A speckle noise suppression method based on surface waves investigation and monitoring data

The internal energy distribution of waves can be described using ocean-wave spectra.In many ways,obtaining wave spectra on a global scale is critical.Surface waves investigation and monitoring onboard the Chinese-French oceanography satellite is the first space-borne instrument for detecting wave spectra specially,which was launched on October 29,2018.It can avoid the shortage of synthetic aperture radar detection results while still having some problems,especially with the effects of speckle noise.In this study,a method to suppress the speckle noise is proposed.First,the empirical formula for background speckle noise is established.Second,many spatio-temporal representative fluctuation spectra are classified and averaged.Third,rational transfer function filtering is used to obtain speckle noise close to the along-track direction.Finally,a signal-to-noise ratio threshold is used to suppress the abnormal speckle noise.This method solves the problems existing in previous denoising methods,such as excessive denoising in the along-track direction and the inability of some abnormal noises to be denoised in the two-dimensional directional wave spectra.

Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

GF-3 InSAR Achieves Sub-centimeter Level Surface Subsidence Monitoring

The GF-3 satellite was launched on August 10,2016 from the Taiyuan Satellite Launch Center and was put into operation at the end of January,2017.It has acquired nearly 100 thousand C-band multi-polarization ocean and land SAR images,providing data support for many departments covering resource survey,typhoon early warning,disaster assessment,crop yield estimation and polar investigation.Recently,the team led by ZHANG Qingjun from

Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting

Surface roughness is one of the most critical attributes of machined components,especially those used in high-performance systems.Online surface roughness monitoring offers advancements comparable to post-process inspection methods,reducing inspection time and costs and concurrently reducing the likelihood of defects.Currently,online monitoring approaches for surface roughness are constrained by several limitations,including the reliance on handcrafted feature extraction,which necessitates the involvement of human experts and entails time-consuming processes.Moreover,the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental settings.To address these challenges,this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting conditions.Tooltip acceleration signals were acquired during each cutting experiment to develop two datasets,and no handcrafted features were extracted.Five deep learning models were developed and evaluated using standard performance metrics.A convolutional neural network stacked on a long short-term memory network outperformed all other network models,yielding exceptional results,including a mean absolute percentage error as low as 1.51%and an R2value of 96.6%.Furthermore,the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously employed.The performance of the model remained consistent and commendable under varied conditions,asserting its applicability in real-world scenarios.

Instantly Investigating the Adsorption of Polymeric Corrosion Inhibitors on Magnesium Alloys by Surface Analysis under Ambient Conditions

Surface engineering of magnesium alloys requires adequate strategies, processes and materials permitting corrosion protection. Liquid formulations containing corrosion inhibitors often are to be optimized according to the demands of the respective substrate and following the service conditions during its application. As an interdisciplinary approach, a combination of several techniques for instantly monitoring or elaborately analyzing the surface state of magnesium was accomplished in order to characterize the performance of new adsorbing sustainable amphiphilic polymers which recently were developed to facilitate a multi-metal corrosion protection approach. The application of established techniques like Contact Angle measurements and X-ray Photoelectron Spectroscopy investigations was supplemented by introducing related and yet faster online-capable and larger-scale techniques like Aerosol Wetting Test and Optically Stimulated Electron Emission. Moreover, an inexpensive setup was configured for scaling the inset and the extent of degradation processes which occur at local electrochemical circuits and lead to hydrogen bubble formation. Using these analytical tools, changes of the surface state of emeried AM50 samples were investigated. Even in contact with water, being a moderate corrosive medium, the online techniques facilitated detecting surface degradation of the unprotected magnesium alloy within some seconds. In contrast, following contact with a 1 weight% formulation of a polymeric corrosion inhibitor, surface monitoring indicated a delay of the onset of degradation processes by approximately two orders of magnitude in time. Mainly based on the spectroscopic investigations, the corrosion inhibiting effects of the investigated polymer are attributed to the adsorption of a primary polymer layer with a thickness of a few nanometers which occurs within some seconds. Immersion of magnesium for several hours brings up a protective film with around ten nanometers thickness.

Monitoring the surface evolution of a nanoporous core-shell electrocatalyst for oxygen reduction reaction

Subject Code:E01With the support by the National Natural Science Foundation of China,a collaborative study by the research groups led by Profs.Ding Yi(丁轶)and Luo Jun(罗俊)from the School of Materials Science and Engineering,Tianjin University of Technology and Prof.Liu Limin(刘利民)from Beijing