Experimental study of core MHD behavior and a novel algorithm for rational surface detection based on profile reflectometry in EAST

Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasma perturbations on the reflect signal is important.The characteristics of the reflect signal from profile reflectometry,the time-of-flight(TOF)signal associated with the MHD instabilities,are investigated in EAST.Using a 1D full-wave simulation code by the Finite-DifferenceTime-Domain(FDTD)method,it is well validated that the local density flattening could induce the discontinuity of the simulated TOF signal and an obvious change of reflect amplitude.Experimental TOF signals under different types of MHD instabilities(sawtooth,sawtooth precursors and tearing mode)are studied in detail and show agreement with the simulation.Two new improved algorithms for detecting and localizing the radial positions of the low-order rational surface,the cross-correlation and gradient threshold(CGT)method and the 2D convolutional neural network approach(CNN)are presented for the first time.It is concluded that TOF signal analysis from profile reflectometry can provide a straightforward and localized measurement of the plasma perturbation from the edge to the core simultaneously and may be a complement or correction to the q-profile control,which will be beneficial for the advanced tokamak operation.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Lightweight Surface Litter Detection Algorithm Based on Improved YOLOv5s

In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower,a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels.The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network;introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce computational effort.Using a Convolutional Block Attention Mechanism(CBAM)module in the backbone network to strengthen the network’s ability to extract significant target features from images.Finally,the loss function is optimized using the Focal-EIoU loss func-tion to improve the convergence speed and model accuracy.The experimental results illustrate that the improved algorithm outperforms the original Yolov5s in all aspects of the homemade water surface litter dataset and has certain advantages over some current mainstream algorithms in terms of model size,detection accuracy,and speed,which can deal with the problems of real-time detection of water surface litter in real life.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Research on Surface Defect Detection Method of E-TPU Midsole Based on Machine Vision

In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole.

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

For surface defects in electronic water pump shells,the manual detection efficiency is low,prone to misdetection and leak detection,and encounters problems,such as uncertainty.To improve the speed and accuracy of surface defect detection,a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods.In this method,the MobileNetV3 module replaces the backbone network of YOLOv5s,depth-separable convolution is introduced,the parameters and calculations are reduced,and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy.A dataset of electronic pump shell defects is established,and the performance of the improved method is evaluated by comparing it with that of the original method.The results show that the parameters and FLOPs are reduced by 49.83%and 61.59%,respectively,compared with the original YOLOv5s model,and the detection accuracy is improved by 1.74%,which is an indication of the superiority of the improved method.To further verify the universality of the improved method,it is compared with the results using the original method on the PASCALVOC2007 dataset,which verifies that it yields better performance.In summary,the improved lightweight method can be used for the real-time detection of electronic water pump shell defects.

DLF-YOLOF:an improved YOLOF-based surface defect detection for steel plate

Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of small defect are still unsatisfactory.An improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called DLF-YOLOF.First,the anchor-free detector is used to reduce the network hyperparameters.Secondly,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature maps.Also,the soft non-maximum suppression is used to improve detection accuracy significantly.Finally,data augmentation is performed for small defect objects during training to improve detection accuracy.Experiments show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,respectively.This shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.

Detecting Climate Change in Using Extreme Data from Two Surface Weather Stations: Case Study Valle of Comitan and La Esperanza, Chiapas, Mexico

The study area is located between the cities of Comitan (16°10'43"N and 92°04'20''W) a city with 150,000 inhabitants and La Esperanza (16°9'15''N and 91°52'5''W) a town with 3000 inhabitants. Both weather stations are 30 km from each other in the Chiapas State, México. 54 years of daily records of the series of maximum (tmax) and minimum temperatures (tmin) of the weather station 07205 Comitan that is on top of a house and 30 years of daily records of the weather station 07374 La Esperanza were analyzed. The objective is to analyze the evidence of climate change in the Comitan valley. 2.07% and 19.04% of missing data were filled, respectively, with the WS method. In order to verify homogeneity three methods were used: Standard Normal Homogeneity Test (SNHT), the Von Neumann method and the Buishand method. The heterogeneous series were homogenized using climatol. The trends of tmax and tmin for both weather stations were analyzed by simple linear regression, Sperman’s rho and Mann-Kendall tests. The Mann-Kendal test method confirmed the warming trend at the Comitan station for both variables with ZMK statistic values equal to 1.57 (statistically not significant) and 4.64 (statistically significant). However, for the Esperanza station, it determined a cooling trend for tmin and a slight non-significant warming for tmax with a ZMK statistic of -2.27 (statistically significant) and 1.16 (statistically not significant), for a significance level α = 0.05.

A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades

Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.

Detection of water surface capillary wave by analysis of turning-point local signal data using a laser interferometer

A laser interferometry technique is developed to detect water surface capillary waves caused by an impinging acoustic pressure field. The frequency and amplitude of the water surface capillary waves can be estimated from the local signal data at some special points of the phase modulated interference signal, which is called the turning points. Demodulation principles are proposed to explain this method. Experiments are conducted under conditions of different intensity and different frequency driving acoustic signals. The results show the local signal data analysis can effectively estimate the amplitude and frequency of water surface capillary waves.

A Numerical Approach to Design the Kretschmann Configuration Based Refractive Index Graphene-MoS_(2) Hybrid Layers With TiO_(2)-SiO_(2) Nano for Formalin Detection

In this paper,a Kretschmann configuration based surface plasmon resonance(SPR)sensor is numerically designed using graphene-MoS_(2) hybrid structure TiO_(2)-SiO_(2) nano particles for formalin detection.In this design,the observations of SPR angle versus minimum reflectance and SPR frequency(FSPR)versus maximum transmittance(Tmax)are considered.The chitosan is used as probe legend to perform reaction with the formalin(40%formaldehyde)which acts as target legend.In this paper,both graphene and MoS_(2) are used as biomolecular acknowledgment element(BAE)and TiO_(2) as well as SiO_(2) bilayers is used to improve the sensitivity of the sensor.The numerical results show that the variation of FSPR and SPR angles for inappropriate sensing of formalin is quite insignificant which confirms the absence of formalin.On the other hand,these variations for appropriate sensing are considerably significant that confirm the presence of formalin.At the end of this article,the variation of sensitivity of the proposed biosensor is measured in corresponding to the increment of a refractive index with a refractive index step 0.01 refractive index unit(RIU).In inclusion of TiO_(2)-SiO_(2) bilayers with graphene-MoS_(2),a maximum sensitivity of 85.375%is numerically calculated.