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 controlsystem 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.
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.
This paper is concerned with the stability analysis of a class of nonlinear teleoperation systems with time-varying delays. The proportional derivative position position control scheme with asymmetrical time-varying d...
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This paper is concerned with the stability analysis of a class of nonlinear teleoperation systems with time-varying delays. The proportional derivative position position control scheme with asymmetrical time-varying delays in control loops is considered for the master and the slave robots. Firstly, a new Lyapunov-Krasovskii functional(LKF) with several delayproduct-type terms is constructed by using the information of asymmetrical time-varying delays;and the Wirtinger-based integral inequality and the reciprocally convex matrix inequality are used to estimate the derivative of the LKF. As a result, a delaydependent stability criterion with less conservatism is established. Finally, an example is given to show the effectiveness and merits of the proposed criterion.
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.
As a unique property of the object surface, the spectral reflectance plays an important role in computer vision applications and in realistic material reproduction. To determine the influence of the light source on th...
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As a unique property of the object surface, the spectral reflectance plays an important role in computer vision applications and in realistic material reproduction. To determine the influence of the light source on the spectral reflectance reconstruction accuracy, the pseudo-inverse method is adopted to reconstruct the spectral reflectance. Further, the genetic algorithm is used to optimize the light source for improving the reconstruction accuracy. The experiment results show that the spectral reflectance reconstruction accuracy is highly affected by the light source spectra, and the light source spectra optimized by the proposed strategy significantly outperforms several commonly used illumination source.
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.
Protein complexes are key molecular entities that play an integral role in human life activities. systematic identification of protein complexes is an important application of data mining in the biological sciences. E...
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Protein complexes are key molecular entities that play an integral role in human life activities. systematic identification of protein complexes is an important application of data mining in the biological sciences. Existing multi-label learning algorithms can effectively label nodes belonging to different complexes in protein-protein interaction network to identify overlapping complexes. However, the protein complexes formed by the stochastic strategy may have unstable results and insufficient community quality. To solve these problems, this paper proposes a novel protein complex identification method based on multisource fused data and the multi-label learning algorithm. The descending order of the potential influence of the nodes is used as the node selection order to solve the problem of unstable partitioning of the composite results. The comprehensive similarity obtained by the link correlation and the similarity of the gene annotations is used as the node label update strategy to improve the quality of the composite. The experimental results show that the new proposed method is much more effective and feasible,and has higher precision and biological significance.
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