Research on X-Ray Inspection of Basin Insulators and Wireless Image Sensing Technology

This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.

A Distributed Fusion Framework of Multispectral and Panchromatic Images Based on Residual Network

Remote sensing images have been widely applied in various industries; nevertheless, the resolution of such images is relatively low. Panchromatic sharpening (pan-sharpening) is a research focus in the image fusion domain of remote sensing. Pan-sharpening is used to generate high-resolution multispectral (HRMS) images making full use of low-resolution multispectral (LRMS) images and panchromatic (PAN) images. Traditional pan-sharpening has the problems of spectral distortion, ringing effect, and low resolution. The convolutional neural network (CNN) is gradually applied to pan-sharpening. Aiming at the aforementioned problems, we propose a distributed fusion framework based on residual CNN (RCNN), namely, RDFNet, which realizes the data fusion of three channels. It can make the most of the spectral information and spatial information of LRMS and PAN images. The proposed fusion network employs a distributed fusion architecture to make the best of the fusion outcome of the previous step in the fusion channel, so that the subsequent fusion acquires much more spectral and spatial information. Moreover, two feature extraction channels are used to extract the features of MS and PAN images respectively, using the residual module, and features of different scales are used for the fusion channel. In this way, spectral distortion and spatial information loss are reduced. Employing data from four different satellites to compare the proposed RDFNet, the results of the experiment show that the proposed RDFNet has superior performance in improving spatial resolution and preserving spectral information, and has good robustness and generalization in improving the fusion quality.

Introduction to the ringing effect in satellite hyperspectral atmospheric spectrometry

Atmospheric remote spectrometry from space has become in the last 20 years a key component of the Earth monitoring system: their large coverage and deci-kelvin stability have demonstrated their usefulness for weather prediction, atmospheric composition monitoring as well as climate monitoring. It is thus critical to investigate the possible sources of errors associated to this technique. One of them is the so-called "ringing error" that appears in Fourier transform spectrometers when the instrument transmission varies at the scale of the spectral resolution. This paper exposes the theoretical basis of this particular type of radiometric uncertainty. Its sensitivity to instrumental parameters as well as the impact on the radiometrically calibrated measurements is assessed in the context of atmospheric infrared sounding using Fourier transform spectrometers. It is shown that this error is an intrinsic feature of such instruments that could safely be ignored in early-generation instruments but will have to be taken into account in the new generation ones as it can yield a significant degradation of the radiometric error budget.

A new method for determination of plastic deformation properties at high strain rate and its modelling

The determination of material properties under high speed loading is a challenge. The system ringing in a conventional servo-hydraulic tensile testing machine deteriorates the quality of force measurement, which makes a precise determination of yield locus, strain hardening and fracture strain difficult. In this work, the system ringing effect of the entire tensile testing system were analyzed. It was determined that the ringing of the system is location and geometry dependent. A new type of tensile sample has been developed. Beside of the usual major plastic deformation area, it has an additional elastic area, within which a locally restricted secondary minor plastic deformation takes place. This very small plastic deformation absorbs the elastic vibrations in this area. Therefore, the deformation forces can be measured by strain gauge without any ringing effect. The plastic deformation behavior of materials can be determined for a wide range of strain rate of 0.0001 - 1000 /s exactly. To explain the functionality and the physical background of the new sample, based on the equations for one-dimensional stress waves and theory of the stress wave attenuation due to dislocation motions, a simplified beam model with analytical formulations could be established und programmed in MATLAB. Verifications show a good prediction of sample geometry using this simplified model.

Image Fusion Algorithm of Focal Region Detection and TAM-SCM Based on SHT Domain

The selection of texture information and block ringing effect in multi-focus fusion process, a new multi-focus image fusion algorithm based on Three Activity Measures (TAM) excitation Spiking Cortical Model, (SCM) in shearlet (SHT) domain is proposed. Firstly, in SHT domain, using local spatial frequency (SF), local energy of gradient (EOG) and different measurements (SF, EOG, and local laplace energy sum (SML)) motivated SCM selected the texture information and construct the initial fusion image (P). Then, the focal region was extracted from the significant feature of the difference between the image P and the original image. Finally, the joint focus area produces fusion images. To verify the superiority of the proposed algorithm, compare the results of this paper with seven competing methods. Experimental results show that the algorithm can produce clear edges, good visual perception and less distortion.