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For high-performance thermoplastic materials, material behavior results from the degree of crystallization and the distribution of crystalline phases. Due to the less stiff amorphous and the stiffer and anisotropic crystalline phases, the microstructural properties are inhomogeneous. Thus, imaging of the microstructure is an important tool to characterize the process-induced morphology and the resulting properties. Using focusing ultrasonic transducers with high frequency (25 MHz nominal center frequency) enables the imaging of specimens with high lateral resolution, while wave propagation is related to the elastic modulus, density and damping of the medium. The present work shows experimental results of high-frequency ultrasonic spectroscopy (HF-US) applied to injection-molded polyether-ether-ketone (PEEK) tensile specimens with different process-related morphologies. This work presents different analysis procedures, e.g., backwall echo, time of flight and Fourier-transformed time signals, facilitating the mapping of gradual mechanical properties and assigning them to different crystalline content and morphological zones. [ABSTRACT FROM AUTHOR]

Aiming at the problems of color distortion, noise interference and long running time in the dark channel prior algorithm with large depth of field, an image defogging method based on multi-scale wavelet transform and improved fusion dark channel was proposed. Firstly, the foggy image was decomposed by two-level wavelet transform, the high-frequency component was denoised by soft threshold, and the low-frequency component was defogged by improved adaptive fusion dark channel. Finally, a local linear model was used to correlate the high-frequency and low-frequency component coefficients for wavelet reconstruction. The experiment show that the proposed algorithm has high defogging effect and can improve the quality of defogging image. [ABSTRACT FROM AUTHOR]

The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture. [ABSTRACT FROM AUTHOR]

Highlights • Single end cable and overhead line protection in MTDC grids using morphological gradient of voltage (MGV); • Discrimination between internal and external faults; • Protection stability against breaker opening and opposite pole faults; • Threshold setting based on line series inductor and maximum fault resistance; • Fast, reliable and low sampling frequency protection up to a significant high fault resistance. Abstract This paper presents a new method for the protection of transmission lines in voltage source converter based multi-terminal HVDC grids. The fault generated travelling waves at the faulted line ends are filtered by series inductors. The filtered voltage waveform processed by morphological gradient accompanied by fault voltage drop is employed for fast single-end protection of HVDC lines. The proposed method is stable against external DC and AC faults, as well as faults at opposite pole and the breaker opening transients from the adjacent lines. Moreover, it does not require communications between the line ends, it is capable to detect and discriminate relatively high resistance faults, and uses relatively low sampling rate. The effectiveness of the proposed protection method is validated by simulation study on a four-terminal HVDC grid including modular multilevel voltage source converters. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the requirements for high-precision products could not be satisfied due to the low cutting precision of traditional automatic cutting machines, a method of using machine vision technology to improve the cutting precision of the cutting machine was proposed by studying the structure of the cutting machine. Firstly, the improved morphological gradient filter operator was used to find the rough edge of the sheet, and then the sub-pixel fine positioning was performed by the gray scale method. Finally, the sub-pixel edge points of meeting the requirements were fitted into a straight line by least square method. Then the offset of the sheet was calculated according to the geometric relationship, and was used to guide the correction platform to compensate the deviation. The method was simulated and tested by image edge positioning experiment designed by Matlab. The results indicate that the cutting precision of the traditional automatic cutting machine can reach 0. 03 mm theoretically, which meets the precision requirements of most high-precision products. [ABSTRACT FROM AUTHOR]

Abstract Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors. Highlights • Mapping channel edges is a significant issue in 3D seismic interpretation. • Curvelet transform is a proper scale-space representation for curve singularities. • Curvelet transform is used to enhance channel boundaries in seismic horizon slice. • Morphological gradient algorithm detects channel edges. • The experimental results are comparable with the Canny, Sobel and LoG filters. [ABSTRACT FROM AUTHOR]

This study proposes a novel scheme using mathematical morphology to effectively discriminate between the internal fault and inrush currents of power transformers. The proposed scheme consists of two parts. The first part is based on morphological gradient (MG), for discrimination of currents occurring without considering the current transformer (CT) saturation. The second one uses a symmetrical criterion involving MG to identify internal fault current under CT saturation. To demonstrate its compatibility and efficiency, the scheme is verified employing the data simulated using PSCAD/EMTDC and data collected from laboratory experiment, respectively. The simulation and experimental results indicate that the proposed scheme can improve the accuracy of inrush current identification, even under certain extreme conditions. [ABSTRACT FROM AUTHOR]

In dealing with low contrast or borderline blurred liver CT images with traditional level set algorithm, the evolving curve often stopped in local gradient minimal regions or false edges. In order to solve this problem, this paper proposed a level set segmentation method based on parameterized morphological gradient modification to study. Firstly, it transformed image's morphological gradient to enhance its contrast. Then further it set up function relationship between structural elements radius and gradient within specific neighborhood and modified image gradient, therefore, to enhance the polymerization degree of the target edge, removed the noise and local minimal caused by irregular detail and reduced the migration of object's contour position at the same time. Finally, it used level set method to segment single or multiple targets on consideration of the image gradient information. The experimental results show that the algorithm effectively solves the problem of pseudo edge segmentation in standard level set method. The liver tumor can be segmented with this algorithm accurately. [ABSTRACT FROM AUTHOR]

Highlights: [•] We presents focusing on an object of interest in a thermal infrared (IR) image. [•] Thermal IR images do not have strong edge components of an object. [•] Focusing on an object must retain prominent features of the object for recognition. [•] Morphological gradient operator outperforms conventional gradient operators. [Copyright &y& Elsevier]

Abstract: This paper presents a novel wavelet decomposition scheme, named adaptive morphological gradient lifting wavelet (AMGLW), for detecting bearing defects. The adaptability of the AMGLW consists in that the scheme can select between two filters, mean the average filter and morphological gradient filter, to update the approximation signal based on the local gradient of the analyzed signal. Both a simulated signal and vibration signals acquired from bearing are employed to evaluate and compare the proposed AMGLW scheme with the traditional linear wavelet transform (LWT) and another adaptive lifting wavelet (ALW) developed in literature. Experimental results reveal that the AMGLW outperforms the LW and ALW obviously for detecting bearing defects. The impulsive components can be enhanced and the noise can be depressed simultaneously by the presented AMGLW scheme. Thus the fault characteristic frequencies of bearing can be clearly identified. Furthermore, the AMGLW gets an advantage over LW in computation efficiency. It is quite suitable for online condition monitoring of bearings and other rotating machineries. [Copyright &y& Elsevier]