Fuzzy Pattern Recognition Approach Research Articles

A multicriteria fuzzy pattern recognition approach for assessing the vulnerability to drought: Mediterranean region

The first objective of this paper is to explore a new integrated approach to estimate drought vulnerability taking into account the characteristics of a system that make it prone to be affected by an external hazard. The second objective is to investigate the link between fuzzy pattern recognition and distance based multi-criteria categorization oriented to the assessment of the vulnerability to drought. Firstly, relevant information is grouped into drought sensitivity and adaptive capacity criteria. Instead of the estimation of a unique score for the vulnerability, we propose a classification of the vulnerability to drought into several, in general, non ordered categories. Initially, only the ideal and the anti-ideal points are considered. The link with the multicriteria technique for order preference by similarity to ideal solution (TOPSIS) is investigated. Next, many non-ordered categories are considered which are modulated from all the combinations of the extreme points. Finally, the original fuzzy pattern recognition is considered where the centres are not selected a priori but based on the sample itself. A choice that strengthens the meta-multicriteria character of the proposed approaches is that the categories are not ordered, but they are modulated from all the combinations of the extreme points.

A Fractal-Interpolation Model for Diagnosing Spalling Risk in Concrete at Elevated Temperatures

Spalling risk analysis and assessment of fire-loaded concrete structures is an essential part of research on fire-resistant behavior of concrete structures. In this work, the authors developed a stochastic interpolation-based fractal model capable of quickly assessing spalling risk in concrete at high temperatures based on mainly factors affecting explosive spalling. The preliminary grading standard of the factors elected in the study was established based on the data from literature. Test samples were then generated by the stochastic interpolation method according to uniform distribution. A spalling risk diagnosis model was built based on fractal dimension weights. In addition, 10 specimens were assessed with the presented model. The evaluation results obtained by the model in this study were in good agreement with those obtained by the fuzzy pattern recognition approach and coincided well with the actual fire test results. The applicability and reliability of the proposed model were verified and can provide theoretical support for identification of the spalling risk of concrete at elevated temperatures.

Geoelectrical data processing using neuro fuzzy pattern recognition scheme for unambiguous subsurface modelling

Geoinversion takes off different forms to assess the subsurface formations more noticeably. The evolution of soft computing inversion technique makes the geoinversion to the next level of modelling parameters. In this research work, the novel neuro fuzzy pattern recognition approach was introduced to solve the non-linearity involved in geoelectrical resistivity data for appraising the subsurface parameters. The novelty encompasses in generating the patterns using Artificial Neural Networks (ANN) for the geoelectrical resistivity data obtained from the Vertical Electrical Sounding (VES) data as well as the pattern recognition done by the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm is predominant in mitigating the near world truth information that is available. Moreover, the ambiguities of the principle of equivalence have been reduced further by incorporating the Dar-Zarrouk parameter evaluation of longitudinal conductance and transverse resistivity. Thus, this tool could be a good alternate for any conventional algorithm for unravelling such complex problems.

Geoelectrical data processing using neuro fuzzy pattern recognition scheme for unambiguous subsurface modelling

Geoinversion takes off different forms to assess the subsurface formations more noticeably. The evolution of soft computing inversion technique makes the geoinversion to the next level of modelling parameters. In this research work, the novel neuro fuzzy pattern recognition approach was introduced to solve the non-linearity involved in geoelectrical resistivity data for appraising the subsurface parameters. The novelty encompasses in generating the patterns using Artificial Neural Networks (ANN) for the geoelectrical resistivity data obtained from the Vertical Electrical Sounding (VES) data as well as the pattern recognition done by the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm is predominant in mitigating the near world truth information that is available. Moreover, the ambiguities of the principle of equivalence have been reduced further by incorporating the Dar-Zarrouk parameter evaluation of longitudinal conductance and transverse resistivity. Thus, this tool could be a good alternate for any conventional algorithm for unravelling such complex problems.

Behavior‐based botnet detection in parallel

ABSTRACTBotnet has become one major Internet security issue in recent years. Although signature‐based solutions are accurate, it is not possible to detect bot variants in real‐time. In this paper, we propose behavior‐based botnet detection in parallel (BBDP). BBDP adopts a fuzzy pattern recognition approach to detect bots. It detects a bot based on anomaly behavior in domain name service (DNS) queries and transmission control protocol (TCP) requests. With the design objectives of being efficient and accurate, a bot is detected using the proposed five‐stage process, including: (i) traffic reduction, which shrinks an input trace by deleting unnecessary packets; (ii) feature extraction, which extracts features from a shrunk trace; (iii) data partitioning, which divides features into smaller pieces; (iv) DNS detection phase, which detects bots based on DNS features; and (v) TCP detection phase, which detects bots based on TCP features. The detection phases, which consume approximately 90% of the total detection time, can be dispatched to multiple servers in parallel and make detection in real‐time. The large scale experiments with the Windows Azure cloud service show that BBDP achieves a high true positive rate (95%+) and a low false positive rate ( ∼ 3%). Meanwhile, experiments also show that the performance of BBDP can scale up linearly with the number of servers used to detect bots. Copyright © 2013 John Wiley & Sons, Ltd.

Fuzzy Pattern Recognition Approach to Construction Contractor Selection

Contractor selection is a complex process crucial to ensuring the success of construction projects. Existing methods by which owners select a suitable contractor have been inadequate because it is difficult for decision-makers to evaluate contractor bids against inexact qualitative criteria. The purpose of this paper is to propose a Multiple-layer Fuzzy Pattern Recognition (MFPR) approach to solve contractor selection problem. Integrating judgments, experience and preferences of decision-makers, this paper uses the paired comparison method to decide relative membership degrees of qualitative criteria as well as weights of the criteria set, a case study for a channel construction project was used to demonstrate the feasibility of this approach. The findings clearly indicate that the techniques may successfully harmonize different opinions and reach a group consensus.

On-Line Adaptive Fuzzy Diagnosis System: Fusion and Supervision

This paper gives a description of an adaptive on-line diagnosis system for the detection of slow varying changes in dynamical systems using a fuzzy pattern recognition approach. This contribution is an extent ion of a previous paper (Boudaoud et al ., 1996), we have included in the diagnosis and supervision scheme a fusion moduleto combine the decisions of the different sources i.e. classes.

Tool Wear Monitoring and Diagnosis in Milling Using Vibration Signal

In the context of tool wear and surface finish diagnosis, a fuzzy pattern recognition approach is presented to monitor on-line the evolution of a system. Vibration signal processing provides a set of pertinent parameters. Based on fuzzy set theory, a membership function (to the initial class or starting state of the system) is calculated on-line and introduces a one dimension space for data representation. Using an abrupt change detection algorithm (CUSUM and GLR) an automatic decision on the state of the system is made possible. In the application case, the deterioration of the surface finish is successfully detected.

A fuzzy pattern recognition approach to power system transient stability assessment

The aim of the paper is the fast transient stability assessment of electric power systems by using a fuzzy pattern recognition method. The assessment is performed by assuming several regions of stable and unstable classes for the purpose of classifying the system operating states, in order to avoid errors of classification. This method is suitable for on-line applications and its performance is illustrated by the transient stability analysis of a sample power system.