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The use of nonwoven geotextile filters is common in geoenvironmental and geotechnical engineering applications. The main requirements for successful performance of drainage and geotextile filters are soil retention, permeability and clogging resistance. In case of anti-clogging capabilities, the most popular method to evaluate the filtration behaviour of nonwoven geotextile in contact with soils is gradient ratio test. Also the number of constrictions of nonwoven geotextile should be determined on the basis of fibre diameter, thickness and porosity. The number of constrictions has been found to explain the different filtration behaviours of nonwoven geotextiles with similar or even the same opening sizes but different structures for various soil conditions. This paper presents the gradient ratio test program for internally unstable soil and nonwoven geotextiles with different structures. Test results show a significant impact of the number of constrictions on the filtration characteristics of geotextile. What is more some modifications to the filter design criteria are proposed based on interpretation of the experimental results.

Nonwoven geotextiles have been widely used as a substitute for mineral materials to provide filtration functions in civil engineering. However, in filter systems, geotextiles are the first to be in contact with soft, saturated, and fine soils. For that reason, the selection of a geotextile filter depends on the characteristics of the geosynthetics and the base soil (e.g. grain size distribution, internal stability, permeability) and on the design and boundary conditions such as continuity of the soil–geotextile filter contact interface. The geotextile filter must be properly designed to avoid clogging. The most commonly method used for measuring filtration compatibility of soil–geotextile systems is the gradient ratio test. This paper presents the gradient ratio test program for needle-punched nonwoven geotextile used as a filter layer and internally unstable soil. Laboratory tests were conducted in a modified gradient ratio test apparatus. Test results show that gradient ratio increases with time due to clogging. Also the need for a measurementofwaterheadsveryclosetothegeotextiletomeasurement of water heads very close to the geotextile to provide additional information on the soil–geotextile system behaviour were presented.

Denoising is an essential preprocessing step for most applications using synthetic aperture radar (SAR) images at different processing levels. Besides suppressing the noise, a good filter should also effectively preserve the image edge information. To quantitatively assess the edge-preservation performance of SAR filters, a number of indices have been investigated in the literature; however, most of them do not fully employ the statistical traits of the SAR image. In this paper, we review some of the typical edge-preservation assessment indices. A new referenceless index is then proposed. The ratio gradient is utilized to characterize the difference between two non-overlapping neighborhoods on opposite sides of each pixel in both the speckled and despeckled images. Based on these gradients and the statistical traits of the speckle, the proposed indicator is derived under a Bayesian framework. A series of experiments conducted with both simulated and real SAR datasets reveal that the proposed index shows good performances, in both robustness and consistency. For reproducibility, the source codes of the index and the testing datasets are provided.

Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator.

To avoid serious clogging and loss of drainage capacity, which puts the underground structure at risk of anti-floating failure, the buried drainage filter must be equipped with a nonwoven geotextile layer. In this scenario, nonwoven geotextiles are subjected to normal compressive stress, which can cause changes in geotextile porosity and structure, affecting the filtration behavior of the geotextile filter. In this paper, in order to evaluate the filtration compatibility of the soil–geotextile system, gradient ratio (GR) tests were performed under a hydraulic gradient of 1.0 using a specially designed gradient ratio filtration device capable of applying normal stress. In total four nonwoven geotextiles and two types of soil were used. The results of the gradient ratio filtration tests were discussed in terms of GR values, the permeability of the soil–geotextile system, and the amount of fines retained in geotextiles. It was shown that under a larger normal compressive stress, the GR value would also increase, while the permeability coefficient of the soil–geotextile system decreased. The filtration responses to various soil–geotextile combinations differed under normal compressive stress. A thick nonwoven geotextile with a small filtration opening size exhibited poor filtration performance while benefiting soil retention. Fines retention was influenced by geotextile thickness, soil type, and normal compressive stress magnitude. In addition, for nonwoven geotextiles filter fine-grained soil under normal compressive stress, the test results indicated that anticlogging design criteria should be improved.

To investigate the influence of uniaxial tension on the permeation characteristics of the Geotextile-sand system under different water flows, a self-developed multifunctional gradient ratio permeameter was used to conduct relevant permeation tests on three commonly used geotextiles in engineering. The study, respectively, explores the variations in seepage velocity and gradient ratio of the Geotextile-sand system under different uniaxial tension strains with unidirectional water flow and reciprocating water flow, as well as the effects of different water flows on the system under the same uniaxial tension strain. The test results indicate that the trends of gradient ratio and seepage velocity in geotextiles are consistent under different water flows; however, the gradient ratio under reciprocating flow is smaller, while the seepage velocity is greater compared to unidirectional flow.

This work focused on the nonlinear seepage behaviors of flow in pore-fracture media. Natural sandstones were selected to prefabricate single-fracture specimens with different inclinations (0–90°). Seepage tests of combined media were performed under different confining pressures (8–10 MPa) and different water pressures (3–7 MPa) in a triaxial pressure chamber. The fitting analysis of experimental data showed that Forchheimer’s law described the nonlinear characteristics of flow in the pore-fracture media. Linear term coefficient a and nonlinear term coefficient b of the sandstone samples with different inclinations changed more obviously with the increased inclination. When the fracture inclination was greater than 30°, a and b values had a sudden jump. The nonlinear inertial-parameter equation of fluid flow in pore-fracture media was proposed based on non-Darcy flow coefficient β and inherent permeability k. The applicability of the following methods to evaluate Darcy’s law was discussed, including normalized hydraulic conductivity, pressure gradient ratio, and discharge ratio. The three methods were able to determine critical parameters and distinguish linear and nonlinear flow. Furthermore, it was specified for the first time that when β was negative, critical nonlinear effect E was −0.1, and Forchheimer’s coefficient F0 was −0.091. In the −∇P-Q relationship, the fitting curve was convex to the −∇P axis, and the increase of Q was higher than the linear increase, presenting the nonlinearity of overflow. On the one hand, the fractures and pores were compressed under the confining pressure due to the prefabricated fractures of different shapes and different inclinations. A higher seepage water pressure was needed to stabilize the seepage system with the excessive flow rate. On the other hand, the barrier effect of the fluid inside the rock was completely lost because the fluid expanded the seepage channel. Its permeability was changed, leading to seepage instability.

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the combined support vector regression (SVR) algorithm. First, the gravity gradient difference ratio (GGDR) equation, which is only related to the obstacle’s position, is obtained based on the gravity gradient equation by using the difference and ratio methods. Aiming at solving the shortcomings of the GGDR equation based on Newton–Raphson method (NRM), combined with SVR algorithm, a novel SVR–gravity gradient joint method (SGJM) is proposed. Second, the differential ratio dataset is constructed by simulating the gravity gradient data generated by obstacles, and the obstacle location model is trained using SVR. Four measuring lines were selected to verify the SVR-based positioning model. The verification results show that the mean absolute error of the new method in the x, y, and z directions is less than 5.39 m, the root-mean-square error is less than 7.58 m, and the relative error is less than 4% at a distance of less than 500 m. These evaluation metrics validate the reliability of the novel SGJM-based detection of underwater obstacles. Third, comparative experiments based on the novel SGJM and traditional NRM were carried out. The experimental results show that the positioning accuracy of x and z directions in the obstacle’s position calculation based on the novel SGJM is improved by 88% and 85%, respectively.

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.

Decolorization is an image processing technique that converts a color input image into a grayscale image. This paper discusses the decolorization process and provides an overview of the methods based on the different principles used: basic conversion from RGB to YUV format using ITU Recommendations 601, 709, and 2020; basic conversion from RGB to LAB color space; the method using cumulative distribution function of color channels; one global decolorization method; and one based on deep learning. The grayscale images produced by these methods were evaluated using four objective metrics, allowing for a thorough analysis and comparison of the decolorization results. Additionally, the execution speed of the algorithms was assessed, providing insight into their performance efficiency. The results demonstrate that different metrics evaluate the decolorization methods differently, highlighting the importance of selecting an appropriate metric that aligns with the subsequent image processing tasks following decolorization. Furthermore, it was shown that the decolorization methods depend on the content of the images, performing better on natural images than on artificially generated ones. The decolorization methods were also examined in the context of object segmentation and edge detection. The results from segmentation and edge detection were aligned with the decolorization results, revealing that certain objective metrics for evaluating decolorization more effectively assessed the properties of the decolorized images, which are crucial for successful object segmentation and edge detection.