The wavelet edge detecting method was introduced to the welding seam image processing in this paper, which can make up the defect of usual edge detecting methods in antinoise ability and precise locating ability. The ...
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The wavelet edge detecting method was introduced to the welding seam image processing in this paper, which can make up the defect of usual edge detecting methods in antinoise ability and precise locating ability. The B spline wavelet was applied to extract the image edge and the similarity distance was defined to compare the extracting results. The contrasting results demonstrate the wavelet edge detection is better than the usual methods, which justified the validity that the wavelet transform can be used efficiently in the welding seam image processing.
A model-based matching method is proposed for welded joint localization and recognition. Simple parameterized joint models are defined, which approach the actual joint pose fast in a iterative style by using the parti...
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A model-based matching method is proposed for welded joint localization and recognition. Simple parameterized joint models are defined, which approach the actual joint pose fast in a iterative style by using the partial Hausdorff distance (PHD) as the similarity measurement. Statistical analysis is employed to determine the matching parameters adaptively, and the dimension of parameter space is decreased by performing the estimation on the structured light plane, which make a robust and real-time performance. Experiments show that accurate result can be acquired in real time, which meets the actual applications' requirements.
A control scheme combined with backstepping, radius basis function (RBF) neural networks and adaptive control is proposed for the stabilization of nonlinear system with input and state delay. By using state transforma...
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In the letter [Neurocomputing 71(1–3) (2007) 428–438], there exists one minor error in computing the derivative of V 2 ( ɛ ( t ) ) and thus, the proof of Theorem 1 needs some improvement.
In the letter [Neurocomputing 71(1–3) (2007) 428–438], there exists one minor error in computing the derivative of V 2 ( ɛ ( t ) ) and thus, the proof of Theorem 1 needs some improvement.
Object states estimation and data association are main facets of multi-object tracking. Under complex situations, one object often grouped with others, or occluded by other objects or background, which can increase th...
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The decoupling and linearize (D&L) control of induction motor is an important approach to improve the performance further. The analytical inverse system can realize D&L of nonlinear system when the model is ex...
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The decoupling and linearize (D&L) control of induction motor is an important approach to improve the performance further. The analytical inverse system can realize D&L of nonlinear system when the model is exactly known, but for the induction motor with parameters varying and disturbance, the D&L is destroyed. So the neural network inverse system (NNIS) theory was adapted to approximate the analytical inverse system in order to weaken the couple of rotor flux and speed, the NNIS was designed for the induction motor in the synchronous rotating (dq) reference frame in this paper. Through the analytical inverse system expression we pointed out that the D&L effect is unrelated to the position of d axis. Subsequently, the neural network inverse control (NNIC) structure was proposed. As a special case, the NNIS of induction motor in rotor field oriented (MT) reference frame was also given, the comparison of this NNIC with direct rotor field oriented control (DRFOC) was done and we conclude that it is an improved method of DRFOC. At last, the simulation and experiment were done to test the proposed structures.
Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is ro...
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Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article presents the integration of color distributions into particle filtering. A target is tracked with a particle filter by comparing its histogram with the histograms of the sample positions using the Bhattacharyya distance. Additionally, to solve the sample impoverishment (all particles collapse to a single point within a few iterations) in the particle-filter algorithm, a new resampling algorithm is proposed to tackle sample impoverishment. The performance of the proposed filter is evaluated qualitatively on various real-world video sequences. The experimental results show that the improved color-based particle filter algorithm can reduce sample impoverishment effectively and track the moving object very well.
A novel color correlogram based particle filter was proposed for an object tracking in visual surveillance. By using the color correlogram as object feature, spatial information is incorporated into object representat...
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Previous studies mostly assume deterministic interactions among neighboring individuals for games on graphs. In this paper, we relax this assumption by introducing stochastic interactions into the spatial Prisoner’s ...
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Previous studies mostly assume deterministic interactions among neighboring individuals for games on graphs. In this paper, we relax this assumption by introducing stochastic interactions into the spatial Prisoner’s dilemma game, and study the effects of interaction stochasticity on the evolution of cooperation. Interestingly, simulation results show that there exists an optimal region of the intensity of interaction resulting in a maximum cooperation level. Moreover, we find good agreement between simulation results and theoretical predictions obtained from an extended pair-approximation method. We also show some typical snapshots of the system and investigate the mean payoffs for cooperators and defectors. Our results may provide some insight into understanding the emergence of cooperation in the real world where the interactions between individuals take place in an intermittent manner.
In view of the bad forecasting results of the standard epsiv-support vector machine (SVM) for product sale series with the normal distribution noise, a SVM based on the Gaussian loss function named by g-SVM is propose...
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In view of the bad forecasting results of the standard epsiv-support vector machine (SVM) for product sale series with the normal distribution noise, a SVM based on the Gaussian loss function named by g-SVM is proposed. And then, a hybrid forecasting model for product sales and its parameter-choosing algorithm are presented. The results of its application to car sale forecasting indicate that the short-term forecasting method based on g-SVM is effective and feasible.
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