This paper mainly studies the self-adaptation of Pulse Coupled Neural Network(PCNN) and the application in handwritten digit recognition. First, the edge extraction algorithm of image using PCNN and maximum entropy is...
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This paper mainly studies the self-adaptation of Pulse Coupled Neural Network(PCNN) and the application in handwritten digit recognition. First, the edge extraction algorithm of image using PCNN and maximum entropy is proposed, the parameters' optimization of PCNN is realized by Simple Genetic Algorithm. Then, the foveation algorithm based on PCNN is used to extract the feature points of handwritten digits. Finally, a BP neural network with two hidden layers is used to recognize the images of handwritten digits which have been preprocessed. Experimental results on handwritten digit recognition demonstrated that the proposed method reached good performance on feature extraction and the recognition has better accuracy compared with the original method using BP neural network directly.
Aiming at the problem that the target occlusion and target loss in the target tracking process can not be solved by most tracking algorithms, anti-occlusion correlation filter tracking based on multi-peaks response is...
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Aiming at the problem that the target occlusion and target loss in the target tracking process can not be solved by most tracking algorithms, anti-occlusion correlation filter tracking based on multi-peaks response is proposed. When the target is occluded, the response of the multi-peaks and the highest peaks exceeds the set thresholds, and the two-dimensional matrix of the Gaussian distribution is used to perform a point multiplication operation with the confidence map to obtain a new confidence map, and the current frame model update is stopped. Experiments show that compared with other algorithms, the algorithm has a significant improvement in tracking accuracy and speed when dealing with video sequences whose targets are occluded.
Ignition temperature in a sintering process is a dominant factor for the quality of sinter ore. However, it is difficult to control the temperature since the pressure of the gas that is used in the ignition fluctuates...
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Ignition temperature in a sintering process is a dominant factor for the quality of sinter ore. However, it is difficult to control the temperature since the pressure of the gas that is used in the ignition fluctuates constantly. This paper presents an intelligentcontrol to stabilize the ignition temperature in a sintering process. First, this paper analyzes the characteristics of an ignition process and studies the main factors affecting the ignition temperature. Then, a particle swarm optimization(PSO)-Elman model is established to predict the ignition temperature. Next, this paper describes an intelligentcontrol system, including a fuzzy controller, two expert controllers, and a switching controller. Finally, the experimental results based on actual run data show that the intelligentcontrol strategy stabilizes the ignition temperature.
Video saliency detection aims to extract salient objects in video. In order to resolve the problems of incomplete extraction salient area and the mixing between background and salient objects at boundary, this paper p...
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Video saliency detection aims to extract salient objects in video. In order to resolve the problems of incomplete extraction salient area and the mixing between background and salient objects at boundary, this paper proposes a video saliency detection method via multi-features fusion based on Boolean Map. The main innovation in this method is that global topological relationship of video frames are utilized to compute saliency values. Firstly, boolean maps are generated according to the Boolean Map theory via combine motion features and color features in videos, and then attention maps are calculated by masking unsurroundedness area, finally, video saliency maps are obtained by fusing all attention maps. Experiments on Segtrack V2 and Fukuchi benchmark datesets show that the proposed method successfully obtains complete and clear boundary salient area, and it outperforms the other general models.
In this paper, aiming at the pesticide residue in water resources, considering the necessity of rapid detection on site,a method based on machine vision for pesticide residue detection is proposed. The method combines...
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In this paper, aiming at the pesticide residue in water resources, considering the necessity of rapid detection on site,a method based on machine vision for pesticide residue detection is proposed. The method combines machine vision and image processing technology to quickly extract the RGB three-component eigenvalues of the pesticide residue detection card. Each RGB channel has 256 brightness levels, which can overcome the shortcomings of low brightness level and indistinct image,and improve the accuracy of detection. The results show that the method is simple,fast and efficient, it is suitable for quickly extracting the characteristic values of pesticide residue detection cards.
The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large, the computation and storage burde...
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The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large, the computation and storage burden in the kernel learning algorithm will increase. In such case, there will be long running time and the real-time performance of the control system cannot be guaranteed. Therefore, in order to enhance the computational speed, this paper proposes a parallel architecture based iterative segmentation optimal cyclic block kernel learning algorithm. The experimental results show that the proposed method can significantly improve the computational efficiency of the kernel learning algorithm, which has important engineering practice significance.
In this paper, the problem of synchronization is investigated for a class of discrete-time nonlinear singularly perturbed complex networks(SPCNs). A slow sampling discrete-time nonlinear SPCN model is first devised, w...
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In this paper, the problem of synchronization is investigated for a class of discrete-time nonlinear singularly perturbed complex networks(SPCNs). A slow sampling discrete-time nonlinear SPCN model is first devised, which includes the slow and the fast states as well as the general sector-like nonlinear functions. By utilizing the Kronecker product, a new Lyapunov function dependent on singular perturbation parameter(SPP) is constructed. A sufficient condition in terms of linear matrix inequalities(LMIs) is derived under which the discrete-time nonlinear SPCN is globally asymptotically synchronized. When these LMIs have feasible solutions, the global asymptotic synchronization is ensured and the upper bound of the SPP is evaluated. A numerical example is given to illustrate the effectiveness of our results.
This paper is concerned with the asymptotical synchronization of two identical chaotic Lur'e systems using sampleddata control. Firstly, by defining the synchronization error, the synchronization of original syste...
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This paper is concerned with the asymptotical synchronization of two identical chaotic Lur'e systems using sampleddata control. Firstly, by defining the synchronization error, the synchronization of original systems is converted into the stabilization design of the error system. Then, an augmented Lyapunov-Krasovskii functional with new terms is constructed and its derivative is estimated by using Wirtinger-based integral inequality and the reciprocally convex matrix inequality. As a result,a less conservative synchronization criterion is established to design the sampled-data scheme. Finally, the advantage of the proposed method is demonstrated by a numerical example.
It is of great significance for accurate and fast magnetic measurement of high magneto-crystalline anisotropy film materials. In this paper, an surface magneto-optical Kerr effect automatic measurement system based on...
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It is of great significance for accurate and fast magnetic measurement of high magneto-crystalline anisotropy film materials. In this paper, an surface magneto-optical Kerr effect automatic measurement system based on LabVIEW is designed according to the practical requirements of hysteresis loop measurement of magnetic films. The system takes the SR830 lock-in amplifier as core, combined with virtual instrument technology and correlative detection technology, which achieves the effective measurement of the weak Kerr signal of Ni Fe soft magnetic film material under strong noise environment, and automatically draws its hysteresis loop. The measurement system has high resolution, high output signal-to-noise ratio, high automation, and has good application value.
In this paper, an adaptive dynamic programming(ADP) algorithm based on value iteration is proposed to tackle the stochastic linear quadratic(SLQ) optimal tracking control problem for discrete-time systems subject to m...
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In this paper, an adaptive dynamic programming(ADP) algorithm based on value iteration is proposed to tackle the stochastic linear quadratic(SLQ) optimal tracking control problem for discrete-time systems subject to multiplicative ***, an augmented system made up of the original system and the command generator is constructed and the condition of well-posedness for SLQ problem is given. Next, the SLQ problem is converted into the deterministic problem through system transformation. Then, ADP algorithm is utilized to solve the SLQ problem with convergence analysis. In the iteration process of ADP algorithm, the system dynamics are not essential to solve the Bellman equation but are needed to update the control gain matrix. Finally, simulation results have shown that the proposed scheme for the problem gives good tracking performance.
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