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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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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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.
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.
In this paper, a robust adaptive control scheme is proposed for the stabilization of uncertain linear systems with discrete and distributed delays and bounded perturbations. The uncertainty is assumed to be an unknown...
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In this paper, a robust adaptive control scheme is proposed for the stabilization of uncertain linear systems with discrete and distributed delays and bounded perturbations. The uncertainty is assumed to be an unknown continuous function with norm-bounded restriction. The perturbation is sector-bounded. Combining with the liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the exponential stability of the closed-loop system for any admissible uncertainty.
Aiming at the product demand series with multidimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vect...
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Aiming at the product demand series with multidimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vector machine named C v -SVM is proposed. And then, a product demand forecasting method and its relevant parameter-choosing algorithm are put forward. The results of application in car demand forecasting show that the forecasting method based on C v -SVM is effective and feasible.
Based on the finite automata theory, a thinking of distributed deadlock supervisor is proposed to avoid deadlock in Knowledgeable Manufacturing systems, which is insensitive to system size and improves the efficiency ...
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Based on the finite automata theory, a thinking of distributed deadlock supervisor is proposed to avoid deadlock in Knowledgeable Manufacturing systems, which is insensitive to system size and improves the efficiency of automata modeling and operating. The conception of the auto-correlation operation is presented, then the corresponding distributed deadlock supervisors are constructed, and thus the deadlock-free strategy of the knowledgeable manufacturing cell is obtained, which can be used to monitor status of the knowledge manufacturing cell in real-time and guarantees safe running of the system. Finally, a case study is presented to demonstrate the feasibility and effectiveness of the proposed strategy.
Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algo...
Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algorithm to solve the multi-objective problem and increasing its convergence speed. The proposed algorithm is named as multi-objective Particle Swarm Marriage in Honey Bees Optimization (MOPSMBO). It uses non-dominated sorting strategy and crowded-comparison approach, utilizes the local Particle Swarm Optimization (PSO) to perform the local characteristic, and simpler the structure of MBO. Based on the Markov chain theory, we prove that MOPSMBO can converge with probability one to the entire set of minimal elements. Simulations are done on several multi-objective test functions and multi-objective Traveling Salesman Problem (TSP). By comparing MOPSMBO with MOGA, NPGA, NSGA and NSGA-II, simulation results show that MOPSMBO has better convergence speed and can better converge near the true Pareto-optimal front.
An adaptive fuzzy H control scheme which possesses the strongpoint of adaptive fuzzy control and H control was proposed in this paper. The adaptive fuzzy control can compensate the nonlinear dynamic friction with its ...
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ISBN:
(纸本)9787900719706
An adaptive fuzzy H control scheme which possesses the strongpoint of adaptive fuzzy control and H control was proposed in this paper. The adaptive fuzzy control can compensate the nonlinear dynamic friction with its universal approximation function and strong robustness, while the H control can suppress the disturbance efficaciously. Finally, the closed-loop system stability and asymptotic position tracking performance were guaranteed by the Lyapunov function and the system tracking accuracy improved, which was verified by the simulation results.
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