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The traditional weak signal chaotic detection system still restricts some technical issues in the situation of the signal with noise, such as poor denoising ability and low detection precision. In this paper, we propose a novel weak signal chaotic detection system based on an improved wavelet transform algorithm. First, the traditional wavelet transform algorithm domain variables have been transformed and discretized to eliminate the redundant transform. Then, based on the discrete optimization, the wavelet coefficients have been optimized by threshold compromise strategy. The improved wavelet transform algorithm is applied in the weak signal chaotic detection system. The noise signal after finite discrete processing is treated as a perturbation of cycle power and put into a chaotic system for detecting weak signal under the noise conditions. The simulation experiments show that the proposed improved wavelet transform algorithm has a better denoising effect than the traditional wavelet transform algorithm. Moreover, the improved algorithm shows better accuracy and higher robustness in the weak signal chaotic detection system.

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For the accuracy of traditional HOG feature detection operator in the application of ATM retentate detection is not high, this paper proposes a distinguish between optimization based on texture HOG feature detection operator ATM retentate detection model. First eliminate background of the original image by LBP operato, in order to highlight the local texture feature of detecting target, and then set a tolerance factor to eliminate the instability of LBP operator when neighborhood pixels change small, then the application of probability theory is adopted to optimize variance the similarity measures, and finally on the basis of the LBP background elimination, using the idea of entropy to HOG feature weighted in order to improve the detection accuracy. Experimental results show that the accuracy of proposed improved HOG feature detection based on texture distinguish optimization operator's is higher, the effect in the application of ATM machine retentate detection is better.

As the airbag of a flexible robot is affected by external environmental factors during the profiling process, there are many uncertainties in the process of deformation of the airbag. For this reason, the general nonlinear control strategy cannot obtain an accurate data model. In this paper, a flexible robot profiling MFA (Model-Free Adaptive) model based on adaptive predictive dynamic linear optimization is proposed. Firstly, the real-time thickness of the airbag is obtained through edge detection by using the image processing algorithm. Secondly, the airbag aerodynamic model is constructed by visual servo control strategy. Then, a nonlinear control system based on model-free adaptive control is established. Thirdly, the weighting factor is used to limit the variation range of the input quantity, and the deviation of the actual value and the expected value is corrected by the adaptive prediction mechanism. Finally, the servo control the airbag is completed. The experimental results show that the improved model proposed in this paper solves the overshoot phenomenon of the standard control model with less control error and higher robustness.