Extra Computation Time Research Articles

Volume and Surface Area of 3D Mesh Objects : A Matlab GUI

In many applications of science and engineering, the real physical related objects are usually modelled in terms of mesh objects, where, the mesh will be used during computation or simulation. For most cases, a good mesh is required in order to increase the accuracy of the results. As the mesh represents the original object, the mesh should have similar physical properties of the original object such as the surface area or the volume. However, finding the value of these parameters could require extra computational cost and increase overall time taken to solve the problem. Thus, it might be useful if these parameters can be determined separately to ensure good mesh is used before any simulation is performed. In this study, a Matlab GUI is developed to enable user for easily computing the volume and the surface area of three-dimensional mesh objects. In order to use the GUI, user will firstly need to import the Mesh that is generated by the software Netgen Mesh generator. Once any mesh is imported as an input to the GUI, the program will use the concepts in vectors, which includes dot and cross products, to compute both parameters. Apart from presenting briefly the steps in creating the GUI with Matlab, this paper will also provide some examples to demonstrate the use of the GUI.

An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .

Lead Green's functions from quadratic eigenvalue problems without mode velocity calculations.

In quantum transport calculations, the proper handling of incoming and outgoing modes for retarded Green's functions is achieved via the lead self-energies. Computationally efficient and accurate methods to calculate the self-energies are thus very important. Here we present an alternative method for calculating lead self-energies which improves on a standard approach to solving quadratic eigenvalue problems that arise in quantum transport modeling. The method is based on a perturbative analysis of the generalized Schur decomposition to determine the relevant set of eigenvalues for transmitting modes. This allows us to circumvent finding the velocities of the modes (left- or right-moving) that are needed in order to calculate the lead Green's function from translationally invariant Green's functions. This saves computational time irrespective of the value of the imaginary part added to the energy. We compare our method with two existing methods-a popular iterative method and a standard eigenvalue method that explicitly calculates the velocities of the propagating modes. Our comparison shows that both eigenvalue methods are more robust than the iterative method. Furthermore, the comparison also shows that above a small threshold of propagating modes, the standard eigenvalue method requires extra computation time over our perturbation method. This excess of computation time grows linearly with the number of propagating modes.

An efficient many objective optimization algorithm with few parameters

During the past two decades, numerous many-objective optimization evolutionary algorithms (MaOEAs) have been proposed to tackle the challenges traditional multi-objective evolutionary algorithms face, that is to deal with abundant non-dominated solutions and low selection pressure. Specifically, a series of sophisticated selection strategies have been adopted, some of which need additional pre-defined parameters or a significant amount of extra computation time, which retards their applications in real life. In this paper, we propose an efficient indicator-based MaOEA with few parameters, SDE+-MOEA, that uses an adaptive combination of commonly used selection methods to improve search efficiency based on SDE+. SDE+ addresses situations where SDE cannot distinguish individuals with the same SDE values. The adaptive selection method dynamically selects between one-time and iterative selection methods at different stages of evolution to improve the search efficiency. Furthermore, apart from the four parameters for generating offspring, our proposed SDE+-MOEA does not introduce additional parameters. We conduct experimental studies to compare SDE+-MOEA with 11 state-of-the-art algorithms, including 2REA, ISDE+, θ-DEA, LMPFE, CVEA3, SRA, MOEA/DD, IBEA, Two_Arch2, NSGA-III, and SPEA2SDE using four representative performance indicators (HV, SP, PD and GD) on MaF benchmark with 5, 8, 10, and 15 objectives. Experimental studies demonstrate that, compared to the algorithms, SDE+-MOEA achieves better HV and competitive convergence and uniformity performance, while requiring few parameters. It means that SDE+-MOEA can find a solution set with better convergence and uniformity to help decision-makers understand the solved problems. Furthermore, SDE+-MOEA loses little spreadability since a better convergence often leads to a worse spread.

Squeeziness for non-deterministic systems

Failed Error Propagation greatly reduces the effectiveness of Software Testing by masking faults present in the code. This situation happens when the System Under Test executes a faulty statement, the state of the system is affected by this fault, but the expected output is observed. Therefore, it is a must to assess its impact in the testing process. Squeeziness has been shown to be a useful measure to assess the likelihood of fault masking in deterministic systems. The main goal of this paper is to define a new Squeeziness notion that can be used in a scenario where we may have non-deterministic behaviours. The new notion should be a conservative extension of the previous one. In addition, it would be necessary to evaluate whether the new notion appropriately estimates the likelihood that a component of a system introduces Failed Error Propagation. We defined our black-box scenario where non-deterministic behaviours might appear. Next, we presented a new Squeeziness notion that can be used in this scenario. Finally, we carried out different experiments to evaluate the usefulness of our proposal as an appropriate estimation of the likelihood of Failed Error Propagation. We found a high correlation between our new Squeeziness notion and the likelihood of Failed Error Propagation in non-deterministic systems. We also found that the extra computation time with respect to the deterministic version of Squeeziness was negligible. Our new Squeeziness notion is a good measure to estimate the likelihood of Failed Error Propagation being introduced by a component of a system (potentially) showing non-deterministic behaviours. Since it is a conservative extension of the original notion and the extra computation time needed to compute it, with respect to the time needed to compute the former notion, is very small, we conclude that the new notion can be safely used to assess the likelihood of fault masking in deterministic systems.

Compromise policy for multi-stage stochastic linear programming: Variance and bias reduction

This paper focuses on algorithms for multi-stage stochastic linear programming (MSLP). We propose an ensemble method named the “compromise policy”, which not only reduces the variance of the function approximation but also reduces the bias of the estimated optimal value. It provides a tight lower bound estimate with a confidence interval. By exploiting parallel computing, the compromise policy provides demonstrable advantages in performance and stability with marginally extra computational time. We further propose a meta-algorithm to solve the MSLP problems based on in-sample and out-of-sample optimality tests. Our meta-algorithm is incorporated within an SDDP-type algorithm for MSLP and significantly improves the reliability of the decisions suggested by SDDP. These advantages are demonstrated via extensive computations, which illustrate the effectiveness of our approach.

Thermochemical evaluation of adaptive and fixed density functional theory quadrature schemes.

A systematic study is made of the accuracy and efficiency of a number of existing quadrature schemes for molecular Kohn-Sham Density-Functional Theory (DFT) using 408 molecules and 254 chemical reactions. Included are the fixed SG-x (x = 0-3) grids of Gill et al., Dasgupta, and Herbert, the 3-zone grids of Treutler and Ahlrichs, a fixed five-zone grid implemented in Molpro, and a new adaptive grid scheme. While all methods provide a systematic reduction of errors upon extension of the grid sizes, significant differences are observed in the accuracies for similar grid sizes with various approaches. For the tests in this work, the SG-x fixed grids are less suitable to achieve high accuracies in the DFT integration, while our new adaptive grid performed best among the schemes studied in this work. The extra computational time to generate the adaptive grid scales linearly with molecular size and is negligible compared with the time needed for the self-consistent field iterations for large molecules. A comparison of the grid accuracies using various density functionals shows that meta-GGA functionals need larger integration grids than GGA functionals to reach the same degree of accuracy, confirming previous investigations of the numerical stability of meta-GGA functionals. On the other hand, the grid integration errors are almost independent of the basis set, and the basis set errors are mostly much larger than the errors caused by the numerical integrations, even when using the smallest grids tested in this work.

An easy-to-implement highly efficient algorithm for nonlinear Kutta condition in boundary element method

An easy-to-implement highly efficient algorithm for the nonlinear Kutta condition in the boundary element method is proposed. The main innovation is to pre-calculate an inverse matrix and use it to replace a solving system of equations with vector–scalar multiplication and matrix–vector multiplication. This allows calculating the Jacobian matrix in each nonlinear Kutta condition iteration with little computational effort, which is important for fast and robust convergence. The open-water characteristics of four different propellers are calculated with the linear and nonlinear Kutta conditions. The simulations show that the nonlinear Kutta condition results in more accurate open-water characteristics and more physically reasonable surface pressure distributions. In addition, the nonlinear Kutta condition takes no more than 3 extra seconds for an open-water simulation, and this extra time does not increase much with the number of simulation steps. The method proposed in this work is expected to improve the computational speed of the boundary element method while maintaining the same accuracy, or improve the accuracy with little extra computational time.

An efficient time-dependent auxiliary variable approach for the three-phase conservative Allen–Cahn fluids

Based on a time-dependent auxiliary variable approach, we propose linear, totally decoupled, and energy dissipative methods for the three-phase conservative Allen–Cahn (CAC) fluid system. The three-phase CAC equation has been extensively applied in the simulation of multi-component fluid flows because of the following advantages: (i) Total mass is conserved, (ii) Topological change of the interface can be implicitly captured. Compared with the ternary Cahn–Hilliard (CH) model, the CAC-type model is simple to solve. When we solve the CAC model by using the classical scalar auxiliary variable (SAV) approach, extra computational time is needed because we must decouple the local and non-local variables. The variant of SAV approach considered in the present study not only leads to linear and energy stable schemes, but also achieves highly efficient computation. Linear and decoupled equations need to be updated at each time step. We adopt the linear multigrid algorithm to speed up the convergence. Extensive numerical experiments with and without fluid flows are conducted to validate the temporal accuracy, mass conservation, and energy law.

Unconditionally energy-stable time-marching methods for the multi-phase conservative Allen–Cahn fluid models based on a modified SAV approach

In this study, we add nonlocal Lagrange multipliers to the Allen–Cahn (AC) equation to establish the N-component conservative Allen–Cahn (CAC) system and the ternary conservative Allen–Cahn–Navier–Stokes (CACNS) system which conserve the mass for each phase. Then, we construct linear, totally decoupled, and energy dissipative methods for those multi-component phase-field. In the classical scalar auxiliary variable (SAV) approach, the phase variables and the scalar auxiliary variables are coupled, which need extra computational time to decouple. In this paper, we adopt a variant of SAV approach in which all variables are totally decoupled and can be calculated in a step-by-step manner. The linear multigrid algorithm is adopted to accelerate convergence. We further analytically prove the unique solvability, modified energy dissipation law, and mass conservation in time-discretized version. We demonstrate the performance of the proposed scheme via various two-dimensional (2D) and three-dimensional (3D) numerical simulations. Contrast tests between the N-component CAC model and the N-component Cahn–Hilliard (CH) model show that the N-component CAC model is efficient.

MEAN: Multi-Edge Adaptation Network for Salient Object Detection Refinement

Recent advances in salient object detection adopting deep convolutional neural networks have achieved state-of-the-art performance. Salient object detection is task in computer vision to detect interesting objects. Most of the Convolutional Neural Network (CNN)-based methods produce plausible saliency outputs, yet with extra computational time. However in practical, the low computation algorithm is demanded. One approach to overcome this limitation is to resize the input into a smaller size to reduce the heavy computation in the backbone network. However, this process degrades the performance, and fails to capture the exact details of the saliency boundaries due to the downsampling process. A robust refinement strategy is needed to improve the final result where the refinement computation should be lower than that of the original prediction network. Consequently, a novel approach is proposed in this study using the original image gradient as a guide to detect and refine the saliency result. This approach lowers the computational cost by eliminating the huge computation in the backbone network, enabling flexibility for users in choosing a desired size with a more accurate boundary. The proposed method bridges the benefits of smaller computation and a clear result on the boundary. Extensive experiments have demonstrated that the proposed method is able to maintain the stability of the salient detection performance given a smaller input size with a desired output size and improvise the overall salient object detection result.

Fourth-Order Compact Finite Difference Method Combined with Richardson Extrapolation for OneDimensional Heat Equation

In this paper, Fourth-Order Compact Finite Difference method combined with Richardson extrapolation to solve the one-dimensional heat equation has been presented. The method is found to be fourth-order convergent in space and second-order in time variable. The method is also unconditionally stable and consistent for solving a one-dimensional heat equation. When combined with the Richardson extrapolation, the desired level of numerical solutions can be obtained without the requirement of further mesh refinement which claims extra computational time and memory. To validate the applicability of the proposed method, two model examples are considered and solved for different values of spatial and temporal step lengths. The numerical solutions presented in tables and the simulations of the model problems show that the proposed method approximates the exact solution very well. In a nutshell, the proposed method is efficient and capable of solving the one-dimensional heat equation.

Local feature descriptor using discrete first and second fundamental forms

3D object recognition from point clouds is a fast-growing field of research in computer vision. 3D object recognition methods can be classified into two categories: global feature-based and local feature-based methods. The local feature-based methods are more popular than global ones. Because the global feature-based methods need a prior segmentation of the scene, they are not suitable for real-world scenes. Many previous local descriptor methods limit their performance by introducing a local reference frame or axis (LRF/A). Estimating the LRF/A for each keypoint leads to extra computational time and error. We use the fundamental theorem of surface theory to introduce a simple and efficient local feature descriptor based on the coefficients of discrete first and second fundamental forms. The proposed method overrides the necessity of an LRF/A, and it required a small feature dimension of seven, which means it is a low-complexity and fast procedure. To assess the proposed method, we have compared it with eight state-of-the-art descriptors and applied it to the three popular datasets to extract features and recognize the correspondences. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of pairwise registration measurements, recall versus 1-precision curve, and the computational time.

Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection.

Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.

Comparison study of URANS and hybrid RANS-LES models on predicting vertical axis wind turbine performance at low, medium and high tip speed ratio ranges

Comparison study of unsteady Reynolds-averaged Navier-Stokes (URANS) and hybrid RANS-LES models is carried out for predicting the performance of three-straight-bladed vertical axis wind turbine operating within tip speed ratios (TSRs) ranges of (1.44–3.3). The evaluation is focused on power coefficient, moment coefficient and vortex structure generation, growth and transportation predictions. It was found that URANS turbulence modelling is sufficient for averaged power coefficient prediction and specific range of TSR evaluation, and it also benefits from short simulation run time. To further evaluate flow field details and to understand the underlying flow physics such as dynamic stall behaviour, hybrid RANS-LES turbulence modelling is necessary. Comparing between hybrid models adopted, stress-blended eddy simulation based on transition shear-stress transport (TSST) model has achieved an overall better performance, such as a reduction of simulation discrepancy by 50% in low TSRs range compared to TSST turbulence model. In both medium and high TSRs ranges, the modelling discrepancies are less than 3% compared to TSST turbulence models at about 25% extra computational time. Furthermore, in high TSRs range, hybrid RANS-LES models are able to predict the appearance of vortex shedding at high azimuthal angles (θ ≥ 180°) for which URANS models failed to capture.

Flow-level and efficient traffic engineering in conventional routing systems

Existing solutions of flow-level traffic engineering (TE) usually depend on the deployment of SDN or MPLS. In this paper, we design a flow-level and efficient TE scheme based on the conventional hop-by-hop routing protocol, i.e., OSPF. Motivated by the analysis and modeling on the real Internet traffic, we propose to detect and schedule a few large flows in real-time. The rerouting paths for large flows are computed in a centralized server and are distributed through extended OSPF. A few ACL entries are used for flow-level forwarding. We formalize the link weight assignment-based large flow scheduling problem and prove the problem is NP-hard. We propose to precompute several candidate paths to reduce decision computation overhead and path stretch. We develop an algorithm with performance bounds to allocate large flows to paths, and two algorithms to reduce extra LSA number for different system designs. Experiment results show our scheme can reroute large flows within 0.5 s. Simulation results show our scheme gets congestion metric values (i.e., performance ratios) 10% worse than the optimal for source and destination addresses-based flows. Our optimization mechanisms reduce the extra LSA number and computation time by 87% and 83% respectively for our scheme with pre-computed paths.

Finite-difference and integral schemes for Maxwell viscous stress calculation in immersed boundary simulations of viscoelastic membranes.

The immersed boundary method (IBM) has been frequently utilized to simulate the motion and deformation of biological cells and capsules in various flow situations. Despite the convenience in dealing with flow-membrane interaction, direct implementation of membrane viscosity in IBM suffers severe numerical instability. It has been shown that adding an artificial elastic element in series to the viscous component in the membrane mechanics can efficiently improve the numerical stability in IBM membrane simulations. Recently Li and Zhang (Int J Numer Methods Biomed Eng 35:e3200, 2019) proposed a finite-difference method for calculating membrane viscous stress. In the present paper, two new schemes are developed based on the convolution integral expression of the Maxwell viscoelastic element. We then conduct several tests for the accuracy, stability, and efficiency performances of these three viscous stress schemes. By studying the behavior of a one-dimensional Maxwell element under sinusoidal deformation, we find that a good accuracy can be achieved by selecting an appropriate relaxation time. The twisting sphere tests confirm that, compared to the numerical errors induced by other components in capsule simulations, such as the finite element method for membrane discretization and IBM for flow-membrane interaction, the errors from the viscous stress calculation are negligible. Moreover, extensive simulations are conducted for the dynamic deformation of a spherical capsule in shear flow, using different numerical schemes and various combinations of the artificial spring constants and calculation frequency for the membrane viscous stress calculation. No difference is observed among the results from the three schemes; and these viscous stress schemes require very little extra computation time compared to other components in IBM simulations. The simulation results converge gradually with the increase in the artificial spring stiffness; however, a threshold value exists for the spring stiffness to maintain the numerical stability. The viscous stress calculation frequency has noapparent influence on the calculation results, but a large frequency number can cause the simulation to collapse. We therefore suggest to calculate the membrane viscous stress at each simulation time step, such that a better numerical stabilitycan be achieved. The three numerical schemes have nearly identical performances in all aspects, and they can all be utilized in future IBM simulations of viscoelastic membranes.

Neural network approximated Bayesian inference of edge electron density profiles at JET

A neural network (NN) has been trained on the inference of the edge electron density profiles from measurements of the JET lithium beam emission spectroscopy (Li-BES) diagnostic. The novelty of the approach resides in the fact that the network has been trained to be a fast surrogate model of an existing Bayesian model of the diagnostic implemented within the Minerva framework. Previous work showed the very first application of this method to an x-ray imaging diagnostic at the W7-X experiment, and it was argued that the method was general enough that it may be applied to different physics systems. Here, we try to show that the claim made there is valid. What makes the approach general and versatile is the common definition of different models within the same framework. The network is tested on data measured during several different pulses and the predictions compared to the results obtained with the full model Bayesian inference. The NN analysis only requires tens of microseconds on a GPU compared to the tens of minutes long full inference. Finally, in relation to what was presented in the previous work, we demonstrate an improvement in the method of calculation of the network uncertainties, achieved by using a state-of-the-art deep learning technique based on a variational inference interpretation of the network training. The advantage of this calculation resides in the fact that it relies on fewer assumptions, and no extra computation time is required besides the conventional network evaluation time. This allows estimating the uncertainties also in real time applications.

An Extrinsic Calibration Method With Closed-Form Solution for Underwater Opti-Acoustic Imaging System

Forward-looking sonar (FLS) and camera are widely used sensors in multimodal systems for underwater environments. The extrinsic calibration is a precondition for this kind of multimodal systems to fuse optical and acoustic information. Due to the very different imaging formation mechanisms and models of 3-D-to-2-D projection for imaging sonar and optical camera, the multimodal correspondence and the sonar-pose recovery from a 2-D sonar image are two main issues in extrinsic calibration work. In this article, we propose an extrinsic calibration method for the opti-acoustic imaging system combining a camera and an FLS in underwater environment. The proposed method includes an estimation process yielding closed-form expressions to determine the sonar pose with respect to the calibration board, which enables the estimation of extrinsic parameters to be directly computed from sensor measurements and the knowledge of calibration-board structure. A calibration board with two sets of feature arrays is used to generate two 2-D-to-3-D correspondences, which circumvents the 2-D-to-2-D multimodal correspondence problem. An iterative optimization process is provided to further improve the performance, while the extra computation time is negligible. Both simulated and actual experiments show that the method outperforms the existing iterative method based on bundle adjustment.

Improving eye-tracking calibration accuracy using symbolic regression.

Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations. These approaches allowed collecting more data compared to the standard 9-point calibration. However, the computation of the mapping function which provides planar gaze positions from pupil features given as input is mostly based on polynomial regressions, and little work has investigated alternative approaches. This paper fills this gap by providing a new calibration computation method based on symbolic regression. Instead of making prior assumptions on the polynomial transfer function between input and output records, symbolic regression seeks an optimal model among different types of functions and their combinations. This approach offers an interesting perspective in terms of flexibility and accuracy. Therefore, we designed two experiments in which we collected ground truth data to compare vestibulo-ocular and smooth pursuit calibrations based on symbolic regression, both using a marker or a finger as a target, resulting in four different calibrations. As a result, we improved calibration accuracy by more than 30%, with reasonable extra computation time.