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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 150)
Statistics in Medicine     Hybrid Journal   (Followers: 139)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 50)
Sociological Methods & Research     Hybrid Journal   (Followers: 43)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Computational Statistics     Hybrid Journal   (Followers: 15)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Statistical Papers     Hybrid Journal   (Followers: 4)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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Engineering With Computers
Journal Prestige (SJR): 0.485
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667
Published by Springer-Verlag Homepage  [2469 journals]
  • Accurate quantification of blood flow wall shear stress using
           simulation-based imaging: a synthetic, comparative study

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      Abstract: Abstract Simulation-based imaging (SBI) is a blood flow imaging technique that optimally fits a computational fluid dynamics (CFD) simulation to low-resolution, noisy magnetic resonance (MR) flow data to produce a high-resolution velocity field. In this work, we study the effectivity of SBI in predicting wall shear stress (WSS) relative to standard magnetic resonance imaging (MRI) postprocessing techniques using two synthetic numerical experiments: steady flow through an idealized, two-dimensional stenotic vessel and a model of an adult aorta. In particular, we study the sensitivity of these two approaches with respect to the Reynolds number of the underlying flow, the resolution of the MRI data, and the noise in the MRI data. We found that the SBI WSS reconstruction: (1) is insensitive to Reynolds number over the range considered ( \(\mathrm {Re} \le 1000\) ), (2) improves as the amount of MRI data increases and provides accurate reconstructions with as little as three MRI voxels per diameter, and (3) degrades linearly as the noise in the data increases with a slope determined by the resolution of the MRI data. We also consider the sensitivity of SBI to the resolution of the CFD mesh and found there is flexibility in the mesh used for SBI, although the WSS reconstruction becomes more sensitive to other parameters, particularly the resolution of the MRI data, for coarser meshes. This indicates a fundamental trade-off between scan time (i.e., MRI data quality and resolution) and reconstruction time using SBI, which is inherently different than the trade-off between scan time and reconstruction quality observed in standard MRI postprocessing techniques.
      PubDate: 2022-08-10
       
  • An enrichment technique for bending analysis of in-plane heterogeneous
           thin plates with weak singularities

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      Abstract: Abstract Static solution of thin elastic plate problems with in-plane varying thickness or material properties having weak point singularities (e.g. crack tip or notches) is studied using a novel enrichment technique. Since the smooth basis functions are not capable of adapting to the adjacency of the singular edge point, enrichment bases called Equilibrated Singular Basis Functions (EqSBFs) are added to improve the solution quality. A combination of Chebyshev polynomials of the first kind and trigonometric functions are used as basis functions. The equilibrium equation is enforced by a weighted residual approach over a fictitious domain which contains the main problem domain. The total integration process is replaced by a composition of normalized pre-evaluated integrals, thus speeding up the procedure considerably. The novelty of the paper is that the proposed method can automatically identify and reproduce the enriching terms corresponding to the singularity order of the problem, which is an advantage with respect to the similar methods that need a priori knowledge of the analytical singularity order. Although the proposed technique is developed in the context of boundary methods, it may also be useful in other enriched methods such as XFEM.
      PubDate: 2022-08-08
       
  • A new well-balanced spectral volume method for solving shallow water
           equations over variable bed topography with wetting and drying

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      Abstract: Abstract The shallow-water equations are a hyperbolic conservation law system with source terms, which can be used in various engineering applications. Designing a high-order numerical method to preserve exactly steady-state solutions is a challenging task. Another difficulty is the appearance of dry regions in the computational domain, where no water or very little water is present. Special attention needs to be paid; otherwise, numerical methods may fail in these regions creating unphysical negative water depths. In this paper, a new high-order well-balanced Chebyshev spectral volume with a new hydrostatic reconstruction (HR) scheme is presented to preserve the steady-state solutions, and at the same time, deal with wetting and drying without loss of mass conservation. In addition, the shallow water equations may have some discontinuous solutions, even for smooth initial conditions. We modify the C-WENO limiter to reconstruct the numerical approximation on target cells that have numerical oscillations. One of the significant advantages of the modified C-WENO limiter compared to other limiters is that it only depends on the numerical approximation of the target cell and immediate neighbors. With the modified C-WENO limiter, we can achieve a high order of accuracy and non-oscillatory properties and maintain the proposed method’s well-balanced and positivity-preserving properties. To restrict the time step to the Courant–Friedrichs–Lewy condition and ensure stability and accurate results, we introduce a semi-implicit discretization of the friction source term, which does not need an iteration method. Various numerical tests are presented to evaluate the proposed method’s performance in terms of high-order accuracy, well-balanced, positivity-preserving, non-oscillatory, and mass conservation properties.
      PubDate: 2022-08-06
       
  • An efficient hierarchical fuzzy simulation method for estimating failure
           possibility

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      Abstract: Abstract The failure possibility (FP) can reasonably measure the safety degree of the structure under fuzzy uncertainty, and the estimation of FP can be transformed into searching the point with the maximum joint membership function (MF) of fuzzy input vector in the failure domain (also known as fuzzy design point). In the current fuzzy simulation (FS) method, the fuzzy design point is searched in the maximum value region of the fuzzy input vector corresponding to the lowest membership level which is equal to 0 and the computational efficiency is low. In this paper, an efficient hierarchical fuzzy simulation (HFS) method is proposed for estimating FP. In the proposed method, by the nature that the fuzzy design point is a failure point with the maximum joint MF, the fuzzy design point is first searched in the smaller value region of input vector corresponding to the larger membership level, and the membership level is automatically reduced layer by layer to expand the search region until the failure points appear. Compared with the traditional FS method, the proposed HFS method not only guarantees the search accuracy of the fuzzy design point, but also reduces the total search region; thus the computational efficiency is improved. In addition, an adaptive Kriging model is also embedded in the search process of HFS. Since the adaptively updated Kriging model is used to replace the real performance function for recognizing the state of the simulated sample points during the search process, the strategy of combining the Kriging model with HFS method can further improve the search efficiency of the fuzzy design point. The results of examples show that the proposed HFS method is reasonable and efficient.
      PubDate: 2022-08-03
       
  • An isogeometric analysis-based topology optimization framework for 2D
           cross-flow heat exchangers with manufacturability constraints

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      Abstract: Abstract Heat exchangers (HXs) have gained increasing attention due to the intensive demand of performance improving and energy saving for various equipment and machines. As a natural application, topology optimization has been involved in the structural design of HXs aiming at improving heat exchange performance (HXP) and meanwhile controlling pressure drop (PD). In this paper, a novel multiphysics-based topology optimization framework is developed to maximize the HXP for 2D cross-flow HXs, and concurrently limit the PD between the fluid inlet and outlet. In particular, an isogeometric analysis solver is developed to solve the coupled steady-state Navier–Stokes and heat convection–diffusion equations. Non-body-fitted control mesh is adopted instead of dynamically remeshing the design domain during the evolution of the boundary interface. The method of moving morphable voids is employed to represent and track boundary interface between the hot and the remaining regions. In addition, various constraints are incorporated to guarantee manufacturability of the optimized structures with respect to practical considerations in additive manufacturing, such as removing sharp corners, controlling channel perimeters, and minimizing overhangs. To implement the iterative optimization process, the method of moving asymptotes is employed. Numerical examples show that the HXP of the optimized structure is greatly improved compared with its corresponding initial design, and the PD between the fluid inlet and outlet is controlled concurrently. Moreover, a smooth boundary interface between the channel and the cold fluid, and improved manufacturability are simultaneously obtained for the optimized structures.
      PubDate: 2022-08-03
       
  • Probabilistic medical image imputation via deep adversarial learning

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      Abstract: Abstract The ability to impute missing images from a sequence of medical images plays an important role in enabling the detection, diagnosis and treatment of disease. Motivated by this, in this manuscript we propose a novel, probabilistic deep-learning algorithm for imputing images. Within this approach, given a sequence of contrast enhanced CT images, we train a generative adversarial network (GAN) to learn the underlying probabilistic relation between these images. Thereafter, given all but one member from a sequence, we infer the probability distribution of the missing image using Bayesian inference. We make the inference problem computationally tractable by mapping it to the low-dimensional latent space of the GAN. Thereafter, we use Markov Chain Monte Carlo (MCMC) techniques to learn and sample the inferred distribution. Moreover, we propose a novel style loss unique to contrast-enhanced computed tomography (CECT) imaging to improve the texture of the generated images, and apply these techniques to infer missing CECT images of renal masses collected during an IRB-approved retrospective study. In doing so, we demonstrate how the ability to infer the probability distribution of the missing image, as opposed to a single image recovery, can be used by the end-user to quantify the reliability of the imputed results.
      PubDate: 2022-08-03
       
  • Development of an RVE using a DEM–FEM scheme under modified approximate
           periodic boundary condition to estimate the elastic mechanical properties
           of open foams

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      Abstract: In this article, a methodology based on Discrete Element Method (DEM) and Finite Elements Method (FEM) combined with modified Approximate Periodic Boundary Condition (mAPBC) has been utilized to develop a cubic Representative Element Volume (RVE). The developed methodology has then been effectively utilized to estimate the elastic mechanical properties of open foams, validated for the case of Ti open-cell foams with non-regular distribution of spherical pores. The non-regular distribution of overlapping pores was generated by means of DEM, while the homogenized elastic constants for foams with 30–70 percentage porosity are computed using FEM based on a Small Lineal Perturbation Method (SLPM). The accuracy of the developed approach has been demonstrated through the comparison of the simulations obtained for different RVE sizes with experimental results and predictive models available in literature. Graphic abstract
      PubDate: 2022-08-01
       
  • An improved model along with a spectral numerical simulation for
           fractional predator–prey interactions

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      Abstract: Abstract Predator–prey models appear in various fields of bio-mathematics used for the analysis of interactions of biological systems. Due to the complexities of the physical context for the real-world problems of food chain dynamics, introducing new models compatible with experimental results stays ongoing research. Many models have been proposed and analyzed for these systems in recent years. In this paper, we propose a new fractional-order predator–prey model with negative feedback on both species with memory-dependent effects, which increases the compatibility level of the model. Then we present a novel Laguerre spectral numerical simulation for the proposed model by introducing Laguerre modal basis functions with collocation and Galerkin techniques. We then transfer the nonlinear model into a system of algebraic equations, which is solved by efficient numerical solvers. Finally, we provide some test problems to show the efficiency of the proposed model and the computational method.
      PubDate: 2022-08-01
       
  • A multi-objective optimization algorithm for feature selection problems

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      Abstract: Abstract Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.
      PubDate: 2022-08-01
       
  • The overall framework design of automatic logistics system using a hybrid
           ANN-PSO model

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      Abstract: Abstract Automated logistic systems are commonly applied in the enterprise logistic processes while allowing to improve the reliability and efficiency of logistics procedures with computer simulation. The logistics system focuses on realizing the space and time efficiency of material and understanding the links of logistics to achieve the optimum economic effect. Therefore, in this study, 150 data were collected from 20 hospitals in Malaysia to adopt the automated logistic system under simulation of the computer to decrease the costs of hospitals and increase the serviceability (output). Since obtaining this information for the automated logistic systems is not easy and has a high cost in a traditional way, this study was performed by the integration of artificial neural network (ANN) and particle swarm optimization (PSO) as the ANN-PSO. The input data that were obtained from the processing of automated logistic system in hospitals verified by using a hybrid of ANN-PSO. Later, the results were measured with five regression indicators of determination coefficient (R2), root mean square (RMSE), Pearson correlation coefficient (r), Nash–Sutcliffe model efficiency coefficient (NSE) and Willmott’s index (WI). Considering the results of RMSE, RSQR and r in both testing and training phases, the good performance of ANN-PSO in determining the effectiveness of applying automated logistic system using computer simulation method in hospital management, in terms of raising its serviceability and reducing the costs was proved. As a result, it was shown that ANN-PSO can successfully determine the effectiveness of using an automated logistic system under computer simulation method in hospital management.
      PubDate: 2022-08-01
       
  • Efficient curvature-constrained least cost route optimization on parallel
           architectures

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      Abstract: Abstract When choosing a path for a linear infrastructure between two terminals, several types of constraints apply for installation and operational criteria. For offshore pipelines or cables, curvature constraints are typically important not only due to the flexural rigidity but also the maneuverability of the laying vessels. Roads are subject to similar curvature constraints to ensure the safety of users travelling at the design speed. Yet, such constraints are not taken into account in traditional least cost routing methods, often based on Dijkstra’s algorithm. Post-process smoothing is often necessary, resulting in non-optimal routes. We present a new method for least cost route optimization, allowing to incorporate the curvature constraints into the primary calculations as opposed to a post-determination consideration. With this technique, smoothing of the least cost route becomes unnecessary, preserving its optimal character. Optimization algorithms for the trajectories of forward-moving vehicles were adapted for the routing of linear infrastructures and modified to include an angular discretization of variable resolution, resulting in faster and more accurate results. In addition to the inclusion of curvature constraints, the proposed method offers a higher flexibility in terms of local route orientation compared to the traditional Dijkstra’s algorithm, producing routes of lower cost. The numerical solver was implemented on parallel architectures, to compute optimal routes on realistic domains in reasonable computational times. For personal computers with a few computing cores, the OpenMP implementation on Central Processing Units does not significantly increase the iteration count and provides significant speedups. For certain configurations, the use of Graphical Processing Units reduces further the computational time.
      PubDate: 2022-08-01
       
  • Computer simulation via a couple of homotopy perturbation methods and the
           generalized differential quadrature method for nonlinear vibration of
           functionally graded non-uniform micro-tube

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      Abstract: Abstract In this paper, to improve the vibrational response of microstructures, the impact of the nonlinear modal analysis of axially functionally graded (AFG) truncated conical micro-scale tube including the thermal loading for the different type of cross sections such as uniform section, linear tapered section, convex section, the exponential section are studied that are applicable for various application, for example, the micro-thermal fins, macro-/micro-fluid-flow diffuser, fluid-flow nozzle, fluid-flow throat, micro-sensor, etc. The nonlinear equations are obtained applying Hamilton’s principles based on the modified couple stress to determine the size effect and Euler–Bernoulli beam theory considering the von-Kármán’s nonlinear strain. The material combination varies along the tube’s length, denouncing the AFG tube made by metal and ceramic phases. The nonlinear equations are solved by applying a couple of homotopy perturbation methods (HPM) to calculating the nonlinear results and the generalized differential quadrature method (GDQM) to providing the initial conditions. The linear and nonlinear results presented the effect of various cross sections and other parameters on the micro-tube frequency that are valuable to design and manufacture the micro-electro-mechanical systems (MEMS).
      PubDate: 2022-08-01
       
  • A wavelet approach for the variable-order fractional model of ultra-short
           pulsed laser therapy

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      Abstract: Abstract In this paper, the ultra-short pulsed laser treatment is numerically simulated for a focused laser beam applied to a cylindrical domain. To do so, the general form of the variable-order fractional-order, dual-phase lag bioheat transfer equation is implemented. To determine the major affecting parameters, the dimensionless form of the heat equation is derived and solved numerically. An efficient method based on the 2D Legendre wavelets is developed to provide a numerical solution for this variable-order time fractional model. The man advantage of the proposed algorithm is that it converts the solution of the problem into solution of a system of algebraic equations. The validity of the formulated method is investigated through one numerical example. The effect of several operational and thermo-physical properties including the phase lag time, fractional order, and the duration of active laser beam in each on/off cycle on the thermal field and heat penetration depth is examined. According to the results, it is concluded that by increasing the fractional order from 0.1 to 0.9, 65.1% increase in the penetration length occurs.
      PubDate: 2022-08-01
       
  • Low occupancy high performance elemental products in assembly free FEM on
           GPU

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      Abstract: Abstract Assembly free FEM bypasses the assembly step and solves the system of linear equations at the element level using Conjugate Gradient (CG) type iterative solver. The smaller dense Matrix-vector Products (MvPs) are encapsulated within the CG solver and are computed either at element level or degree of freedom (DoF) level. Both these strategies exploit the computing power of GPU effectively, but the performance is lagging due to the uncoalesced global memory access on GPU. This paper proposes an improved MvP strategy in assembly free FEM, which improves the performance by coalesced global memory access using on-chip faster shared memory and using the texture cache memory on GPU. Since GPU has limited shared memory (in few KBs), the proposed technique suffers from a problem known as low occupancy. Despite the low occupancy issue, the proposed strategy outperforms both element based and DoF based MvP strategies on GPU. Numerical experiments compared with element level and DoF level strategies on GPU and found that, GPU instance of proposed MvP outperforms both strategies approximately by factor of 7 and 1.5 respectively.
      PubDate: 2022-08-01
       
  • Blasting pattern optimization using gene expression programming and
           grasshopper optimization algorithm to minimise blast-induced ground
           vibrations

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      Abstract: Abstract Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.
      PubDate: 2022-08-01
       
  • Variable-fidelity hypervolume-based expected improvement criteria for
           multi-objective efficient global optimization of expensive functions

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      Abstract: Abstract Variable-fidelity surrogate-based efficient global optimization (EGO) method with the ability to adaptively select low-fidelity (LF) and high-fidelity (HF) infill point has emerged as an alternative to further save the computational cost of the single-fidelity EGO method. However, in terms of the variable-fidelity surrogate-assisted multi-objective optimization methods, existing methods rely on empirical parameters or are unable to adaptively select LF/HF sample in the optimal search process. In this paper, two variable-fidelity hypervolume-based expected improvement criteria with analytic expressions for variable-fidelity multi-objective EGO method are proposed. The first criterion relies on the concept of variable-fidelity expected improvement matrix (VFEIM) and is obtained by aggregating the VFEIM using a simplified hypervolume-based aggregation scheme. The second criterion termed as VFEMHVI is derived analytically based on a modified hypervolume definition. Both criteria can adaptively select new LF/HF samples in the iterative optimal search process to update the variable-fidelity models towards the HF Pareto front, distinguishing the proposed methods to the rests in the open literature. The constrained versions of the two criteria are also derived for problems with constraints. The effectiveness and efficiency of the proposed methods are verified and validated over analytic problems and demonstrated by two engineering problems including aerodynamic shape optimizations of the NACA0012 and RAE2822 airfoils. The results show that the VFEMHVI combined with the normalization-based strategy to define the reference point is the most efficient one over the compared methods.
      PubDate: 2022-08-01
       
  • VAE-TALSTM: a temporal attention and variational autoencoder-based long
           short-term memory framework for dam displacement prediction

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      Abstract: Abstract The safety and health monitoring of dams has attracted increasing attention. In this paper, a novel prediction model based on variational autoencoder (VAE) and temporal attention-based long short-term memory (TALSTM) network is proposed for the long-term deformation of arch dams. In the proposed model, the convolutional neural network-based VAE is applied to extracting the features of environmental data. The TALSTM is employed to construct the relationship between the dam displacement and extracted features. For verification, an arch dam is taken as an example. Through the comparison among nine baseline prediction models, the proposed model is more stable and effective than other prediction models. Furthermore, the proposed model could capture the features of environmental data accurately and provide better prediction results. Therefore, the proposed model is more suitable for engineering applications.
      PubDate: 2022-08-01
       
  • Quasi-3D large deflection nonlinear analysis of isogeometric FGM
           microplates with variable thickness via nonlocal stress–strain gradient
           elasticity

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      Abstract: Abstract Via the nonlocal stress–strain gradient continuum mechanics, the microscale-dependent linear and nonlinear large deflections of transversely loaded composite sector microplates with different thickness variation schemes are investigated. Microplates are assumed to be prepared from functionally graded materials (FGMs) the characteristics of which are changed along the thickness direction. A quasi-3D plate theory with a sinusoidal transverse shear function in conjunction with a trigonometric normal function was employed for the establishment of size-dependent modelling of FGM microplates with different thickness variation schemes. Then, to solve the nonlocal stress–strain gradient flexural problem, the non-uniform rational B-spline type of isogeometric solution methodology was applied for an accurate integration of geometric discerptions. It was found that the gap between load–deflection curves drawn for linear, concave and convex thickness variation patterns became greater by changing FGM composite microplate boundary conditions from clamped to simply supported. In addition, it was found that by considering only the nonlocal size effect, the plate deflection obtained by the nonlocal strain gradient quasi-3D plate model was greater than that extracted by the classical continuum elasticity because of the softening character of nonlocal size effect, while the strain gradient microstructural size dependency acted in opposite way and represented a stiffening character.
      PubDate: 2022-08-01
       
  • Research on slope reliability analysis using multi-kernel relevance vector
           machine and advanced first-order second-moment method

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      Abstract: Abstract To increase the efficiency and accuracy in slope stability analysis, a reliability analysis method based on machine learning and the advanced first-order second-moment (AFOSM) method was proposed, and the partial derivative of the machine-learning algorithm was derived. First, a multi-kernel was introduced to establish the multi-kernel relevance vector machine (MKRVM). Then, the kernel parameters of the MKRVM were optimized by the harmony search (HS) method to use the high-precision MKRVM method instead of the traditional methods for determining the factor of safety. It was necessary to obtain the partial derivative of the performance function, which was explicitly expressed by the trained MKRVM in this paper. Finally, the AFOSM was adopted to calculate the reliability index of the slope, as the AFOSM was more reliable because the design point was located at the failure surface. With two samples, from a single-layer slope and a multilayer slope, the calculation results show that the MKRVM–AFOSM is easy to use, highly computationally efficient, and reliable.
      PubDate: 2022-08-01
       
  • An efficient method by nesting adaptive Kriging into Importance Sampling
           for failure-probability-based global sensitivity analysis

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      Abstract: Abstract Failure-probability-based global sensitivity (FP-GS) analysis can measure the effect of the input uncertainty on the failure probability. The state-of-the-art for estimating the FP-GS are less efficient for the rare failure event and the implicit performance function case. Thus, an adaptive Kriging nested Importance Sampling (AK-IS) method is proposed in this work to efficiently estimate the FP-GS. For eliminating the dimensionality dependence in the calculation, an equivalent form of the FP-GS transformed by the Bayes’ formula is employed by the proposed method. Then the AK model is nested into IS for recognizing the failure samples. After all the failure samples are correctly identified from the IS sample pool, the failure samples are transformed into those subjected to the original conditional probability density function (PDF) on the failure domain by the Metropolis–Hastings algorithm, on which the conditional PDF of the input on the failure domain can be estimated for the FP-GS finally. The proposed method highly improves the efficiency of estimating the FP-GS comparing with the state-of-the-art, which is illustrated by the results of several examples in this paper.
      PubDate: 2022-08-01
       
 
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