Fuzzy relations and fuzzy relational databases

This paper deals with the connections existing between fuzzy set theory and fuzzy relational databases. Our new result dealing with fuzzy relations is how to calculate the greatest lower bound (glb) of two similarity relations. Our main contributions in fuzzy relational databases are establishing from fuzzy set theory what a fuzzy relational database should be (the result is both surprising and elegant), and making fuzzy relational databases even more robust.Our work in fuzzy relations and in fuzzy databases had led us into other interesting problems—two of which we mention in this paper. The first is primarily mathematical, and the second provides yet another connection between fuzzy set theory and artificial intelligence. In understanding similarity relations in terms of other fuzzy relations and in making fuzzy databases more robust, we work with closure and interior operators; we present some important properties of these operators. In establishing the connection between fuzzy set theory and artificial intelligence, we show that an abstraction on a set is in fact a partition on the set; that is, an abstraction defines an equivalence relation on the underlying set.     

Physico-Mechanical Properties of Nano Silica-Filled Epoxy-Based Mono and Hybrid Composites for Structural Applications

Silicon - This research article describes the results of nano-silica (nSiO2) filled epoxy (Ep) mono composites with different contents (0.5, 1.0, 1.5, 3, and 5 wt.%) and carbon...     

Performance of orthogonal frequency division multiplexing based M-ary quadrature amplitude modulation in asymmetrical dual-hop mixed RF-FSO transmission system

This paper investigates the performance of a dual-hop mixed relay system with radio frequency (RF) and free-space optics (FSO) communication under the effect of pointing error (PE) and atmospheric turbulence (AT). This paper considers a system where RF and FSO links are cascaded. The RF link is modeled by Nakagami-m fading, and the FSO link is modeled as gamma–gamma (G-G) fading channel. Both the channel models use orthogonal frequency division multiplexing (OFDM) with M-ary quadrature amplitude modulation (QAM). The expressions for probability density function, cumulative distribution function, signal-to-noise ratio, and ergodic capacity are derived. The moment generating function (MGF) of fading and the bit error rate (BER) of the OFDM-based M-ary QAM scheme is derived in terms of Meijer's G-function. It has been observed that, in fixed gain relay systems, the modulation scheme's BER is dominated by the SNR of the RF link. While in a variable gain relay system, the turbulence conditions of the FSO system affect the SNR and the BER of the modulation method. The feasibility of heterodyne detection and intensity modulation direct detection (IM/DD) is analyzed in terms of outage probability and ergodic capacity. The results can be used to choose the optimal modulation order and relay system for QAM-OFDM-based optical wireless systems.     

Fast and accurate fuzzy C‐means algorithm for MR brain image segmentation

Fuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C‐Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 188–195, 2016     

Implications of failure criteria choices on the rapid concept design of composite grillage structures using multiobjective optimisation

Grillage topologies are commonly used in many composite structural applications to produce low mass designs that have a high stiffness. While composite failure criteria are being compared in many different simple structures, for example plates and tubes, literature must also compare more complicated applications, including grillages, as there are distinct differences in behaviour. This paper therefore performs analysis of grillage structures with more up to date failure criteria, taken from the world wide failure exercise, than previously investigated. The grillage theory selected is that of Navier theory with elastic equivalent properties due to its low computational expense for use with a genetic algorithm to optimise a composite structure. The results take an example from leisure boatbuilding showing the grillages produced from the different limit states, comparing the cost and mass. The final results show that the method allows a rapid analysis of grillages and that the selection of the limit state has an important effect on the optimised grillage topology.     

Hand posture and gesture recognition techniques for virtual reality applications: a survey

Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.     

On the fractal characteristics of low Damköhler number flames

Knowledge of the fractal properties of premixed flame surfaces can potentially be used to help develop turbulent combustion models. Here, direct numerical simulations of low Damköhler number flames are used to analyse the fractal nature of the flames. Two sets of data are considered: (i) thermochemical hydrogen–air turbulent premixed plane-jet flames with detailed chemistry and (ii) thermonuclear flames in type Ia supernovae. A three-dimensional box counting method is used to investigate fractal dimension of the flame surface, characterising the self similarity of flame fronts. In the premixed flames, the fractal dimension is found to vary in time between 2.1 and 2.7. The supernovae flames in distributed combustion regimes yield fractal dimension about 2.7. The results for the maximum fractal dimensions are higher than previously reported. They are explained theoretically by a Reynolds number similarity argument which posits that the high Reynolds number, low Damköhler number limiting value of the fractal dimension is 8/3. Also tested is Mandelbrot’s fractal additive law which relates the fractal dimension determined in two dimensions, which is typical of experimental measurements, to that in three dimensions. The comparison of the fractal dimension in both two-dimensional and three-dimensional spaces supports the additive law, even though the flames considered do not formally satisfy isotropy. Finally, the inner-cut off is extracted from the hydrogen flames and found to be consistent in order of magnitude with Kolmogorov scaling.     

Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.     

Thermoelastic Stress Analysis of a GRP Tee Joint

A detailed study of the stresses that are developed in a glass reinforced plastic (GRP) tee joint under service loads is described. The joints are fabricated by laminating a boundary angle over a radiused fillet on either side of the ‘tee’. Full-field stress characterisation data is provided by a thermoelastic analysis of the tee joint. Calibration procedures that allow the thermoelastic data to be compared with the results of a finite element analysis are detailed. The results of the thermoelastic analysis are compared with values obtained from the finite element analysis. The applicability of thermoelastic analysis as a validation tool for finite element models of composite materials is assessed.