Synthesis of (BaxSr1−x)(Zn1/3Nb2/3)O3 ceramics by reaction-sintering process and microstructure

(Ba_xSr_1−x)(Zn_1/3Nb_2/3)O₃ complex perovskite ceramics produced by reaction-sintering process were investigated. Secondary phases ZnNb₂O₆ and (Cu₂Zn)Nb₂O₈ were found at the perovskite matrix grain boundaries. 98.5–99.8% of the theoretical density was obtained. In (Ba_0.5Sr_0.5)(Zn_1/3Nb_2/3)O₃ and (Ba_0.7Sr_0.3)(Zn_1/3Nb_2/3)O₃, the amount and volume of second phases decreased clearly at 1500 °C.     

A novel video abstraction method based on fast clustering of the regions of interest in key frames

Online video nowadays has become one of the top activities for users and has become easy to access. In the meantime, how to manage such huge amount of video data and retrieve them efficiently has become a big issue. In this article, we propose a novel method for video abstraction based on fast clustering of the regions of interest (ROIs). Firstly, the key-frames in each shot are extracted using the average histogram algorithm. Secondly, the saliency and edge maps are generated from each key-frame. According to these two maps, the key points for the visual attention model can be determined. Meanwhile, in order to expand the regions surrounding the key points, several thresholds are calculated from the corresponding key-frame. Thirdly, based on the key points and thresholds, several regions of interest are expanded and thus the main content in each frame is obtained. Finally, the fast clustering method is performed on the key frames by utilizing their ROIs. The performance and effectiveness of the proposed video abstraction algorithm is demonstrated by several experimental results.     

New MPPT algorithm for PV applications based on hybrid dynamical approach

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.     

Nonlinear PI control for variable pitch wind turbine

Wind turbine uses a pitch angle controller to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train. This paper investigates a nonlinear PI (N-PI) based pitch angle controller, by designing an extended-order state and perturbation observer to estimate and compensate unknown time-varying nonlinearities and disturbances. The proposed N-PI does not require the accurate model and uses only one set of PI parameters to provide a global optimal performance under wind speed changes. Simulation verification is based on a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST), respectively. Simulation results show that the N-PI controller can provide better dynamic performances of power regulation, load stress reduction and actuator usage, comparing with the conventional PI and gain-scheduled PI controller, and better robustness against of model uncertainties than feedback linearization control.     

Pulse-modulation eddy current probes for imaging of external corrosion in nonmagnetic pipes

Since the nonmagnetic pipe is normally utilized in corrosive and hostile environment, it is prone to the external corrosion which occurs on the outer surface of the pipe and severely undermines the structural integrity and safety. Although Pulsed Eddy Current technique (PEC) is currently preferred for detection and evaluation of subsurface defects in tubular conductors, it is subject to technical drawbacks. In light of this, Pulse-modulation Eddy Current technique (PMEC) is intensively investigated in the paper for enhancement of the evaluation sensitivity to external corrosion and accuracy of corrosion imaging. Closed-form expressions of the PMEC response and its sensitivity to external corrosion in tubular conductors are formulated via the Extended Truncated Region Eigenfunction Expansion (ETREE) modeling. Following simulations for analysis and comparison of field signals and evaluation sensitivities of PMEC and PEC, experiments of PMEC for evaluation and imaging of external corrosion are carried out. Through theoretical and experimental investigation, it has been found that regarding the evaluation and imaging of external corrosion in nonmagnetic pipes, the PMEC-based probe have higher sensitivity and imaging accuracy than that based on PEC. The superiority of PMEC to PEC in inspection of tubular conductors is further identified.     

Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.     

Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.     

Modified dynamic surface approach with bias torque for multi-motor servomechanism

This paper presents a modified neural dynamic surface control (DSC) with an adaptive bias torque for the multi-motor servomechanism (MMS) with backlash, friction and other disturbances. By introducing a continuous hybrid differentiator to replace the first-order filter in each step, a modified DSC is developed to improve the load tracking precision of MMS. However, when the MMS enters the backlash band, only DSC cannot guarantee the load tracking performance. Thus, an adaptive bias torque is firstly proposed based on the prescribed performance function technique to compensate the backlash nonlinearity and guarantee the load tracking performance of MMS. In addition, the unknown dynamics including the friction and other disturbances are approximated by using wavelet echo state networks where the weights are all updated online. By means of Lyapunov stability theory, the semi-globally uniformly ultimately bounded (SGUUB) property of all signals in the closed-loop system is proved. Finally, simulations and experimental results based on a four-motor servomechanism are presented to show the effectiveness of the proposed approach.