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.
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.
Imaging Photoplethysmography (IPPG) is an emerging technique for the extraction of vital signs of human being using video recordings. IPPG technology with its advantages like non-contact measurement, low cost and easy operation has become one research hot spot in the field of biomedicine. However, the noise disturbance caused by non-microarterial area cannot be removed because of the uneven distribution of micro-arterial, different signal strength of each region, which results in a low signal noise ratio of IPPG signals and low accuracy of heart rate.
In this paper, we propose a method of improving the signal noise ratio of camera-based IPPG signals of each sub-region of the face using a weighted average. Firstly, we obtain the region of interest (ROI) of a subject’s face based camera. Secondly, each region of interest is tracked and feature-based matched in each frame of the video. Each tracked region of face is divided into 60x60 pixel block. Thirdly, the weights of PPG signal of each sub-region are calculated, based on the signal-to-noise ratio of each sub-region. Finally, we combine the IPPG signal from all the tracked ROI using weighted average. Compared with the existing approaches, the result shows that the proposed method takes modest but significant effects on improvement of signal noise ratio of camera-based PPG estimated and accuracy of heart rate measurement.
The high efficiency solar cells usually have high capacitance characteristic, so the measurement of their photoelectric performance usually requires long pulse width and long sweep time. The effects of irradiance non-uniformity, probe shielding and spectral mismatch on the IV curve measurement are analyzed experimentally. A compensation method for irradiance loss caused by probe shielding is proposed, and the accurate measurement of the irradiance intensity in the IV curve measurement process of solar cell is realized. Based on the characteristics that the open circuit voltage of solar cell is sensitive to the junction temperature, an accurate measurement method of the temperature of solar cell under continuous irradiation condition is proposed. Finally, a measurement method with the characteristic of high accuracy and wide application range for high capacitance solar cell is presented.
Based on a testing method of spatial frequency response(SFR), a setup for characteristics measurements of the infrared defect tester,which can also be called electroluminescence tester(EL tester), a machine examining defects of photovoltaic (PV) panel, was built. The influences of focusing plane adjustments and infrared light box arrangements on resolution measurement of EL tester in full field of view were analyzed. For different types of EL testers, portable and fixed, testing methods and procedures were presented. Especially, a novel testing method for portable EL was claimed, which could do the work well without reference background. Based on method claimed and setup built, the resolutions of different types of EL testers were obtained and stable results were achieved. This setup is portable designed to meet online measurements requirements of PV industry.
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