In this paper, a fuzzy clustering technique for image segmentation is developed by incorporating a hybrid of local spatial membership and data information into the conventional hard C-means (HCM) algorithm. This incorporation is a threefold procedure. (1) The membership function of a pixel is spatially smoothed in the pixel vicinity. (2) The Kullback-Leibler (KL) divergence between the pixel membership function and the smoothed one is added to the HCM objective function for fuzzification. (3) The resulting fuzzified HCM is regularized by adding a weighted HCM-like function where the original pixel data are replaced by locally smoothed ones. Thereby the weight is proportional to the residual of the locally smoothed membership. This residual decreases when many pixels existing in the pixel vicinity belong to the same cluster. Thus, the weighted distance decreases, allowing the pixel membership to follow the dominant membership in the pixel vicinity. The simulation results of segmenting synthetic, medical and media images have shown that the proposed algorithm provides better performance compared to several previously developed algorithms. For example, in a synthetic image, with added white Gaussian noise having a variance of 0.3, the proposed algorithm provides accuracy, sensitivity and specificity of 92%, 84% and 94.7% respectively, while the algorithm with the closest results provides 81.9% of accuracy, 62.2% of sensitivity and 86.8% of specificity. In addition, the proposed algorithm shows the capability to identify the number of clusters.

In this paper, a fuzzy clustering technique for image segmentation is developed by incorporating a hybrid of local spatial membership and data information into the conventional hard C-means (HCM) algorithm. This incorporation is a threefold procedure. (1) The membership function of a pixel is spatially smoothed in the pixel vicinity. (2) The Kullback-Leibler (KL) divergence between the pixel membership function and the smoothed one is added to the HCM objective function for fuzzification. (3) The resulting fuzzified HCM is regularized by adding a weighted HCM-like function where the original pixel data are replaced by locally smoothed ones. Thereby the weight is proportional to the residual of the locally smoothed membership. This residual decreases when many pixels existing in the pixel vicinity belong to the same cluster. Thus, the weighted distance decreases, allowing the pixel membership to follow the dominant membership in the pixel vicinity. The simulation results of segmenting synthetic, medical and media images have shown that the proposed algorithm provides better performance compared to several previously developed algorithms. For example, in a synthetic image, with added white Gaussian noise having a variance of 0.3, the proposed algorithm provides accuracy, sensitivity and specificity of 92%, 84% and 94.7% respectively, while the algorithm with the closest results provides 81.9% of accuracy, 62.2% of sensitivity and 86.8% of specificity. In addition, the proposed algorithm shows the capability to identify the number of clusters.

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In this note, novel linear matrix inequality (LMI) analysis conditions for the stability of linear parameter-varying (LPV) systems in input-output (IO) representation form are proposed together with bilinear matrix inequality (BMI) conditions for fixed-structure LPV-IO controller synthesis. Both the LPV-IO plant model and the controller are assumed to depend affinely and statically on the scheduling variables. By using an implicit representation of the plant and the controller interaction, an exact representation of the closed-loop behavior with affine dependence on the scheduling variables is achieved. This representation allows to apply Finsler's Lemma for deriving exact stability as well as exact quadratic performance conditions. A DK-iteration based solution is carried out to synthesize the controller. The main results are illustrated by a numerical example.

In conventional polishing processes, the polishing parameters are constant along the surface. Hence, if the desired material to be removed from the surface is not equally distributed, an over-polishing may occur for the areas with small material removal and under-polishing for the areas with large material removal. Consequently, the quality of the processed surface may not meet the manufacture requirements. In this paper, the authors proposed a polishing algorithm to deal with this problem using neural network (NNW) and genetic algorithm (GA). The NNW is used to predict the polishing performance parameters corresponding to a certain polishing parameters. In addition, the GA is employed to optimize the polishing parameters according to an objective function that includes the desired material removal and surface roughness improvement using the output from the trained NNW model. The effectiveness of the proposed algorithm is verified through experiments of polishing uneven surface.

In this paper, the novel design of a force-controlled end-effector for automated polishing processes is presented. The proposed end-effector is to be integrated into a macro-mini robot polishing cell. The macro robot (in this study, it is a six-axis industrial robot) is used to position the mini robot (the proposed end-effector) according to the workpiece profile while the mini robot controls the polishing force. Th end-effector has a polishing head that can be extended and retracted by a linear hollow voice coil actuator to provide tool compliance. The main advantage of the proposed design is that it allows this motion without extending or retracting the polishing motor nor spindle, which reduces the inertial effects that may results in undesired vibrations. By integrating a force sensor, the polishing force is measured and fed back to the controller to regulate it according to the polishing pre-planned requirements. The effectiveness of the proposed device to track a certain desired force with step changes under different feed rates has been examined through polishing experiments. The results demonstrate the effectiveness of the presented device to reduce the vibration and achieve remarkable force tracking.

This work intended to study the effect of the surface scratch depths with scratch depths of 272, 416 and 573 μm on the drop weight (impulse) impact energy of glass fiber mats reinforced unsaturated polyester composites with 25 Vol.% fiber volume content. Moreover, the fracture behavior of the abovementioned composites was also investigated for each case. The results showed that the impact energy of scratched composites was enhanced by adding the scratch on the compression side for different scratch depths compared to that of the virgin specimen. Moreover, effect of increasing scratch depth has slightly effect on the total impact energy and time duration contact of the composites. The fracture behavior showed that by adding the scratch to the compression side of the composites for different scratch depths, the crack deflection and delamination propagate along the weak preferable path (scratch path) and so the area of the crack increases and this dissipates most of the impact energy and so the impact energy is improved. Moreover, on the back face matrix cracks and fiber splitting are the main failure mode and the perforation is accompanied by a pyramid-shaped back face failure.

This work aims to compare tensile properties between different glass fiber architecture reinforced polyester composites which are fabricated by the hand lay-up technique. The stacking sequences of glass fibers consists of five layers which are plain woven (PWGC), short strand fiber (STGC), and sandwich layer glass composites (SLGC) (two layers of plain woven as skin layers and three layers of short glass fibers as a core). The results showed that the tensile strength of all different composite laminates are significantly higher compared to the neat resin and plain woven glass reinforced polyester composites showed the highest values compared with other composite laminates. The tensile failure in PWGC laminate is governed by extensive fiber pull out and delamination, whereas in STGC laminates the failure shows pull out of fibers and little delamination. On the other hand, SLGC hybrid laminate shows mixed failure mode where extensive fiber pull out with little delamination is observed in plain woven skin layers and little fiber pull out in short glass fibers in the core of the laminate.