Printed Surface Defect Detection Model Based on Positive Samples

For a long time, the detection and extraction of printed surfacedefects has been a hot issue in the print industry. Nowadays, defect detectionof a large number of products still relies on traditional image processingalgorithms such as scale invariant feature transform (SIFT) and orientedfast and rotated brief (ORB), and researchers need to design algorithms forspecific products. At present, a large number of defect detection algorithmsbased on object detection have been applied but need lots of labeling sampleswith defects. Besides, there are many kinds of defects in printed surface,so it is difficult to enumerate all defects. Most defect detection based onunsupervised learning of positive samples use generative adversarial networks(GAN) and variational auto-encoders (VAE) algorithms, but these methodsare not effective for complex printed surface. Aiming at these problems, Inthis paper, an unsupervised defect detection and extraction algorithm forprinted surface based on positive samples in the complex printed surface isproposed innovatively. We propose a kind of defect detection and extractionnetwork based on image matching network. This network is divided into thefull convolution network of feature points extraction, and the graph attentionnetwork using self attention and cross attention. Though the key pointsextraction network, we can get robustness key points in the complex printedimages, and the graph network can solve the problem of the deviation becauseof different camera positions and the influence of defect in the differentproduction lines. Just one positive sample image is needed as the benchmarkto detect the defects. The algorithm in this paper has been proved in “TheFirst ZhengTu Cup on Campus Machine Vision AI Competition” and gotexcellent results in the finals. We are working with the company to apply it inproduction.

QUANTILE ESTIMATION WITH AUXILIARY INFORMATION UNDER POSITIVELY ASSOCIATED SAMPLES

The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.

RAMAN AND ATR-FTIR SPECTROSCOPY TOWARDS CLASSIFICATION OF WET BLUE BOVINE LEATHER USING RATIOMETRIC AND CHEMOMETRIC ANALYSIS

There is a substantial loss of value in bovine leather every year due to a leather quality defect known as“looseness”.Data show that 7%of domestic hide production is affected to some degree,with a loss of$35m in export returns.This investigation is devoted to gaining a better understanding of tight and loose wet blue leather based on vibrational spectroscopy observations of its structural variations caused by physical and chemical changes that also affect the tensile and tear strength.Several regions from the wet blue leather were selected for analysis.Samples of wet blue bovine leather were collected and studied in the sliced form using Raman spectroscopy(using 532 nm excitation laser)and Attenuated Total Reflectance-Fourier Transform InfraRed(ATR-FTIR)spectroscopy.The purpose of this study was to use ATR-FTIR and Raman spectra to classify distal axilla(DA)and official sampling position(OSP)leather samples and then employ univariate or multivariate analysis or both.For univariate analysis,the 1448 cm^(-1)(CH_(2) deformation)band and the 1669 cm^(-1)(Amide I)band were used for evaluating the lipid-to-protein ratio from OSP and DA Raman and IR spectra as indicators of leather quality.Curve-fitting by the sums-of-Gaussians method was used to calculate the peak area ratios of 1448 and 1669 cm^(-1 )band.The ratio values obtained for DA and OSP are 0.57±0.099,0.73±0.063 for Raman and 0.40±0.06 and 0.50±0.09 for ATR-FTIR.The results provide significant insight into how these regions can be classified.Further,to identify the spectral changes in the secondary structures of collagen,the Amide I region(1600-1700 cm^(-1))was investigated and curve-fitted-area ratios were calculated.The 1648:1681 cm^(-1)(non-reducing:reducing collagen types)band area ratios were used for Raman and 1632:1650 cm^(-1)(triple helix:α-like helix collagen)for IR.The ratios show a significant difference between the two classes.To support this qualitative analysis,logistic regression was performed on the univariate data to classify the samples quantitatively into one of the two groups.Accuracy for Raman data was 90% and for ATR-FTIR data 100%.Both Raman and ATR-FTIR complemented each other very well in differentiating the two groups.As a comparison,and to reconfirm the classification,multivariate analysis was performed using Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA).The results obtained indicate good classification between the two leather groups based on protein and lipid content.Principal component score 2(PC2)distinguishes OSP and DA by symmetrically grouping samples at positive and negative extremes.The study demonstrates an excellent model for wider research on vibrational spectroscopy for early and rapid diagnosis of leather quality.

Sampled data based containment control of second-order multi-agent systems under intermittent communications

This paper studies the sampled data based containment control problem of second-order multi-agent systems with intermittent communications,where velocity measurements for each agent are unavailable.A novel controller for second-order containment is put forward via intermittent sampled position data measurement.Several necessary and sufficient conditions are derived to achieve intermittent sampled containment control by means of analyzing the relationship among control gains,eigenvalues of the Laplacian matrix,the sampling period,and the communication width.Finally,several simulation examples are used to testify the correctness and effectiveness of the theoretical results.