Filter Scale Research Articles

AUTOMATED DETECTION OF LUNAR RIDGES BASED ON DEM DATA

Abstract. Wrinkle ridges are a common feature in the lunar maria and record subsequent contraction of mare infill. Automatic detection of wrinkle ridges is challenging because the ridges are of irregular shapes and many ridges have been eroded and/or degraded over time. The proposed method consists of the following steps. First, as the slope can reflect the gradient changes of the ridge rims to a certain extent, the slope map is generated and converted to a grayscale (0–255) image. Then the phase symmetry of the slope map is calculated with filter wavelength and filter scales parameters, which reduce the regions into symmetry regions. Next, a regional threshold is applied to limit the ridge candidates. Candidates with values less than the threshold are rejected. Moreover, the images are processed using a series of morphological operations, such as close, open, edge linking and noise removal. Finally, after thresholding the ridge map can be obtained. An experiment was performed using Lunar Reconnaissance Orbiter Camera (LROC) WAC image and topographic data from LOLA, the results demonstrate promising performance with detection percentage from 73 to 90.

Stochastic Spectral Method for Radar-Based Probabilistic Precipitation Nowcasting

AbstractNowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can produce reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h−1). For higher intensities (above 5 mm h−1), adequate skill can be obtained up to 15 min.

Performance optimization for a phase-sensitive optical time-domain reflectometry based on multiscale matched filtering

Phase-sensitive optical time-domain reflectometry (ϕ-OTDR) has been widely used to interrogate multipoint vibration events in the health monitoring of large-scale infrastructures. Since high signal-to-noise ratio (SNR) is the core parameter for evaluating the performance of ϕ-OTDR, many researchers have presented digital signal processing (DSP) methods for SNR enhancement. However, the DSP methods using fixed parameters cannot achieve the optimum SNR for different vibration events. In addition, although transform domain analysis methods such as wavelet transform could provide multiscale observation for target events, a large amount of computation would be required simultaneously. Matched filtering is a commonly used technique to extract noisy signals in traditional wireless radar systems. Mathematical theory has indicated that matched filtering is also suitable for signal extraction in ϕ-OTDR. The optimization of system performance could be realized when the filter scale matches the length of vibration events. For different lengths of disturbance region, a multiscale matching filtering method has been proposed, which makes it possible to choose appropriate filter scale and finally obtain the optimal SNR. Experimental results have shown that the proposed multiscale matching filtering method could improve the SNR by over 6 dB even under strong noise influence and reach the lowest locating uncertainty of 0.49 m with low time consumption, compared to conventional methods.

Robust Long-Term Tracking via Instance-Specific Proposals

Correlation filter-based trackers are able to achieve long-term tracking when an additional detector is available. However, it is still challenging to achieve robust and accurate tracking due to several complicated situations, including occlusion and severe deformation. This is because a simple model is difficult to adapt to dramatic appearance changes of the object. Furthermore, redetection results are sometimes unreliable as the detector is trained on only a limited number of samples. In this paper, we propose a tracker named Complementary Learners with Instance-specific Proposals (CLIP). This tracker consists of three major components, including a translation filter, a scale filter, and an error correction (EC) module. The translation filter incorporates complementary features to handle severe target appearance variations. It is further combined with the scale filter to predict the state of the target. Finally, an adaptive updating mechanism is proposed to balance the stability and flexibility of the tracking model. Moreover, an instance-specific proposal generator is embedded into the EC module to recover the lost target from tracking failures. The experimental results on OTB2015, VOT2016, Temple-Color 128, and UAV20L demonstrate that the proposed CLIP tracker achieves comparable performance with the state-of-the-art trackers on several scenarios, including occlusion, deformation, and out-of-plane rotation. Moreover, the CLIP tracker is able to run at a speed of 35 frames/s on OTB2015, which makes it highly suitable for many real-time applications.

Removal of Fe (II) ions from drinking water using Electrocoagulation (EC) process: Parametric optimization and kinetic study

Presence of Fe (II) ions in drinking water across different parts of India is a serious health concern in recent years. To reduce the Fe (II) ions in drinking water, researches are now being carried out using efficient and economical methods like adsorption, electrocoagulation etc. In the present work, experimental studies were carried out to optimize various operating parameters of electrocoagulation (EC) for efficient removal of iron (Fe (II) ions) from drinking water. Further, total dissolved solids, turbidity, conductivity, salinity, pH was accessed to verify the quality of treated water. From experimental results it was observed that, initial pH of 6.34, current density of 2 mA cm−2 with inter-electrode distance of 1 cm, electrocoagulation time of 45 min, initial salt (NaCl) concentration of 0.33 g L-1, stirring speed of 400 RPM are the optimum operating parameters to produce drinkable water (Fe (II) ions <0.30 mg L-1) from tap water having initial Fe (II) ions of 20 mg L-1. Kinetic analysis of EC process infers that Fe (II) ion removal follows the first-order kinetic model with respect to different operating parameters. The rate constant (k) increased from 0.0352 min-1 to 0.1087 min-1 when the current density was increased from 1 mA cm-2 to 2.5 mA cm-2, due to availability of higher amounts of aluminium ion flocs during EC. Experimental studies confirm that suspended flocs generated during electrocoagulation can be separated by centrifugal methods as well as microfiltration membrane to replace laboratory scale filter paper-based methods.

3D object recognition method with multiple feature extraction from LiDAR point clouds

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.

The sub-microbarom notch in acoustic wind-filter response

The measurement of the frequency response of infrasound elements with spatial-averaging wind filters is often done by comparison with a reference sensor and with ambient noise as the excitation. Frequently, a notch appears in the response just below the microbarom band—a notch that is not explained by the acoustics of the wind filter. In fact, this notch is diagnostic of the spatial averaging of wind-associated turbulence. The frequency region of the notch is bounded above and below by regions in which excellent determinations of response can be made (1) below the notch under moderate- to high-wind conditions where the scale of the turbulence exceeds the scale of the wind filter rendering the wind filter ineffective and (2) in the microbarom region where the acoustic component is strong and coherent across the entire wind-filter aperture. Furthermore, the phase of the response is not affected in the region of the notch. Consequently, the true acoustic response can be estimated in the notch region in several ways. It would, however, be a mistake to ignore the information about the effectiveness of the wind filter that, in effect, creates the notch.

SWI processing using a local phase difference modulated venous enhancement filter with noise compensation

Susceptibility weighted imaging (SWI) involves post-processing of gradient echo images which are sensitive to the spatial variations in magnetic susceptibility. The aim of this study is to develop an automated filtering scheme to enhance the contrast-to-noise ratio (CNR) and blooming on SWI. Here, the high-pass filtering for SWI processing is designed by applying a weighting function to the neighboring phase differences to enhance the susceptibility-related (SuR) contrast. This is accomplished by summing the neighboring phase differences, weighted with a scaled and shifted error function of the phase difference. Besides using the filter weights of this weighted high-pass (WHP) filter to minimize the filtering artefacts using a filter scale parameter, the CNR is further increased by introduction of the neighborhood-based noise compensation weights into the filtering process. These weights are deduced from the channel phase distribution, conditioned on the channel magnitude and noise variance. Using in vivo SWI data acquired at 1.5 T (16 nos.) and 3.0 T (30 nos.), the magnitude SWI processed using the noise compensated WHP (WHPC) filter is shown to provide an average CNR improvement of 68.40% over that of a homodyne high-pass (HHP) filter. Two tailed t-tests performed separately for different field strengths, show significant differences (p < 0.001) between mean separations of phase masks generated from the WHPC and HHP filtered phase images. In conclusion, the WHPC filter, tuned by the mean separation of the phase mask, enhances the SuR contrast of magnitude SWI for evaluation of mild cognitive impairments, brain tumor and hemorrhagic stroke.

Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis

Neurosarcoidosis and primary central nervous system lymphomas, although distinct disease entities, can both have overlapping neuroimaging findings. The purpose of our preliminary study was to assess if magnetic resonance texture analysis can differentiate parenchymal mass-like neurosarcoidosis granulomas from primary central nervous system lymphomas. A total of nine patients was evaluated, four with parenchymal neurosarcoidosis granulomas and five with primary central nervous system lymphomas. Magnetic resonance texture analysis was performed with commercial software using a filtration histogram technique. Texture features of different sizes and variations in signal intensity were extracted at six different spatial scale filters, followed by feature quantification using statistical and histogram parameters and 36 features were analysed for each sequence (T1-weighted, T2-weighted, fluid-attenuated inversion recovery, diffusion-weighted, apparent diffusion coefficient, T1-post contrast). The non-parametric Mann-Whitney test was used to evaluate the differences between different texture parameters. The differences in distribution of entropy on T2-weighted imaging, apparent diffusion coefficient and T1-weighted post-contrast images were statistically significant on all spatial scale filters. Magnetic resonance texture analysis using medium and coarse spatial scale filters was especially useful in discriminating neurosarcoidosis from primary central nervous system lymphomas for mean, mean positive pixels, kurtosis, and skewness on diffusion-weighted imaging ( P < 0.004-0.030). At spatial scale filter 5, entropy on T2-weighted imaging ( P = 0.001) was the most useful discriminator with a cut-off value of 6.12 ( P = 0.001, area under the curve (AUC)-1, sensitivity (Sn)-100%, specificity (Sp)-100%), followed by kurtosis and skewness on diffusion-weighted imaging with a cut-off value of -0.565 ( P = 0.011, AUC-0.97, Sn-100%, Sp-83%) and-0.365 ( P = 0.008, AUC-0.98, Sn-100%, Sp-100%) respectively. Filtration histogram-based magnetic resonance texture analysis appears to be a promising modality to distinguish parenchymal neurosarcoidosis granulomas from primary central nervous system lymphomas.

Smoothness-Increasing Accuracy-Conserving (SIAC) Filtering for Discontinuous Galerkin Solutions over Nonuniform Meshes: Superconvergence and Optimal Accuracy

Smoothness-increasing accuracy-conserving (SIAC) filtering is an area of increasing interest because it can extract the “hidden accuracy” in discontinuous Galerkin (DG) solutions. It has been shown that by applying a SIAC filter to a DG solution, the accuracy order of the DG solution improves from order k+1 to order 2k+1 for linear hyperbolic equations over uniform meshes. However, applying a SIAC filter over nonuniform meshes is difficult, and the quality of filtered solutions is usually unsatisfactory applied to approximations defined on nonuniform meshes. The applicability to such approximations over nonuniform meshes is the biggest obstacle to the development of a SIAC filter. The purpose of this paper is twofold: to study the connection between the error of the filtered solution and the nonuniform mesh and to develop a filter scaling that approximates the optimal error reduction. First, through analyzing the error estimates for SIAC filtering, we computationally establish for the first time a relation between the filtered solutions and the unstructuredness of nonuniform meshes. Further, we demonstrate that there exists an optimal accuracy of the filtered solution for a given nonuniform mesh and that it is possible to obtain this optimal accuracy by the method we propose, an optimal filter scaling. By applying the newly designed filter scaling over nonuniform meshes, the filtered solution has demonstrated improvement in accuracy order as well as reducing the error compared to the original DG solution. Finally, we apply the proposed methods over a large number of nonuniform meshes and compare the performance with existing methods to demonstrate the superiority of our method.

Student's &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$t$&lt;/tex-math&gt; &lt;/inline-formula&gt;-Filters for Noise Scale Estimation

In this letter, we analyze certain student's $t$ -filters for linear Gaussian systems with misspecified noise covariances. It is shown that under appropriate conditions, the filter both estimates the state and re-scales the noise covariance matrices in a Kullback–Leibler optimal fashion. If the noise covariances are misscaled by a common scalar, then the re-scaling is asymptotically exact. We also compare the student's $t$ -filter scale estimates to the maximum-likelihood estimates. Simulations demonstrating the results on the Wiener velocity model are provided.

Habitats and cardinal directions are key variables structuring spider leaf litter assemblages under Searsia lancea

Spiders are a prominent component of leaf litter assemblages, where they function as critical predators of other invertebrates. We investigated the influence of habitat, cardinal directions and position on Searsia lancea litter-dwelling spider assemblages in central South Africa. A total of 1521 spiders were collected, representing 27 families and 77 species. Species richness, abundance and coverage per tree were highest in the grassland, followed by the hillside and riparian habitats. Neither of the finer scale filters, direction nor position, had significant effects on species richness or abundance, although there was weak evidence for an increase in spider abundance toward the periphery of a tree (p = 0.09) and a decrease in abundance towards the western cardinal direction (p = 0.08). Species composition, though, was significantly affected by habitat (p = 0.001) and direction (p = 0.014), as well as a three way-interaction between habitat, direction and position (p = 0.01). Hillside assemblage structure is intermediate between the other two habitats. Species abundance distributions of all three habitats were multimodal, indicating disturbance. Particularly, the log-series distribution of both the grassland and riparian woodland point to more disturbed assemblages than the hillside (Poisson log-normal distribution). Habitat therefore played a significant role in affecting richness, abundance and assemblage structure (composition and abundance). Although finer scale variables only had a weak effect on abundance, spider assemblages were influenced by direction, particularly for the climatically more variable habitats of the grassland and hillside. The significantly higher evenness and less disturbed nature of the hillside emphasize the conservation significance of this habitat.

RETRACTED ARTICLE: The visual object tracking algorithm research based on adaptive combination kernel

In order to enhance the robustness to complicated changes of multiple objects and complex background scene, the visual object tracking algorithm based on Adaptive Combination Kernel has been proposed in the paper. The object tracking procedure has been decomposed into two subtasks: Translation Filter and Scale Filter to estimate the object’s details. Firstly, the Translation Kernel Tracker has used the adaptive combination of Linear Kernel Filter and Gaussian Kernel Filter. The objective function has been developed to obtain the weight coefficients for Linear Kernel filter and the Gaussian Kernel filter, which incorporates not only empirical risk but also maximum value of response output for each kernel. The Adaptive Combination Kernel has the advantages of both local kernel and global kernel. Secondly, the tracking position has been calculated according to the response output of adaptive combination kernel correlation filter. Thirdly, according to the maximum response value, the scene-adaptive learning rate has been designed in the translation filter. The translation filter can be updated with the adaptive learning rate. Finally, one-dimensional scale filter has been used to estimate the object scale. The extensive experimental results have shown that the proposed algorithm is optimal on OTB-50 dataset in success rate and distance precision parameters, which is 6.8 percentage points and 4.1% points than those of KCF and is 2.0 percentage points and 3.2% points than those of BSET. The proposed algorithm has better robustness to the deformation and occlusion than others.

Scale Self-Adaption Tracking Method of Defog-PSA-Kcf Defogging and Dimensionality Reduction of Foreign Matter Intrusion Along Railway Lines

In foggy weather, the occlusion of smoke dust and the variation of imaging scale of foreign objects will mislead the existing algorithm to learn the wrong target information, causing the drift of the tracking box. A kernel correlation filtering target tracking dimensionality reduction algorithm combining dark channel prior and scale estimation is proposed, effectively improve target tracking accuracy in foggy weather. Firstly, in the process of detecting intruding foreign objects along the railway with visual background extractor ViBe, the atmospheric scattering model based on dark channel prior was used to defog video sequence. After that, FHOG features of the initial tracking box were extracted by dense cyclic sampling and scale pyramid technology, and then a Kcf position filter, as well as a scaling filter with PCA dimensionality reduction was trained to realize the scale-adaptive rapid tracking of railway foreign objects in foggy weather. The experimental results show that in terms of tracking accuracy, the proposed Defog-PSA-Kcf algorithm is superior to the non-scale estimation link the generation algorithm Mean Shift devoid of scale estimation and the native Kernel Correlation Filter algorithm (Kcf), and while linear kernel algorithm Dual Correlation Filter algorithm (Dcf), which is higher than the scale-adaptive SA-Kcf and the SAMF algorithm; in terms of tracking speed, it's faster than the Mean Shift, SA-Kcf, and SAMF algorithms, and in tracking speed. The algorithm can achieve fast tracking results comparable to as fast as the Kcf algorithm.

A fast algorithm to racetrack filter multiaxial histories preserving load shape

A fast algorithm is presented to implement a multiaxial version of the racetrack amplitude filter, improved upon the authors’ previous works. The algorithm only requires a single user-defined scalar filter amplitude to eliminate noise and potentially non-damaging events from arbitrary non-proportional multiaxial stress or strain histories, while preserving all key features of the load path. It can be applied as a pre-processing step to both invariant-based and critical-plane multiaxial damage models, highly decreasing the computational cost in assessments involving oversampled or noisy data, much improving the usefulness of computationally-intensive multiaxial models. Since load path shape is well preserved (within the chosen filter resolution), peaks and valleys from subsequent candidate plane projections are not filtered out. Moreover, path-equivalent stress or strain calculations can still be applied after the filtering process, without precision loss beyond the filter resolution. The algorithm is synchronous and preserves load order, becoming suitable for strain-based multiaxial damage models involving plastic memory, as well as for mixed-mode crack propagation calculations. The filter efficiency is validated from oversampled tension-torsion experimental data in 316L stainless steel tubular specimens, following complex non-proportional histories. Finally, a fully-tested computer implementation of the algorithm is presented.

Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter

Tracking methods based on a correlation filter have attracted much attention because of their high efficiency and strong robustness. However, a tracker based on a single feature is obviously not sufficient to adapt to the complex appearance changes of the target. Besides, rapid and exact scale estimation is still a challenging problem in the field of visual tracking. In this paper, we introduce an independent scale filter for the estimation of the scale of an object and merge two complementary features to further boost the performance of the tracker. At the same time, a dimension reduction strategy is adopted to decrease the computational load. Finally, a dynamic learning rate-based model update mechanism is inserted to effectively alleviate model degradation problem by suppressing the influence of noisy appearance changes. The extensive experiments were conducted on the object tracking benchmark (OTB) dataset and Temple color 128 dataset. The quantitative and qualitative results exhibit that compared with other popular trackers, the tracker proposed in this paper acquires favorable results in tracking accuracy, efficiency, and robustness. On the OTB-2015 benchmark dataset, it obtains precision scores of 0.773, 0.782, and 0.714 and success scores of 0.585, 0.606, and 0.534 in the three indexes of OPE, TRE, and SRE. On the Temple color 128 dataset, it acquires precision scores of 0.641, 0.681, and 0.606 and success scores of 0.478, 0.515, and 0.445 in the three indexes of OPE, TRE, and SRE, surpassing many well-known tracking methods. In terms of tracking efficiency, it runs at a speed of 42.3 frames/s on a single CPU, making it suitable for real-time applications.

Long term filtration modelling and scaling up of mixed matrix ultrafiltration hollow fiber membrane: a case study of chromium(VI) removal

Mixed matrix membrane (MMM) belongs to the special class of filter where adsorptive removal of smaller sized solutes is realized by more open pore sized ultrafiltration grade membrane operated at higher throughput. Incorporation of nanoparticles having adsorption tendency towards a specific smaller sized solute in the polymer matrix is responsible for such a behavior. Since solute adsorption is the dominant mechanism of removal, the breakthrough behavior of the membrane dictates its life indicating its regeneration or eventual replacement. Thus, quantification of long time fouling behavior of a scaled up MMM filter is of utmost importance. A detailed two-dimensional transient model is formulated in this work starting from first principles using equations of continuity, motion and convective-diffusive-adsorption based solute transport for filtration through hollow fiber MMM module. The set of governing equations is solved using finite element method by COMSOL v5.3® software package. A case study was considered for adsorptive removal of chromium(VI) using graphene oxide incorporated MMM hollow fibers. The model parameters were evaluated using the experimental long term filtration data of synthetic chromium(VI) solution. The same set of model parameters was used in predictive mode to quantify the long-term performance behavior of a real life chrome tanning effluent that showed remarkable predictive capability of the model in terms of throughput and rejection characteristics of chromium(VI). The validated model is further extended for simulation of large scale filter with high breakthrough volume interrelating number and length of fibers (nf and L) with the operating conditions (trans-membrane pressure drop, TMP and cross flow rates, CFR) so that the filter can sustain its performance over long period of time (i.e., long filter life). Based on the simulation, at a fixed TMP and CFR, a performance curve was generated linking nf, L and breakthrough volume of the filter that can be used as a guideline for designing of large scale filtration units with hollow fiber MMM for industrial applications. In general, the theoretical framework formulated in this work can be used as a foundation for design and scale up of any hollow fiber MMM based filtration system.

Growth of clogs in parallel microchannels

During the transport of colloidal suspensions in microchannels, the deposition of particles can lead to the formation of clogs, typically at constrictions. Once a clog is formed in a microchannel, advected particles form an aggregate upstream from the site of the blockage. This aggregate grows over time, which leads to a dramatic reduction of the flow rate. In this paper, we present a model that predicts the growth of the aggregate formed upon clogging of a microchannel. We develop an analytical description that captures the time evolution of the volume of the aggregate, as confirmed by experiments performed using a pressure-driven suspension flow in a microfluidic device. We show that the growth of the aggregate increases the hydraulic resistance in the channel and leads to a drop in the flow rate of the suspensions. We then derive a model for the growth of aggregates in multiple parallel microchannels where the clogging events are described using a stochastic approach. The aggregate growths in the different channels are coupled. Our work illustrates the critical influence of clogging events on the evolution of the flow rate in microchannels. The coupled dynamics of the aggregates described here for parallel channels is key to bridge clogging at the pore scale with macroscopic observations of the flow rate evolution at the filter scale.

Removal of Barite-Scale and Barite-Weighted Water- or Oil-Based-Drilling-Fluid Residue in a Single Stage

Summary Barite is one of the most common weighting materials used in drilling fluids for deep oil and gas wells. Consequently, the main source of solids forming the filter cake is “barite particles,” the weighting material used in drilling fluids. Barite is insoluble in water and acids such as hydrochloric acid (HCl) and formic, citric, and acetic acids, and barite is moderately soluble in chelating agents such as ethylenediaminetetraacetic acid (EDTA). The present study introduces a new formulation to dissolve barite scale and barite filter cake using converters and catalysts. Barite can be converted to barium carbonate (BaCO3) in a high-pH medium (pH = 12) using a combination of potassium hydroxide (KOH) and potassium carbonate (K2CO3) solutions. Subsequently, HCl or low-pH chelating agents can be used to dissolve the BaCO3. Another solution is to use the EDTA chelating agent at pH of 12 and K2CO3 or KOH as a catalyst/converter in a single step. The removal formulation also contains a polymer breaker (oxidizers or enzymes). The three components of the new formulation are compatible with each other and stable up to 300°F. Solubility experiments were conducted using industrial-grade barite (particle size of 30 to 60 µm). The solubility experiments were conducted at 300°F for 24 hours. Varying concentrations of the catalyst were added to determine the optimal concentration. The developed formulation was tested for the removal of filter cake formed by barite drilling fluid using a high-pressure/high-temperature (HP/HT) cell. Filter-cake removal was conducted for filter cakes formed by both water- and oil-based drilling fluids. The results of this study show that the barite-removal efficiency of the new formulation is 87% for water-based mud (WBM) and 83% for oil-based mud (OBM). The test results show that the solubility of barite particles in 0.6 M EDTA is 62 wt% in 24 hours at 300°F. Adding K2CO3 or KOH catalyst to the 0.6-M-EDTA solution increases the solubility of barite to 90 wt% in 24 hours at 300°F. Thus, barite scale can be removed efficiently using high-pH formulations (pH = 12) to avoid the safety issues associated with HCl. Because the EDTA chelating agent is compatible with the polymer breaker (oxidizer), the filter cake can be removed in a single stage. The concentration of the components of the formulations used in this study is as follows: 10 wt% oxidizer, 10 wt% K2CO3 or KOH concentration (catalyst/converter), and 0.6 M EDTA. The developed formulations achieved more than 80% filter-cake removal in both oil-based and water-based drilling fluids. For OBM, a water-wetting surfactant, a mutual solvent, and an emulsifier were added to the formulation to remove the oil and to make the surface of the filter cake water-wet. In this study, two solutions are proposed to remove the barite filter cake and barite scale from oil and gas wells at different conditions. The first one is using HCl after converting the barium sulfate (BaSO4) to BaCO3 by use of a high-pH medium such as KOH and K2CO3. Although HCl can easily remove the resulting BaCO3, the generated barium chloride (BaCl2) is a safety and health concern. The second method is to create a high-pH medium (pH = 12) using the removal fluid itself, which uses the EDTA chelating agent in addition to using K2CO3 or KOH as converters.

Optimization of mean-shift scale parameters on the EGEE grid.

This paper studies the optimization of Mean-Shift (MS) image filtering scale parameters. A parameter sweep experiment representing 164 days of CPU is performed on the EGEE grid. The mathematical foundations of Mean-Shift and the grid environment used for the deployment are described in details. The experiments and results are then discussed highlighting the efficiency of gradient ascent algorithm for MS parameters optimization and a number of grid observations related to data transfers, reliability, task scheduling, CPU time and usability.