. The operating policy derived from a profust reliability-based optimization model is compared with a simulation model. The model is then used to derive the optimal operation policy for a hypothetical reservoir fed by normally distributed inflow, for a period of five years. The results show that the model is useful in deriving optimal operating policies with improved reliabilities in hydropower production.

Enhancing FRT performance and smoothing output power of DFIG wind farm equipped by SFCL and SMES in a fuzzy framework

Wind farms equipped by Doubly-Fed Induction Generators (DFIG) should have two capabilities so that system operators can efficiently utilizes them. These capabilities include Fault-Ride-Through (FRT) and smoothing the output active power fluctuations, especially when these generators provide significant electrical power. The Superconducting Fault Current Limiter (SFCL) and also the Superconducting Magnetic Energy Storage (SMES) are supplementary devices which are used to enhance these capabilities and reduce electrical power swings. In the present work, improvement of FRT capability and smoothing the output power of wind farms are formulated as a multi-objective problem in a fuzzy framework. The variables of optimization are the Proportional-Integral (PI) gains of DFIG and SMES controllers and also SMES and SFCL parameters. Minimization of the initial energy stored in the SMES unit, the energy losses of SFCL, deviations of the DC-link voltage of DFIG, deviations of output active power of DFIG, deviations of output voltage of DFIG, and DFIG speed deviations are six objective functions of the problem. These objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to obtain the multi-objective function. For optimizing this multi-objective function, the Hybrid Big Bang Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. The proposed algorithm is implemented on a case study to numerically evaluate its efficiency. Simulation results show that the proposed algorithm is more effective to enhance FRT capability and smooth output power of wind farm in comparison with the other algorithms.

Risk assessment on offshore photovoltaic power generation projects in China based on a fuzzy analysis framework

China has begun to promote offshore photovoltaic in coastal areas taking its advantages of saving land resources and proximity to load centers. However, the projects are bound to face a series of risk factors as the industry is in its infancy. This paper conducts a risk assessment on offshore photovoltaic power generation projects in China based on a fuzzy framework. Firstly, 16 risk factors affecting offshore photovoltaic power generation projects in China are identified and classified into 4 groups. Secondly, a risk assessment model is constructed involving Hesitant Fuzzy Linguistic Term Sets, Triangular Fuzzy Number and Fuzzy Synthetic Evaluation. Thirdly, this paper conduct an empirical study of China, and the result shows that the risk level of offshore photovoltaic power generation projects in China is medium high. Finally, some response measures are proposed. The risk index system and corresponding countermeasures can provide a reference for project managers to allocate resources to prevent risk events. Besides, the risk assessment model can help project investors to avoid too risky projects. In addition, the risk assessment on offshore photovoltaic power generation projects in China has not been discussed by scholars yet. Thus, this paper contributes to the literature and expand the knowledge.