With rapid development of rail transport in our country, more and more people choose because of on time, fast and convenient. Safety of the subway is urgent with passenger increasing, and it's very important to inspire hidden danger. The paper proposed The auto-inspection method based on Infrared Laser Imaging and Deep Learning to detect foreign objects between subway doors and the platform screen doors(PSDs). Fast-RCNN Algorithm based on TensorFlow Deep Learning frame was adopted and the image information were fused with classification model, vgg16. The detecting system was built and experiments were made and analyzed. The experimental results showed that this system and method was robust to The illumination variations and focussing. The system is simple and cost-effective and The algorithm is promising for detecting accuracy. The method and technology can be potentially applied for The subway safety detection.
Rolling quality is a key index of the cotton quality, which directly influences the quality of the lint and textiles, however, it is mainly decided through visual classification by skilled personnel. In order to realize the intelligent rapid classification of cotton quality, this paper proposed a decision-level fusion recognition method for the cotton quality grade based on colored-image information. After the preprocessing of images, RGB and HSV features were calculated, respectively. The features are normalization processed and principal component analysis (PCA) is employed to extract the greater contribution features of RGB and HSV images, which are adopted as BP neural network (BPNN) input parameters to identify the quality grade recognition of cotton, respectively, and then output parameters of BPNN are used as independent evidence to construct Basic Probability Assignment (BPA). Finally, D-S Theory is used to obtain the decision fusion and realize the high accuracy the recognition of cotton quality grades. The compared experimental results show that the precision of proposed method is significantly superior to classification using RGB and HSV features respectively. The method provided in this paper can realize the intelligent rapid classification of cotton quality, and proves the feasibility of cotton-graded artificial intelligent classification.
Stimulated emission depletion (STED) microscopy exploits nonlinear saturable optical transition of
fluorescent molecules, allowed to overcome Abbe's diffraction-limit and provides diffraction-unlimited
resolution in far-field optical microscopy. We elaborate the mechanism of STED and the conditions of
depletion. The formula of STED microcopy resolution is deduced through effective point spread
function (E-PSF). The STED system resolution is mainly dominated by the quality of the fluorescence
depletion patterns in the focal plane. The depletion pattern is mainly affected by STED beam intensity,
polarization, phase plate, primary aberrations, STED pulse shape, pulse duration and delay time. In this
paper, we found related models and simulate the relationship between the depletion patterns and the
parameters, and put forward effective approach to enhance the system resolution.
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