Interpolation Algorithm Research Articles

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Comparative experimental study of different fetch distances, interpolation algorithms, source substrate noise, sampling point counts, and resolution for NF of BDFA

Noise figure (NF) is a key metric for Fiber amplifier (FA), which affects the performance of signal transmission. The main factors affecting the NF are related to the amplified spontaneous emission (ASE) characteristics of the active fiber itself. However, during our study of NF, we found some easily overlooked factors that can have a significant impact on the results, such as the fetch distances and interpolation algorithm for calculating the ASE where the signal wavelength is located. Further, to date, we have also found little relevant research on whether the sampling characteristics of the instrument and the substrate noise contribute to the NF. On the other hand, bismuth-doped fiber amplifiers (BDFA) have developed rapidly in recent years. In this work, we use BDFA as an example, using different fetch distances, interpolation algorithms, substrate noise, and sampling variables (resolution and point counts), to comprehensively compare and analyze the impact caused by NF. The experimental results suggest that the distance between the sampling locations on either side of the signal and the center wavelength must exceed the signal’s bandwidth to obtain the actual NF; Linear interpolation and cubic interpolation are more suitable for calculating the NF; Even using a high power input signal of 0 dBm, the NF is only degraded by about 1.8% compared to the case where the substrate noise is ignored; In addition, the effect of different sampling point counts and resolutions have less influence on the NF. We believe this work provides a reliable reference value for calculating the accuracy of NF and will be useful for practical applications of active-fiber amplifiers.

Near real-time interpolative algorithm for modelling air quality in underground mines

As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest data reliability with the fewest possible sensors. Real-time sensors remain costly, and these costs are not exclusively financial; the time required to install, calibrate, and maintain real-time sensors poses a large barrier to widespread implementation. Current atmospheric monitoring systems typically rely on displaying point-specific values. This requires operators to infer real-time airborne contamination distributions. Monitoring and control software utilizing mine ventilation network (MVN) solvers has been implemented in limited cases because of their ability to simulate ventilation systems quickly, but these solvers use a one-dimensional representation of the mine, limiting spatial resolution of estimated distributions. Computational fluid dynamics (CFD) has likewise been considered as a means to improve spatial resolution, but processing times prevent its use as a basis for monitoring and control. For the real-time monitoring of airborne contamination distributions, we propose a spatial interpolation method that can estimate the distribution of airborne contaminants in near-real time. This method provides a middle ground between fast processing times and increased spatial resolution. With the use of a pathfinding algorithm and optimization through absolute percentage error minimization, this method outperforms spatial interpolation with a Euclidean distance. By providing contamination distribution information to operators, this method and its derivatives stand to outperform current atmospheric monitoring systems.

B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T.

CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach. B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices. The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

Integration of conventional well logs and core samples to predict porosity of tight reservoir: a case study from Ordos Basin

To the reservoirs of the oil wells with no cored data, predicting porosity from wireline logs and core samples is an effective approach. Integration of conventional well logs and core samples to predict porosity with large accuracy is a particularly challenging work due to complex logging responses of tight sandstone. Therefore, a novel predicting workflow based on linear interpolation algorithm (LIA) is described to estimate porosity from well logs in the present study. Based on core reposition, porosity correction under overburden pressure, core-log data matching, and calculation of shale content, two multi regression formulas to estimate porosity values are obtained by nearest neighbor algorithm and linear interpolation algorithm respectively. The formulas are applied to the tight sandstone in Chang 9 member of Yanchang Formation in northeast Wuqi Oilfield, Ordos Basin. The comparison results indicate that the porosity predicted from the formula obtained by LIA is in better agreement with the measured porosity, showing a better prediction effect. The application example demonstrates that the LIA formula is of good applicability for the core porosity prediction in the study region. This methodology can further be applied for porosity prediction in other oil regions that have similarities in geological background.

CFA-Based Splicing Forgery Localization Method via Statistical Analysis

The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90.

Clipped seismic record recovery analysis based on the cubic spline interpolation algorithm

Clipped seismic record recovery analysis based on the cubic spline interpolation algorithm

Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight Interpolation

The inverse distance weighting (IDW) interpolation algorithm is used to reconstruct the γ radiation field. The traditional IDW interpolation algorithm is improved. The power exponent of distance P in the IDW for each interpolation point is not fixed and varies from one point to the other point. A fitting expression of P is obtained, which is a function of the coordinates of each point and can minimize the interpolation error when the number of sampling points is specified. Afterward, the improved algorithm is used to reconstruct a γ radiation field of a single source, and the theoretic results are compared with the results from Geant4, yielding an average relative error of 7.50%. The interpolated results from the experimental measurements align well with the actual data, with an average relative error of only 0.12%. The P derived from the interpolated experimental measurement data shows an error of 2.0% compared to the power exponent obtained from the Geant4 data interpolation. Then we set up a double-source γ radiation scene experiment and measured the count rate data at the grid points. At the same time, the experiment scene was simulated by Geant4. The improved IDW algorithm could not reconstruct the double-source γ radiation field well, thus further improvement is needed.

Fast System Matrix Generation Based on Single Angle Calibration in Open-Sided Field Free Line Magnetic Particle Imaging.

Open-sided field-free line magnetic particle imaging (OS FFL MPI) is a novel medical imaging system configuration that has received significant attention in recent years. However, the measurement-based system matrix (SM) image reconstruction for OS FFL MPI typically requires multiple angle calibration (MAC), which is time-consuming in practice. To address this issue, we propose a fast 2D SM generation method that requires only a single angle calibration (SAC). The SAC method exploits the rotational invariance of the system function. Based on the measured single angle system function, the system function is rotated to generate system functions at other angles, and then the SM for image reconstruction is constructed. Then, we conducted various simulation experiments and built an OS FFL MPI scanner to evaluate the proposed SAC method. The experiments demonstrating the effectiveness of SAC in reducing calibration workload, requiring fewer scanning numbers while maintaining a similar image reconstruction quality compared to MAC method. Furthermore, the SM generated by SAC produces consistent imaging results with the SM generated by MAC, regardless of the interpolation algorithms, the number of rotation angles, or the signal-to-noise ratios employed in phantom imaging experiments. SAC has been experimentally verified to reduce acquisition time while maintaining accurate and robust reconstruction performance. The significance of SAC lies in its contribution to improving calibration efficiency in OS FFL MPI, potentially facilitating the implementation of MPI in a wider range of applications.

A deep-learning super-resolution reconstruction model of turbulent reacting flow

Super-resolution reconstruction using deep-learning techniques is a valuable tool for enhancing computational and experimental visualization of flow fields to recover detailed features from coarse results. In this study, we propose the Pyramid Res U Super Resolution (PRUSR) network as a flow reconstruction model that upgrades the basic architecture of the Downsampled Skip-Connection Multi-Scale (DSC/MS) model with the pre-activated residual block (PRB). To improve the model accuracy, we also implement a new loss function called Mean Square Structure Similarity (MSSSIM), which combines the Mean Square Error (MSE) and Structure Similarity (SSIM). We test the model on a practical problem of visualizing the three-dimensional turbulent jet flame and find that the PRUSR model accurately reconstructs the super-resolution flow field, particularly in large downsampling ratio conditions, outperforming the original DSC/MS model and traditional interpolation algorithms such as bilinear and bicubic even with fewer input data. Additionally, the PRUSR model is capable of reconstructing sub-grid turbulent flow and flame thickness by inferring high-frequency information in the inertial subrange, making it a powerful post-processing tool for turbulent flow visualization and analysis.

A trocar puncture robot for assisting venipuncture blood collection

Abstract The venous blood test is a prevalent auxiliary medical diagnostic method. Venous blood collection equipment can improve blood collection’s success rate and stability, reduce the workload of medical staff, and improve the efficiency of diagnosis and treatment. This study proposed a rigid-flexible composite puncture (RFCP) strategy, based on which a small 7-degree-of-freedom (DOF) auxiliary venipuncture blood collection (VPBC) robot using a trocar needle was designed. The robot consists of a position and orientation adjustment mechanism and a RFCP end-effector, which can perform RFCP to avoid piercing the blood vessel’s lower wall during puncture. The inverse kinematics solution and validation of the robot were analyzed based on the differential evolution algorithm, after which the quintic polynomial interpolation algorithm was applied to achieve the robot trajectory planning control. Finally, the VPBC robot prototype was developed for experiments. The trajectory planning experiment verified the correctness of the inverse kinematics solution and trajectory planning, and the composite puncture blood collection experiment verified the feasibility of the RFCP strategy.

Microwave catalytic pyrolysis of heavy oil: A lump kinetic study approach

Managing heavy oil (HO) has become a vital issue in terms of environmental considerations. Microwave pyrolysis of HO is one of the efficient methods of upgrading and recycling this complex material. In this work, microwave catalytic pyrolysis (MCP) of HO was explored by investigating the effect of heating time (2–10 minutes) and temperature (460–730ºC) on the product yield and quality. The most critical point of this study was utilizing a unique design of SiC foam@HZSM-5 as a microwave absorber and catalyst simultaneously. This special design could eliminate the main problem of previous studies: the unwanted temperature gradient inside the reactor, which was misleading the prediction of kinetic parameters by recording the wrong temperature. The yield of each product’s category from the MCP process was reconstructed through a biharmonic spline interpolation (BSI) algorithm to visualize the simultaneous effect of the heating time and temperature on the yield. The optimum temperature for the MCP process was about 600ºC and a heating time of 6 minutes, which yielded the most valuable hydrocarbons in liquid and gas products. The produced liquid oil and non-condensable gases were analyzed through GC-MS and the collected results were categorized based on the properties of the detected compounds. An eight-lump kinetic model was then proposed based on that categorization. The calculated pre-exponential factors (k0) were higher than the expected values, while the apparent activation energies (Ea) were considerably lower than the reported data in the literature, which was due to the non-thermal and thermal effects of microwave irradiation on the reaction mechanism. The validation of the proposed model indicated the capability of this model to predict reliable data under these conditions.

Research on NURBS Curve Interpolation Algorithm Based on Mline-Gear

Aiming at the problem of speed fluctuation caused by the large error between the ideal interpolation step and the actual interpolation step in the traditional non-uniform rational B-spline ( NURBS ) curve interpolation, a NURBS curve interpolation algorithm based on Mline-Gear is proposed. The differential and derivative definitions are derived to transform the Mline and Gear formulas respectively, and the prediction and correction formulas of the interpolation parameters are obtained. Then, it is judged whether the interpolation parameter values obtained by the correction formula meet the speed fluctuation rate requirements, and the interpolation can be continued. Otherwise, the Steffensen descent formula is used to iteratively correct the interpolation parameter values. In order to ensure the calculation accuracy and avoid solving high-order derivatives, the fourth-order Runge-Kutta method is used to solve the initial two interpolation parameters. Finally, the algorithm is simulated. The simulation results show that the algorithm reduces the feed speed fluctuation rate while ensuring a small number of iterations.

Dynamic map‐making technology and the application in geographic information science

AbstractThe dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)‐related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the “frame” model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS‐related courses teaching and evaluates the teaching effect of dynamic maps.

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].

To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.

An overset interpolation algorithm for multi-phase flows using 3D multiblock polyhedral meshes

In this study, we present a novel interpolation scheme for chimera simulations in CFD that treats flows with discontinuities. This scheme is suitable for interpolation over polyhedral meshes using only scattered data and without the use of stencils. During the interpolation data transfer among overset meshes, both solution variables and their gradients are communicated and then used for the interpolation process. First, the overset topology problem in partitioned polyhedral meshes is addressed, and then a new interpolation algorithm for generally discontinuous fields is introduced. Then, we describe how the gradient approximation of the Finite Volume (FV) method is utilized in order to construct bounded approximations on values on the direction of discontinuities and enhance the accuracy of the low order Nearest Neighbor value (NNV) algorithm. The performance of the proposed algorithm is quantified and validated in various test cases, together with a comparison with NNV. Finally, scalability tests are presented to prove computational efficiency. The method is proved to be highly accurate in propagation cases and performs well in unsteady two-phase problems executed using parallel architectures.

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ radiation fields

Accurate reconstruction of radiation field and path planning are very important for the safety of operators in the process of dismantling nuclear facilities. Based on radial basis function (RBF) interpolation algorithm, this paper discussed the application of inverse multiquadric radial basis Function (IMRBF) interpolation method to the reconstruction of gamma radiation field, and proved the feasibility of reconstructing a radiation field with multiple γ sources. The average relative errors of IMRBF interpolation results were 4.28% and 8.76%, respectively, for the experimental scenarios with single and double gamma sources. After comparing the consistency between the simulated scene and the experimental scene, IMRBF method and Cubic Spline method were respectively used to reconstruct the gamma radiation field by Geant4 simulation data. The results showed that the interpolation accuracy of IMRBF method was superior to that of Cubic Spline method. Further, more RBF interpolation algorithms were used to reconstruct the multi-γ source radiation field, and then the Probabilistic Roadmap (PRM) algorithm was used to optimize the human walking path in the radiation field reconstructed by different interpolation methods. The optimal paths in radiation fields generated by multiple interpolation methods were compared. The results herein contribute to a comprehensive understanding of RBF interpolation methods in reconstructing γ radiation fields and their application in optimizing paths in radiation environments. The insights may provide valuable information for decision-making in radiation protection during the decommissioning of nuclear facilities.