Morphological Gradient Algorithm Research Articles

A self-adaptation non-unit protection scheme for MMC-HVDC grids based on the estimated fault resistance

The existing ultra-fast non-unit protection methods for modular multilevel converter (MMC) HVDC grids based on the protection boundary have shortcomings, such as a lack of adaptive ability and difficulties in threshold setting. The key point is that it is hard to obtain the status of the fault point, which makes the method that uses a fixed threshold value difficult to adapt to various fault conditions. In this paper, the fault voltage which is composed of many sub-components is analyzed based on the built equivalent circuit of MMC-HVDC grids after DC line faults. A fault resistance estimation method is proposed by utilizing the ratio of the second module maximum to the initial module maximum after filtering by a multi-resolution morphological gradient algorithm, and the approximate difference in propagation distance is obtained by using the time difference and the change directions of the initial two surges to correct the influence of distance. A self-adaptation non-unit protection scheme is proposed based on the estimated fault resistance, which consists of the start-up unit, identification of fault types, fault resistance estimation, and self-adaptation main protection. A four-terminal ring-topology MMC-HVDC grid built in PSCAD/EMTDC demonstrates the higher performance of the proposed scheme under various fault conditions.

Maize (Zea mays L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions

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.

A non-unit transmission line protection scheme for MMC-HVDC grids based on a novel distance criterion

Ultra-fast, selective, and reliable protection is significant for MMC-HVDC grids. The methods utilizing the characteristics of fault current limiting reactor (FCLR) are unsatisfactory to detect high resistance faults. In this paper, a novel distance criterion is proposed to form the non-unit protection scheme. The line-zero-mode fault equivalent circuit is built to analyze the characteristics of fault traveling waves at the inductive terminal considering the influence of MMC and the refraction and reflection at the fault point. A multi-resolution morphological gradient algorithm (MMGA) is employed to extract the geometric features of the fault traveling waves. The magnitude of the first modulus maxima of the line-mode fault voltage after MMGA constitutes the start-up criterion. The estimated velocity and the change direction of the line-mode components are adopted to identify if the fault happens in-region. The 4-terminal ±500 kV MMC-HVDC grid built in PSCAD/EMTDC demonstrates that the proposed scheme can accurately identify various faults. The sensitivity analysis shows that the proposed scheme is almost immune to the size of FCLR, the equivalent parameters of MMC, the noise environment, and the DCCB breaking process. The proposed scheme has low requirements for sampling frequency and can be utilized to identify the temporary fault.

Calculation of Size-Specific Dose Estimation (SSDE) of The Pediatric’s Head for Tube Current Modulation (TCM) CT Examinations

Computed Tomography (CT) examinations in the pediatric patient are sensitive to the impact of ionizing radiation, which gives long-term risk. In addition, the actual radiation dose delivered during CT examinations has not been well-described mainly when using tube current modulation (TCM) technique. To quantified this effect, an algorithm for calculating radiation dose is needed. Therefore, this study aims to calculate and analyze the estimated effective dose of head examinations from 3D CT images, based on the equivalent water diameter (Dw)For the TCM technique. Four pediatric patients in the 0-6-year-old age groups were examinations using Somatom Definition Flash with variation in the number of slices. CTDIvol and Tube current values were obtained from the DICOM header. There are two main parts algorithm for Dw calculation. First, images of CT data were converted to Hounsfield units (HU). Then, Dw values based on the patients contour using the K-mean cluster and morphological gradient algorithm were calculated for every slice. The coefficient factor is obtained by American Association of Physicists in Medicine (AAPM) report 220. The values then multiplied by CTDIvolto acquire values of SSDE. The results showed that the relation between SSDE and tube current was strongly correlated with R2 values of 0.982.

Channel boundary detection using a 3D morphological filter and adaptive ellipsoidal structuring element

ABSTRACTChannels are one exploratory object in the oil and gas industry, and detailed channel studies can help identify the sedimentation processes in an area. In addition, channels are a drilling hazard in deep-water sedimentary basins. However, manual interpretation of channels is time-consuming and needs skill and experience. Therefore, in this paper, using morphological filters, we present an algorithm for automatically extracting the channel edges from seismic data. We used a morphological gradient algorithm with an ellipsoidal adaptive structuring element to detect edges. Morphological gradient operators highlight the image edges within the neighbourhood of the structuring element. The adaptive structuring element at each point of the data align with the edges and contours at that point, and so can be more effective in edge detection. Eigenvalue decomposition of the gradient structure tensor gives an estimate of the orientation and anisotropy rate of objects within the data. Hence, parameters of the adaptive structuring element are obtained from the eigenvalues of a 3D gradient structure tensor. Finally, the proposed algorithm is applied to synthetic and real seismic data containing a channel, and the results are compared with other well-known edge detection methods such as Canny, Sobel, semblance and negative curvature. The proposed algorithm was able to correctly identify edge locations, and also outperforms other methods in the presence of noise.

Detection in seismic data using curvelet transform and tensor-based elliptical adaptive structuring elements

Channels are one of the vital issues in the exploration of oil and gas. They can be considered as reservoirs if they are filled with porous and permeable material and placed in a suitable position. A detailed study of the channels can help identify the sedimentation processes of the area, the intensity, and direction of the sea currents. Manual interpretation of the channels is difficult and requires skill. Therefore, in this paper, the adaptive curvelet and morphological gradient algorithm (ACMG) is used for the automatic interpretation of the channels. First, the morphological gradient is applied to extract the edges of the channels; then, the curvelet transform is used to increase the signal-to-noise ratio. The morphological top-hat operator extracts the local maxima of curvelet sub-bands. In this workflow, we applied an elliptical adaptive structuring element (EASE) based on Gradient structure tensor (GST). For the construction of the 2D GST, the horizontal and vertical gradients of the image were calculated by the first-order Gaussian derivative. The eigenvalue decomposition of each structure tensor can provide an estimate of the direction and anisotropy rate of the image objects. Hence, parameters of the elliptical structuring element are obtained by eigenvalue decomposition of the GST. To evaluate ACMG, we compared the final output with the results of the non-adaptive curvelet and morphological gradient algorithm (CMG). Also, we compared it with the common edge-detectors such as Canny, Sobel, Laplacian of Gaussian, and similarity attribute. The comparison shows that both CMG and ACMG well extracted the edge of the channels with the higher signal-to-noise ratio.

Multi-scale morphological gradient algorithm based ultra-high-speed directional transmission line protection for internal and external fault discrimination

<p>This paper introduces an ultra-high-speed directional transmission line protection scheme based on multi-scale morphological gradient algorithm (MSMGA). The directional protection scheme sets down the rules for determining the fault position in relation to the relaying point. The MSMGA is used to extract the fault-induced transient characteristics contained in the voltage and current signals. The associated signals are formed from these transient characteristics and the polarity of their local modulus maxima allow the discrimination between internal and external faults.</p>

Edge Detection of High-Voltage Porcelain Insulators in Infrared Image Using Dual Parity Morphological Gradients

A new edge detection operator based on dual parity morphological gradients is proposed, in order to accurately segment a porcelain insulator string in an infrared image before the identification and diagnosis of deteriorated insulators by the extracted thermal information. When applied to the double-shed insulators, the proposed detector is capable of effectively eliminating the blurred region between the upper and lower sheds as well as preventing over-segmentation by combining both odd and even structural elements. Compared with the multiscale morphological gradient algorithm, this technique reduces the size of the maximum structural elements to optimize the computational complexity. A quantitative evaluation using PSNR, IoU, and FPR indexes shows that the new technique outperforms other common edge detectors. In addition, the experiments on thousands of real infrared images demonstrate the detector to be highly effective despite its low complexity.

Mapping channel edges in seismic data using curvelet transform and morphological filter

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.

Current Transformer Saturation Detection Using Morphological Gradient and Morphological Decomposition and Its Hardware Implementation

In this paper, the improved morphological gradient algorithm and the morphological decomposition algorithm are used to detect the saturated sections of a secondary current, and the detection results are combined by the AND logic so as to provide a more accurate and reliable detection result. The proposed method is implemented in a system on chip, whose core is a field-programmable gate array. To evaluate the performance of the proposed method as well as its hardware implementation, a simple double-side power supply system model is built in RSCAD, and a test platform involving a real-time digital simulator is set up accordingly. The simulation results have shown that the proposed method can correctly identify the saturated sections under various conditions, and its hardware implementation has illustrated that the proposed method can be used in real time in practice.

Multi structure morphological inpainting for the recovery of damaged digitised photographs

Multi structure morphological inpainting for the recovery of damaged digitised photographs is proposed. The motivation behind this paper is to address this interesting application area in terms of Morphological image processing literature and evaluate the interpolation capabilities of the same in the context of inpainting. As the inpainting order plays an important factor for human visualisation it is guided by the orientation of edges at the surrounding known regions of the missing domain. The damaged picture is edge detected by morphological gradient algorithm based on multi-structure element morphology of eight different directions. The edge detected image is represented as eight different edge planes based on the orientation of the edges. This edge plane information is used for reconstructing the regions within the missing part in a particular orientation, as well as for guiding the integration that follows. Edge planes having edges at the boundary of the missing domain are known as positive response edge planes. The damaged regions are morphologically eroded using the structuring elements of corresponding orientations given by the positive response edge planes. The resultant image is obtained using texture synthesis incorporated isotope driven integration of the eroded images. The novelty of our approach is to explicitly specify the direction of filling herby ensuring ease in convergence in different orientations and then streamlining the integration process to guarantee complete and natural look.