Sample Points In Space Research Articles

Multi-target feature selection with subspace learning and manifold regularization

Existing supervised Multi-Target Feature Selection (MTFS) methods seldom consider the nearest-neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low-rank constraint is introduced to achieve multi-layer regression structure to better decouple the inter-input and inter-target relationships. Moreover, the local nearest-neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods.

Multi-objective structural optimization and degradation model of magnesium alloy ureteral stent

BackgroundMg alloys have attractive properties, including biocompatibility, biodegradability, and ideal mechanical properties. Moreover, Mg alloys are regarded as one of the promising candidates for manufacturing ureteral stents. This study proposed a multi-objective optimization method based on the Kriging surrogate model, NSGA-Ⅲ, and finite element analysis to improve the degradation performance of Mg alloy ureteral stents. MethodsThe finite element model for the degradation of Mg alloy ureteral stents has been established to compare the degradation performance of the stents under different parameters. Latin hypercube sampling was adopted to generate train sample points in the design space. Meanwhile, the Kriging surrogate model was constructed between strut parameters and stent degradation behavior. The NSGA-Ⅲ was utilized to determine the optimal solution in the global design space. ResultsThe optimized stent achieved 5.52 ​× ​degradation uniformity (M), 10 ​× ​degradation time (DT), and 4 ​× ​work time (FT). The errors between the Kriging surrogate model and the finite element calculation results were less than 6%. ConclusionThe optimized stent achieved better degradation performance. The degradation behavior of stents was dependent on the design parameters. The multi-objective optimization method based on the Kriging surrogate model and finite element analysis was effective in stent design optimization problems.

On the numerical approximation of Blaschke–Santaló diagrams using Centroidal Voronoi Tessellations

Blaschke–Santaló diagrams are images of maps defined on a set of parameters, taking values into an Euclidean space. Typically, the dimension of the source space is high, possibly infinite, while the target space is two or three dimensional. These diagrams help characterize geometrically various inequalities and are of particular interest in the field of shape optimization. We propose a numerical method, based on Centroidal Voronoi Tessellations, which produces sample points in the parameter space that have uniformly distributed images in the Blaschle–Santaló diagram, therefore providing an accurate description of the latter. Compared with the classical Monte Carlo methods, which simply use a large number of images corresponding to random parameters, the method proposed is computationally efficient and precise. Simulations for two and three dimensional diagrams are presented involving examples in algebra and shape optimization.

The m-dimensional spatial Nyquist limit using the wave telescope for larger numbers of spacecraft

Abstract. Spacecraft constellations consisting of multiple satellites are becoming more and more interesting not only for commercial use but also for space science missions. The proposed and accepted scientific multi-satellite missions that will operate within Earth's magnetospheric environment, like HelioSwarm, require researchers to extend established methods for the analysis of multi-spacecraft data to more than four spacecraft. The wave telescope is one of those methods. It is used to detect waves and characterize turbulence from multi-point magnetic field data, by providing spectra in reciprocal position space. The wave telescope can be applied to an arbitrary number of spacecraft already. However, the exact limits of the detection for such cases are not known if the spacecraft, acting as sampling points, are irregularly spaced. We extend the wave telescope technique to an arbitrary number of spatial dimensions and show how the characteristic upper detection limit in k space imposed by aliasing, the spatial Nyquist limit, behaves for irregularly spaced sampling points. This is done by analyzing wave telescope k-space spectra obtained from synthetic plane wave data in 1D up to 3D. As known from discrete Fourier transform methods, the spatial Nyquist limit can be expressed as the greatest common divisor in 1D. We extend this to arbitrary numbers of spatial dimensions and spacecraft. We show that the spatial Nyquist limit can be found by determining the shortest possible basis of the spacecraft distance vectors. This may be done using linear combination in position space and transforming the obtained shortest basis to k space. Alternatively, the shortest basis can be determined mathematically by applying the modified Lenstra–Lenstra–Lovász (MLLL) algorithm combined with a lattice enumeration algorithm. Thus, we give a generalized solution to the determination of the spatial Nyquist limit for arbitrary numbers of spacecraft and dimensions without any need of a priori knowledge of the measured data. Additionally, we give first insights into the application to real-world data incorporating spacecraft position errors and minimizing k-space aliasing. As the wave telescope is an estimator for a multi-dimensional power spectrum substituting spatial Fourier transform, the results of this analysis can be applied to power spectral density estimation via Fourier transform or other methods making use of irregular sampling points. Therefore, our findings are also of interest to other fields of signal processing.

Application of high-credible statistical results calculation scheme based on least squares Quasi-Monte Carlo method in multimodal stochastic problems

Solving stochastic problems based on multimodal distributions with high accuracy and efficiency is the focus of current research in the stochastic field. However, since exact statistical results in practical engineering problems are unknown, how to efficiently obtain statistical results with high credibility is of great importance. Therefore, a high-credible statistical results calculation scheme is proposed in this paper. First, the multimodal model is transformed into a weighted sum form of multiple unimodal models by Gaussian mixture model. Then, a high-credible statistical results calculation scheme is designed based on the reusability of low-discrepancy sequences in the quasi-Monte Carlo (QMC) method. The scheme calculates the coefficient of variation for the random response statistical results of the unimodal model across various sample ranges. When the coefficient of variation is less than the tolerance error, which indicates low fluctuation, the statistical results are deemed to have converged to the exact solution. In order to further accelerate the calculation efficiency of the proposed scheme, this paper proposes a least squares quasi-Monte Carlo (LSQMC) method to yield high accuracy statistical moments. Compared with the averageness weights in the QMC method, the non-averageness weights obtained by LSQMC method can more effectively reflect the numerical Characteristics of the random variables and the location distribution of the sampling points in the probability space. Finally, numerical examples have demonstrated that the high-credible statistical results calculation scheme based on the LSQMC method can obtain the high accuracy statistical moments with high efficiency in multimodal stochastic problems.

Simultaneous seismic inversion study for channel sandstone identification, northern part of the Eastern Shelf, King County, North-Central Texas

This paper documents the results of a prestack simultaneous seismic inversion conducted to investigate the Wolfcampian lower Tannehill sandstone channels within a mixed carbonate siliciclastic system in the northern Eastern Shelf in King County, North Central Texas. Previous exploration methods for the Tannehill sandstone were based largely on well-log correlation which, because of their widely spaced sample points, are not optimal for channel delineation. We have generated crossplots of P- and S-wave velocity ratios ([Formula: see text]/[Formula: see text] ratios) versus P-impedance to determine lithologic types. We then used 3D seismic data and wireline logs to implement a prestack simultaneous seismic inversion procedure to generate P- and S-impedances, density, and [Formula: see text]/[Formula: see text] ratio volumes to identify the valley-fill Tannehill lowstand systems. Our results find that (1) based on impedance and density, the sandstone-rich channels can be easily separated from the carbonate-rich zones but are difficult to distinguish using [Formula: see text]/[Formula: see text]. (2) Although there appeared to be several channel systems in the area of investigation, two of these, hereby referred to as channel A and channel B, are recognized to be the key contributors of sandstone sediments to the shelf edge, slope, and basin areas. (3) Of these two channels, channel A is the larger and highly sinuous; it is associated with a meander loop, a point bar, and an abandoned channel. At the point-bar location, channel A has a width of 890 m (2920 ft) and a depth of 29 m (94 ft). The point bar is structurally anticlinal and thus constitutes a potential exploration target. In contrast, channel B is less sinuous, without any associated point bar. It has a maximum depth and width of 23 m (75 ft) and 270 m (886 ft), respectively. Finally, the inversion results suggest that other potential Tannehill sandstone reservoirs may still be present elsewhere within King County.

Deep Learning-Based DAS to Geophone Data Transformation

Seismic data are the primary way to study the subsurface structure and properties. A conventional seismic sensor like geophone or accelerometer measures particle velocity or acceleration at a local point only, while distributed acoustic sensing (DAS) measures dynamic strain along the fiber optic cable at densely spaced sample points, where strain rate is obtained over certain gauge length interval. Therefore, DAS measures subsurface properties with high sampling resolution and large coverage. When an optical fiber is installed in a well, DAS can provide continuous, dense downhole recording. However, currently, most of the seismic processing, imaging, and inversion techniques are developed for geophone data. These well-established techniques can be readily and properly utilized if DAS data are transformed into geophone measurements, such as particle velocity. In this study, we present a recurrent neural network (RNN) framework to perform this transformation. This effectiveness of the deep learning-based mapping is then demonstrated with a field measurement data, showing that DAS data can be transformed into particle velocity accurately and robustly using the proposed deep-learning approach.

Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature

Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1-8 M) and temperature (10-40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO3 and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO3 concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO3 concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.

Design of remote sensing image feature extraction method based on high score 2 wavelet transform

The feature extraction of Gaofen-2 Remote Sensing Image (RSI) has problems such as poor extraction accuracy and large noise reduction error. Therefore, this paper designs an RSI feature extraction method based on high score 2 wavelet transform (WT). The RSI is collected with the help of Gaofen-2 satellite and high-resolution remote sensing technology, the key points of the image are determined through the Gaussian difference scale space, and the key points of the edge are judged by the peak curvature value of the difference function at the edge junction, so as to complete the RSI acquisition. Specific filtering and spatial domain transformations are used to remove image noise and improve the quality of RSI. The mean shift (MS) algorithm is used to iteratively find the area with the most dense sample points in the RSI space, complete the image analysis, and realize the preprocessing of the high score 2 RSI. The linear features of the RSI are determined by the WT algorithm, and the image threshold is set for feature extraction of the high score 2 RSI. The experimental results show that in the RSI noise reduction error analysis of different methods, the noise reduction error curve of the sample RSI of the method proposed in this paper has the lowest trend, which is always lower than 2%. Compared with the two methods proposed before, the error is higher. At the same time, in the accuracy analysis of key point feature extraction, the proposed scheme has better accuracy. Therefore, it can be seen that this method has better comprehensive performance, and the proposed method can effectively improve the feature extraction accuracy of RSI and reduce the noise in RSI.

A novel ensemble convex hull-based classification model for bevel gearbox fault diagnosis

The kernel-based geometric learning model has been successfully applied in bevel gearbox fault diagnosis. However, due to its shallow architecture and problems with its sensitivity to noise and outliers, its generalization ability and robustness need to be further improved. Ensemble learning can improve the classification accuracy of sub-classifiers, but it is effective only when the sub-classifiers meet the requirements of difference and accuracy at the same time. However, as strong classifiers, geometric learning models are difficult to produce sub-classifiers with differences. To solve these problems, this study proposes a novel ensemble model, the ensemble convex hull (CH)-based (EnCH) classification model. CH has the advantages of clear geometric meaning and is easy to deform. This paper considers the clustering characteristics of the sample points in the feature space, or both distance and density, and performs differential shrinkage deformation on the original CH. For one thing, this can produce differential CHs to build differential sub-classifiers for the ensemble. Also, it can suppress the interference of noise and outliers to improve robustness. The results of our experiments on the fault dataset of a bevel gear box indicate that the EnCH classification model can improve the generalization of the geometric learning model and has excellent tolerance to noise and outliers.

An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow Channels.

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

Research on Personalized Recommendation Method of Educational Resources Based on Learner Behavior Analysis

The explosion of online learning resources makes the research on personalized recommendation of educational resources increasingly prominent. Based on the theory of learner behavior analysis, this paper analyzes the learning behavior logs of the online learning platform, and constructs a personalized recommendation method for educational resources. It analyzes the learner behavior from the three dimensions of basic attributes, behavior characteristics and result characteristics, and solves the problem of resource analysis. In the simulation process, the method uses the vector space model to complete the modeling of learner behavior, and realizes the division of learner groups based on learner behavior, and further evaluates and optimizes the division of learner groups. In order to further verify the clustering effect in combination with the actual data, the classification results are used for example analysis: the experimental results show that the sample points in the educational resource space are divided into 23 categories according to the distance relationship, and [Formula: see text] is the best. The silhouette coefficient value is 0.54436, and the Calinski–Harabasz score value is 1464.9, which effectively improves the clustering effect of the personalized recommendation method.

Inverse Gaussian Process Modeling for Evolutionary Dynamic Multiobjective Optimization.

For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs.

Mean Arc Distance (MAD): a quantity to compare trajectory 4π sampling in single target cranial stereotactic radiotherapy

Purpose. C-arm linac-based radiotherapy has seen a recent interest in 4π methods of delivery using simultaneous rotations of couch and gantry to reduce doses to organs-at-risk (OARs) and increase dose compactness. While many methods use heuristics to generate trajectories that avoid OARs, combined with arbitrary trajectory restrictions to prevent oversampling, a quantity has not yet been developed to succinctly compare sampling of the 4π space for candidate trajectories as a surrogate for dosimetric compactness. Methods. Evenly spaced sampling points were distributed across a 4π sphere centred on isocentre. A metric, mean arc distance (MAD), was defined that quantifies the average arc distance between all sampling points and their nearest field in a radiotherapy trajectory. The relationship between isodose volume and MAD was examined in 2,047 plans: 900 unique trajectories of fixed port DCA plans, 900 unique trajectories of contiguous field DCA plans, 192 VMAT plans (eight volumes in four locations, each with six trajectories) in matRad with 5 VMAT plans repeated for validation in a clinical planning system, and 10 clinical VMAT cases replanned with five trajectories in a clinical treatment planning system. Results. All isodose volumes greater than 10% of the prescription dose decreased with decreasing MAD in all comparisons. In the range of 10% to 100% of the prescription dose, the rate of isodose volume decrease was exponential as a function of MAD in all comparisons. Reduction of absolute isodose volume is seen with increased 4π sampling, with larger target volumes exhibiting larger absolute reductions. Very low isodoses (0% to 10% of prescription) increased with decreasing MAD. Conclusions. MAD is a 4π sampling quantity useful in quantifying the decrease of isodose volume, relevant for sparing normal tissues. By quantifying this feature, candidate dynamic trajectories can be efficiently compared for 4π sampling. This quantity is explored here for single target cranial radiotherapy but may have applications to other radiotherapy treatment sites.

The effect of structured medication review followed by face-to-face feedback to prescribers on adverse drug events recognition and prevention in older inpatients – a multicenter interrupted time series study

BackgroundThe effectiveness of interventions to improve medication safety in older inpatients is unclear, given a paucity of properly designed intervention studies applying clinically relevant endpoints such as hospital-acquired preventable Adverse Drug Events (pADEs) and unrecognized Adverse Drug Events (uADEs). Therefore, we conducted a quality improvement study and used hospital-acquired pADEs and uADEs as main outcomes to assess the effect of an intervention aimed to improve medication safety in older inpatients.MethodThe study followed an interrupted time series design and consisted of three equally spaced sampling points during baseline and during intervention measurements. Each sampling point included between 80 to 90 patients. A total of 500 inpatients ≥65 years and admitted to internal medicine wards of three Dutch hospitals were included. An expert team retrospectively identified and assessed ADEs via a structured patient chart review. The findings from baseline measurement and meetings with the internal medicine and hospital pharmacy staff were used to design the intervention. The intervention consisted of a structured medication review by hospital pharmacists, followed by face-to-face feedback to prescribers, on average 3 days per week.ResultsThe rate of hospital-acquired pADEs per 100 hospitalizations was reduced by 50.6% (difference 16.8, 95% confidence interval (CI): 9.0 to 24.6, P < 0.001), serious hospital-acquired pADEs by 62.7% (difference 12.8, 95% CI: 6.4 to 19.2, P < 0.001), and uADEs by 51.8% (difference 11.2, 95% CI: 4.4 to 18.0, P < 0.001). Additional analyses confirmed the robustness of the intervention effect, but residual bias cannot be excluded.ConclusionsThe intervention significantly decreased the overall and serious hospital-acquired pADE occurrence in older inpatients, and significantly improved overall ADE recognition by prescribers.Trial registrationInternational Standard Randomized Controlled Trial Number Register, trial registration number: ISRCTN64974377, registration date (date assigned): 07/02/2011.

A POD-Galerkin reduced order model for the Navier–Stokes equations in stream function-vorticity formulation

We develop a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for the efficient numerical simulation of the parametric Navier–Stokes equations in the stream function-vorticity formulation. Unlike previous works, we choose different reduced coefficients for the vorticity and stream function fields. In addition, for parametric studies we use a global POD basis space obtained from a database of time dependent full order snapshots related to sample points in the parameter space. We test the performance of our ROM strategy with the well-known vortex merger benchmark and a more complex case study featuring the geometry of the North Atlantic Ocean. Accuracy and efficiency are assessed for both time reconstruction and physical parameterization.

Multi-Objective Optimization of Bioresorbable Magnesium Alloy Stent by Kriging Surrogate Model.

The study proposed a multi-objective optimization method based on Kriging surrogate model and finite element analysis to mitigate the redial recoil and foreshortening ratio of bioresorbable magnesium alloy stent, and investigate the impact of strut thickness on stent expansion behavior. Finite element analysis have been carried out to compare the expansion behavior of stents with various strut thickness. Latin hypercube sampling (LHS) was adopted to generate train sample points in the design space, and the Kriging surrogate model was constructed between strut parameters and stent behavior. The genetic algorithm (GA) was employed to find the optimal solution in the global design space. Stents with thinner struts experience lower stress but suffer from severe radial recoil and foreshortening effects. The radial recoil is decreased by 66%, and foreshortening ratio is reduced by 60% for the optimized stent with U-bend width 90.7 [Formula: see text] and link width 77.9 [Formula: see text]. The errors between Kriging surrogate model and finite element simulation are 6% and 9% for the radial recoil and foreshortening ratio. Stent expansion behavior are highly dependent on design parameters, i.e. thickness, U-bend and link strut width. The purposed Multi-objective optimization approach based on Kriging surrogate model and finite element analysis is efficient in stent design optimization problem.

Research on disruptive design pre-identification in the preliminary design phase of aero engine flow pass multidisciplinary optimization

In aero engine design, determining whether the preliminary design will have disruptive effects on the detailed design is the key to multidisciplinary design optimization in the preliminary design stage. In order to adapt to the non-orthogonal parameter value range caused by the self-constrained parametric modeling method, a non-orthogonal space mapping method that maps the optimal Latin hypercube sampling points of the traditional orthogonal design space to the non-orthogonal design space is proposed. Based on the logical regression method in machine learning field, a kind of feasible domain boundary identification method is employed to identify whether the sample spatial response meets the relevant criteria. The method proposed in this paper is used to identify and analyze the key technologies of the high-pressure turbine mortise joint structure. It is found that the preliminary design of the aero engine may lead to the failure to obtain a mortise joint structure meeting the design requirements in the detailed design stage. The mortise joint structure needs to be pre-optimized in the preliminary design stage.

A Motion Planning Method for Automated Vehicles in Dynamic Traffic Scenarios

We propose a motion planning method for automated vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic information and making a preliminary driving decision. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing three dimensions (lateral distance, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Simulated Annealing Algorithm (SAA) to improve the process of searching for the optimal trajectories. Experiments in three different simulated driving scenarios (speed maintaining, lane changing, and car following) show that the proposed method can efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete space, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval.

Space partitioning and regression maxima seeking via a mean-shift-inspired algorithm

The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the maxima of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the rates of convergence of the estimated local maxima for the underlying regression model. We also demonstrate the utility of the algorithm for data-enabled discovery through an application on biomolecular structure data.