In this work, the modified Sardar sub-equation method is used to construct optical soliton solutions to the coupled nonlinear Schrodinger equation (CNLSE) having third-order and fourth-order dispersions describing short-pulse propagation in two-core fibers. Several solutions are determined including W-shaped bright, dark soliton solutions, singular soliton solutions, periodic function solutions and the combined complex soliton solutions. It should be noted that this integration scheme is very powerful mathematical tools for obtaining exact optical soliton solutions of nonlinear evolution equations. Under suitable values for the physical parameters, some representative wave structures are graphically displayed. In addition, the linear stability technic is used to analyze the modulation gain spectra in the birefringence associated with an ellipticity angle. In our knowledge, these obtained results are new in the context of nonlinear birefringent optical fibers.

https://link.springer.com/content/pdf/10.1140/epjp/s13360-021-01358-3

Survey of third- and fourth-order dispersions including ellipticity angle in birefringent fibers on W-shaped soliton solutions and modulation instability analysis

This paper presents a vision-based measurement methodology which utilises a micrometre calibration slide and confocal microscope. The technique is suitable for measurement of both angular and linear displacements. The algorithm presented in this paper overcomes the shortcomings of limited CCD pixel count and image size, out of focus areas at the boundaries of the image, and vertical motion of the slide out of the focal plane. Many parameters can be changed within the setup, allowing the resolution and range of measurement to be modified for a given application. Experimental verification has been performed in an angular measurement configuration, demonstrating noise-limited linear and angular resolutions of 0.04 μm and 0.29 μrad, respectively.

A vision-based measurement algorithm for micro/nano manipulation

Generation of excellent self-similar pulses in a dispersion-decreasing fiber

Nonlinear pulse reshaping in a typically designed silicon-on-insulator waveguide and its application to generate a high repetition rate pulse train

Aiming at the problems of target detection network in the defect detection field of thangka images with complex background colors, such as poor small target detection effect, insufficient feature information extraction, prone to error detection and leak detection, and low accuracy of defect detection, this paper proposed the YOLOv5 defect detection algorithm combining attention mechanism and receptive field. First of all, the Backbone network is used for feature extraction, integrating attention mechanism to represent different features, so that the network can fully extract the texture and semantic features of the defect area, and the extracted features are weighted and fused to reduce information loss. Secondly, a weighted fusion of features of different dimensions is transferred by the Neck network, and the combination of FPN and PAN is used to realize the fusion of semantic features and texture features of different layers and to locate the defect target more accurately. Finally, while replacing the GIoU loss function with CIoU, the receptive field is added to the network, so that the algorithm uses a four-channel detection mechanism to expand the detection range of receptive fields, and fuses semantic information between different network layers, so as to achieve fast location and more refined processing of small targets. The experimental results show that compared with the original YOLOv5 network, the detection accuracy of YOLOV5-scSE and YOLOV5-CA networks proposed in this paper has improved by 8.71 percentage points and 10.97 percentage points respectively, and the verification index has been significantly improved. It can quickly and more accurately identify and locate the location of the defect area and has a stronger ability to generalize the defect category, which greatly improves the accuracy of thangka image defect detection.

/pdf/application-of-yolov5-based-on-attention-mechanism-and-1t6bo7k4.pdf

Application of YOLOv5 Based on Attention Mechanism and Receptive Field in Identifying Defects of Thangka Images

Interaction between parabolic pulses in a dispersion-decreasing fiber

The relationship between the self-similar propagation region for single pulse and oscillation region for a pair of self-similar pulses are first investigated in our paper. By introducing self-similar coefficient F and RMS width ratio K, we find that self-similar propagation region starts from z = 1.8LD to z = 18LD while F ≤ 10%. The optimum output of self-similar pulse is also achieved when F and K reach a minimum value simultaneously at z = 3.5LD. The sinusoidal fit oscillation region of self-similar pulse pair ranges from 5/8LD to 2LD while F varies from 40.27 to 7.99%, and the dark soliton fit oscillation region ranges from 2LD to 6LD while F varies from 7.99 to 5.32%, indicating that the sinusoidal fit oscillation region almost occurs before the pulses enter the self-similar propagation region and the dark soliton fit oscillation region occurs within the self-similar pulse propagation region. Furthermore, the oscillation characteristics of interacting pulses are also studied numerically by using split-step Fourier method. The results are beneficial in Dense Wavelength Division Multiplexing transmission system which is in heavy demands of light source in wide-range wavelength.

/pdf/research-on-evolution-region-of-self-similar-pulses-in-a-37bldcbap5.pdf

Research on evolution region of self-similar pulses in a dispersion-decreasing fiber

The quality of welding is directly related to the performance and life of the welding structure. The nondestructive testing technology is mainly used for welding defect defection, while the X-ray testing technology can directly and reliably reflect the shape, location and size of defects. For X-ray images, manual inspection is used at present, and it is easily caused by subjective factors, such as professional level, which may lead to low accuracy. This paper focuses on establishing an end-to-end automatic detection model of X-ray welding defects to improve the accuracy and efficiency of detection based on a deep learning algorithm. Considering the feature information of welding defects, this paper improves on the basis of Faster R-CNN and uses the deep residual network Res2Net to enhance the original backbone network to improve the feature extraction ability. And the weighted feature fusion module is studied, which combines the high-level semantic information and the low-level high-resolution edge detail information to predict the feature map of each layer respectively, to improve the detection performance, especially for small targets. The experimental data show that this method can effectively improve the accuracy and efficiency of welding defect detection. 

Intelligent Welding Defect Detection Model on Improved R-CNN

The defect exhibited a great variety of kinds have a direct impact in the yield of IC wafer in the lithography process. The missing material defect is an important factor of reducing the yield of IC, it mainly causes circuits to be disconnection failure, so it is important to recognize the missing material defect and ascertain the circuit's failure style. In this paper, a recognition method for disconnection failure of the missing material defect based on skeleton character is presented. The thinning algorithm is used to extract the skeleton of image. The principle of deleting redundant points and branches is introduced to get the skeleton of single pixel and single direction. Then, the method of neighborhood field tracking is used to recognizing the disconnection circuits.

Machine Vision Recognition of Disconnection Failure of IC Wafer

Cylinder liners are important to automobile engines. The appearance quality will directly affect the life and safety of the engines. At present, the appearance quality inspection of cylinder liners mainly relies on manual visual judgment, which is easily affected by the subjective factors of inspectors. This paper studies improved machine vision to realize surface defect detection. It proposes the improvement of the attention mechanism and a feature fusion method to locate and classify the defect. Experiments show that the method proposed in this paper has improved both accuracy and speed, and it can detect defects in production and realize industrialization. At the same time, the method studied in this paper has the value of popularization and application for appearance defect detection in other fields.

/pdf/surface-defect-detection-method-based-on-improved-attention-3kf1y853.pdf

Guitang Wang

Papers

Interaction between parabolic pulses in a dispersion-decreasing fiber

Research on evolution region of self-similar pulses in a dispersion-decreasing fiber

Intelligent Welding Defect Detection Model on Improved R-CNN

Machine Vision Recognition of Disconnection Failure of IC Wafer

Surface Defect Detection Method Based on Improved Attention Mechanism and Feature Fusion Model