Issue Of Computational Cost Research Articles

A second-order particle Fokker-Planck model for rarefied gas flows

The direct simulation Monte Carlo (DSMC) method has become a powerful tool for studying rarefied gas flows. However, for the DSMC method to be effective, the cell size must be smaller than the mean free path, and the time step smaller than the mean collision time. These constraints make it difficult to use the DSMC method in multiscale rarefied gas flows. Over the past decade, the particle Fokker-Planck (FP) method has been studied to address computational cost issues in the near-continuum regime. To capture the main features of the Boltzmann equation, various FP models have been proposed, such as the quadratic entropic FP (Quad-EFP) and the ellipsoidal statistical FP (ESFP). Nevertheless, few studies have clearly demonstrated that the FP method offers a computational advantage over the DSMC method without sacrificing accuracy. This is because conventional particle FP methods have employed first-order accuracy schemes. The present study proposes a unified stochastic particle ESFP (USP-ESFP) model. This model improves the accuracy of shear stress and heat flux predictions. Additionally, a spatial interpolation scheme is introduced to the particle FP method. The numerical test cases include relaxation problem, Couette flows, Poiseuille flows, velocity perturbation, and hypersonic flows around a cylinder. The results show that the USP-ESFP model agrees well with both analytical and DSMC results. Furthermore, the USP-ESFP model is found to be less sensitive to cell size and time step than the DSMC method, resulting in a factor of four speed-up for the considered hypersonic flow around a cylinder.

Enhanced Estimation of Axial Compressive Strength for CFRP Based on Microscale Numerical Simulation and the Response Surface Method.

Compressive strength is one of the most important properties of carbon fiber reinforced plastics (CFRP). In this study, a new method for predicting the axial compressive strength of CFRP using the response surface method is developed. We focused on a microbuckling model to predict the compressive strength of unidirectional fiber composites. For the microbuckling model, axial shear properties are required. To obtain the compressive strength for various material properties, we perform individual shear tests and numerical simulations, but these require enormous computational costs and extended time. To address the issue of computational cost, in this study, we propose a new method to predict compressive strength using the response surface method. First, we perform shear simulation in a microscale fracture model for unidirectional CFRP with various parameters of the fiber and resin properties. Based on the results of the shear simulation, the response surface method is used to evaluate and develop prediction equations for the shear properties. This method allows for the study of the objective values of the parameters, without significant computational effort. By comparing both the results predicted from the response surface method (RSM) and the simulation results, we verify the reliability of the prediction equation. As a result, the coefficient of determination was higher than 94%, and the validity of the prediction method for the compressive strength of CFRP using the response surface method (RSM) developed in this study was confirmed. Additionally, we discuss the material properties that affect the compressive strength of composites comprised of fibers and resin. As a result, we rank the parameters as follows: fiber content, elastic modulus after resin yield, yield stress, and initial elastic modulus.

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This study aims to address these limitations: In the feature extraction stage, it introduces adaptive histogram equalization, Gaussian filtering, and discrete cosine transform methods, effectively enhancing and extracting high-quality image features, thereby bolstering the model’s recognition capabilities. To tackle the computational cost issue associated with solving the numerical solutions of neuronal dynamics equations in the KIII model, it replaces the original method with the faster Euler method, reducing time expenses while maintaining good recognition results. In the decision-making stage, several different dissimilarity metrics are compared, and the results indicate that the Spearman correlation coefficient performs best in this context. The improved KIII model is applied to a new domain of traffic sign recognition, demonstrating that it outperforms the baseline KIII model and exhibits certain advantages compared to other models.

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network.

With the advancement of neural networks, more and more neural networks are being applied to structural health monitoring systems (SHMSs). When an SHMS requires the integration of numerous neural networks, high-performance and low-latency networks are favored. This paper focuses on damage detection based on vibration signals. In contrast to traditional neural network approaches, this study utilizes a stochastic configuration network (SCN). An SCN is an incrementally learning network that randomly configures appropriate neurons based on data and errors. It is an emerging neural network that does not require predefined network structures and is not based on gradient descent. While SCNs dynamically define the network structure, they essentially function as fully connected neural networks that fail to capture the temporal properties of monitoring data effectively. Moreover, they suffer from inference time and computational cost issues. To enable faster and more accurate operation within the monitoring system, this paper introduces a stochastic convolutional feature extraction approach that does not rely on backpropagation. Additionally, a random node deletion algorithm is proposed to automatically prune redundant neurons in SCNs, addressing the issue of network node redundancy. Experimental results demonstrate that the feature extraction method improves accuracy by 30% compared to the original SCN, and the random node deletion algorithm removes approximately 10% of neurons.

Efficient Algorithm for Approximating Nash Equilibrium of Distributed Aggregative Games.

In this article, we aim to design a distributed approximate algorithm for seeking Nash equilibria (NE) of an aggregative game. Due to the local set constraints of each player, projection-based algorithms have been widely employed for solving such problems actually. Since it may be quite hard to get the exact projection in practice, we utilize inscribed polyhedrons to approximate local set constraints, which yields a related approximate game model. We first prove that the NE of the approximate game is the ε -NE of the original game and then propose a distributed algorithm to seek the ε -NE, where the projection is then of a standard form in quadratic optimization with linear constraints. With the help of the existing developed methods for solving quadratic optimization, we show the convergence of the proposed algorithm and also discuss the computational cost issue related to the approximation. Furthermore, based on the exponential convergence of the algorithm, we estimate the approximation accuracy related to ε . In addition, we investigate the computational cost saved by approximation in numerical simulation.

On the prediction of particle collision behavior in coarse-grained and resolved systems

The discrete element method (DEM) simulates granular processes and detects inter-particle collisions during the simulation. Detection of collision helps researchers to study the occurrence of particulate mechanisms such as aggregation, breakage, etc. DEM demands high computational costs in simulating industrial-level systems, as it involves an enormous number of particles. DEM coarse-grained model can help to overcome this high computational cost issue. However, the frequency and probability of collisions for different particle size classes may change when coarser particles are introduced. This study introduces a new mathematical formulation, namely the collision dependency function (CDF), which predicts the probability of collisions between different particle classes for systems containing resolved and coarse-grained particles. The CDF is extracted by executing one DEM simulation consisting of number-based uniformly distributed particles. Furthermore, a new optimized scheme is used inside the DEM to store the collision data efficiently. Finally, the collision probabilities between size classes obtained from DEM simulations are compared successfully against their counterparts calculated from the developed model for verification.

Human Fall Detection for Smart Home Caring using Yolo Networks

In order to help the elderly and limit the incidence of falls that result in injuries, effective fall detection in smart home applications is a challenging topic. Many techniques have been created employing both vision and non-vision-based technologies. Many researchers have been drawn to the vision-based technique amongst them because of its viability and application. However, there is still room for improvement in the effectiveness of fall detection given the poor accuracy rate and high computational cost issues with current vision-based techniques. This study introduces a new dataset for posture and fall detection, whose photo images were gathered from Internet resources and data augmentation. It employs YOLO networks for fall detection purpose. Furthermore, different YOLO networks are implemented on our dataset to address the most accurate and effective model. Based on assessment parameters including accuracy, F1 score, recall, and mAP, the performance of the various YOLOv5n, s and YOLOv6s versions are compared. As experimental results showed, the YOLOv5s performed better than other.

Solid Waste Image Classification Using Deep Convolutional Neural Network

Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used at landfill and/or incineration, ultimately leading to improved circular economy. Conventional waste separation relies heavily on manual separation of objects by humans, which is inefficient, expensive, time consuming, and prone to subjective errors caused by limited knowledge of waste classification. However, advances in artificial intelligence research has led to the adoption of machine learning algorithms to improve the accuracy of waste classification from images. In this paper, we used a waste classification dataset to evaluate the performance of a bespoke five-layer convolutional neural network when trained with two different image resolutions. The dataset is publicly available and contains 25,077 images categorised into 13,966 organic and 11,111 recyclable waste. Many researchers have used the same dataset to evaluate their proposed methods with varying accuracy results. However, these results are not directly comparable to our approach due to fundamental issues observed in their method and validation approach, including the lack of transparency in the experimental setup, which makes it impossible to replicate results. Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high accuracy and a high level of methodology transparency is of particular importance in this domain. To investigate the computational cost issue, we used two image resolution sizes (i.e., 225×264 and 80×45) to explore the performance of our bespoke five-layer convolutional neural network in terms of development time, model size, predictive accuracy, and cross-entropy loss. Our intuition is that smaller image resolution will lead to a lightweight model with relatively high and/or comparable accuracy than the model trained with higher image resolution. In the absence of reliable baseline studies to compare our bespoke convolutional network in terms of accuracy and loss, we trained a random guess classifier to compare our results. The results show that small image resolution leads to a lighter model with less training time and the accuracy produced (80.88%) is better than the 76.19% yielded by the larger model. Both the small and large models performed better than the baseline which produced 50.05% accuracy. To encourage reproducibility of our results, all the experimental artifacts including preprocessed dataset and source code used in our experiments are made available in a public repository.

A likelihood-ratio type test for stochastic block models with bounded degrees

A fundamental problem in network data analysis is to test Erdös–Rényi model Gn,a+b2n versus a bisection stochastic block model Gn,an,bn, where a,b>0 are constants that represent the expected degrees of the graphs and n denotes the number of nodes. This problem serves as the foundation of many other problems such as testing-based methods for determining the number of communities (Bickel and Sarkar, 2016; Lei, 2016) and community detection (Montanari and Sen, 2016). Existing work has been focusing on growing-degree regime a,b→∞ (Bickel and Sarkar, 2016; Lei, 2016; Montanari and Sen, 2016; Banerjee and Ma, 2017; Banerjee, 2018; Gao and Lafferty, 2017a,b) while leaving the bounded-degree regime untreated. In this paper, we propose a likelihood-ratio (LR) type procedure based on regularization to test stochastic block models with bounded degrees. We derive the limit distributions as power Poisson laws under both null and alternative hypotheses, based on which the limit power of the test is carefully analyzed. We also examine a Monte-Carlo method that partly resolves the computational cost issue. The proposed procedures are examined by both simulated and real-world data. The proof depends on a contiguity theory developed by Janson (1995).

Uncertainty Quantification Analysis on Silicon Electrodeposition Process Via Numerical Simulation Methods

Abstract Silicon is one of the commonly used semiconductors for various industrial applications. Traditional silicon synthesis methods are often expensive and cannot meet the continuously growing demands for high-purity Si; electrodeposition is a promising and simple alternative. However, the electrodeposited products often possess nonuniform thicknesses due to various sources of uncertainty inherited from the fabrication process; to improve the quality of the coating products, it is crucial to better understand the influences of the sources of uncertainty. In this paper, uncertainty quantification (UQ) analysis is performed on the silicon electrodeposition process to evaluate the impacts of various experimental operation parameters on the thickness variation of the coated silicon layer and to find the optimal experimental conditions. To mitigate the high experimental and computational cost issues, a Gaussian process (GP) based surrogate model is constructed to conduct the UQ study with finite element (FE) simulation results as training data. It is found that the GP surrogate model can efficiently and accurately estimate the performance of the electrodeposition given certain experimental operation parameters. The results show that the electrodeposition process is sensitive to the geometric settings of the experiments, i.e., distance and area ratio between the counter and working electrodes; whereas other conditions, such as the potential of the counter electrode, temperature, and ion concentration in the electrolyte bath are less important. Furthermore, the optimal operating condition to deposit silicon is proposed to minimize the thickness variation of the coated silicon layer and to enhance the reliability of the electrodeposition experiment.

An efficient multistage CBIR based on Squared Krawtchouk-Tchebichef polynomials

Image databases are increasing exponentially because of rapid developments in social networking and digital technologies. To search these databases, an efficient search technique is required. CBIR is considered one of these techniques. This paper presents a multistage CBIR to address the computational cost issues while reasonably preserving accuracy. In the presented work, the first stage acts as a filter that passes images to the next stage based on SKTP, which is the first time used in the CBIR domain. While in the second stage, LBP and Canny edge detectors are employed for extracting texture and shape features from the query image and images in the newly constructed database. The proposed CBIR was tested against existing algorithms on well-known database (Wang’s database), where Manhattan distance is used as a similarity metric. The improvement ratio in terms of computation time between the proposed system and existing system achieves 73.99%, which is considered a promising result.

A GPU implementation of least-squares reverse time migration

Least-squares reverse time migration (LSRTM) is a seismic imaging method that can provide higher-resolution image of the subsurface structures compared to other methods. However, LSRTM is computationally expensive. To reduce the computational time of LSRTM, GPU can be utilized. This leads to the objective of this work which is to develop a GPU implementation of LSRTM. In this work, the two-dimensional first-order acoustic wave equations are solved using the second-order finite difference on a staggered grid and a perfectly matched layer is used as an absorbing boundary condition. The adjoint-state method is used to compute the gradient of the objective function. A linear conjugate gradient method is used to minimize the objective function. Both forward- and backward-propagation of wavefields using the finite-difference method are performed on a single GPU using the NVIDIA CUDA library. For a verification purpose, the GPU program of LSRTM was applied to a synthetic data set generated from the Marmousi model. Numerical results show that LSRTM can provide an image with a higher resolution of subsurface structure compared to a conventional RTM image. For a computational cost issue, the GPU-version of LSRTM is significantly faster than the serial CPU-version of LSRTM.

Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study.

BackgroundDue to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19.ObjectiveThe primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians.MethodsIn this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician.ResultsOur approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM.ConclusionsOur proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.

A novel analytic continuation power series solution for the perturbed two-body problem

Inspired by the original developments of recursive power series by means of Lagrange invariants for the classical two-body problem, a new analytic continuation algorithm is presented and studied. The method utilizes kinematic transformation scalar variables differentiated to arbitrary order to generate the required power series coefficients. The present formulation is extended to accommodate the spherical harmonics gravity potential model. The scalar variable transformation essentially eliminates any divisions in the analytic continuation and introduces a set of variables that are closed with respect to differentiation, allowing for arbitrary-order time derivatives to be computed recursively. Leibniz product rule is used to produce the needed arbitrary-order expansion variables. With arbitrary-order time derivatives available, Taylor series-based analytic continuation is applied to propagate the position and velocity vectors for the nonlinear two-body problem. This foundational method has been extended to also demonstrate an effective variable step size control for the Taylor series expansion. The analytic power series approach is demonstrated using trajectory calculations for the main problem in satellite orbit mechanics including high-order spherical harmonics gravity perturbation terms. Numerical results are presented to demonstrate the high accuracy and computational efficiency of the produced solutions. It is shown that the present method is highly accurate for all types of studied orbits achieving 12–16 digits of accuracy (the extent of double precision). While this double-precision accuracy exceeds typical engineering accuracy, the results address the precision versus computational cost issue and also implicitly demonstrate the process to optimize efficiency for any desired accuracy. We comment on the shortcomings of existing power series-based general numerical solver to highlight the benefits of the present algorithm, directly tailored for solving astrodynamics problems. Such efficient low-cost algorithms are highly needed in long-term propagation of cataloged RSOs for space situational awareness applications. The present analytic continuation algorithm is very simple to implement and efficiently provides highly accurate results for orbit propagation problems. The methodology is also extendable to consider a wide variety of perturbations, such as third body, atmospheric drag and solar radiation pressure.

Prediction on the static response of structures with large-scale uncertain-but-bounded parameters based on the adjoint sensitivity analysis

The number of uncertain parameters increases sharply with structures becoming more complex, which leads to huge computational consuming. Two approaches are presented to efficiently predict the static response bounds of structures with large-scale uncertain-but-bounded parameters. These two methods can solve the problem efficiently because the sensitivity information of structural response with respect to uncertain parameters is used. Since the number of the uncertain parameters is so large, it leads to the expansive computational cost issue due to calculating much gradient information. To deal with this issue, the adjoint method is employed to efficiently generate the partial derivative of the structural response with respect to uncertain parameters by fewer number of finite element analysis. Based on the sensitivity information, the adjoint-based Taylor series Expansion Method (ATEM) is proposed to approximate the structural response when the uncertain range is small. The ATEM has high efficiency because only the value and partial derivatives of one point are required. Moreover, the partial derivatives required in ATEM are efficiently calculated by the adjoint method. Alternatively, an adjoint-based optimization method (AOM) is constructed to find the max/min value of the response bound, which is a method with high accuracy even the uncertain range is large. It provides two choices to address the uncertainty propagation problems under different circumstances. Numerical examples are provided to illustrate the accuracy and efficiency of the proposed approaches.

Nonlinear dynamic analysis of frame-core tube building under seismic sequential ground motions by a supercomputer

Historical records indicate that strong earthquakes are usually accompanied by aftershocks with large peak ground accelerations. Structural damages caused by the mainshock can be further aggravated by aftershocks, which can lead to structural collapse. Current structure seismic design practices generally consider the mainshock effects only. Performance of buildings subjected to mainshock-aftershock sequential ground motions, should always be fully investigated. Previous studies on sequential ground motion focus mainly on frame structures. There is a paucity of publications addressing other type of structures, especially high-rise buildings. The reasons for this omission have been identified as the complexity of high-rise buildings and unaffordable computational costs related to the nonlinear dynamic analysis of long duration sequential ground motions. Thus, the authors used Tianhe-2, once known as the fastest supercomputer in the world, to conduct nonlinear dynamic analysis of a typical 20-story frame-core tube building subjected to sequential mainshock-aftershock ground motions. This study focuses on 104 mainshock and aftershock ground motions from four different sites. Thus, the effects of mainshocks only and sequential ground motions of a frame-core tube structure at four different sites were analyzed in this study's finite element model. The Performance of these structures under mainshocks and sequential ground motions are compared in terms of inter-story displacement ratio, hysteretic energy and damage index based on the Park-Ang model. The results prove that a supercomputer can be used to solve the computational cost issue in structural engineering and emphasize that the effects of sequential earthquakes need to be considered in structural design, even for frame-core tube structures with lateral resisting members.

Effects of Extraframework Species on the Structure-Based Prediction of 31P Isotropic Chemical Shifts of Aluminophosphates

31P NMR spectroscopy is a valuable technique for the characterization of the local structure of aluminophosphates (AlPOs), capable of providing information on the number of crystallographic P sites, their relative populations, and the positions of any dopant atoms in the framework. Assigning the 31P spectra may, however, require multinuclear NMR experiments and/or density functional theory (DFT) calculations, which can be time-consuming, computationally costly, and challenging in cases involving disorder or dynamics. To address the issue of computational cost, we recently demonstrated a simple relationship between the local structure around P (primarily in terms of the mean P–O bond length and P–O–Al bond angle) and the 31P isotropic chemical shift, δiso, calculated by DFT for a series of calcined AlPOs. Here, we extend this approach to as-made AlPOs where we show that, at least to a first approximation, the presence of framework-bound anions and/or guest species within the pores of AlPOs can be translate...

Efficient Permeability Measurement and Numerical Simulation of the Resin Flow in Low Permeability Preform Fabricated by Automated Dry Fiber Placement

We propose a new experimental method using a Hassler cell and air injection to measure the permeability of fiber preform while avoiding a race tracking effect. This method was proven to be particularly efficient to measure very low through-thickness permeability of preform fabricated by automated dry fiber placement. To validate the reliability of the permeability measurement, the experiments of viscous liquid infusion into the preform with or without a distribution medium were performed. The experimental data of flow front advancement was compared with the numerical simulation result using the permeability values obtained by the Hassler cell permeability measurement set-up as well as by the liquid infusion experiments. To address the computational cost issue, the model for the equivalent permeability of distribution medium was employed in the numerical simulation of liquid flow. The new concept using air injection and Hassler cell for the fiber preform permeability measurement was shown to be reliable and efficient.

Sequential surrogate modeling for efficient finite element model updating

Despite the numerous studies concerning finite element model updating (FEMU), a challenging computational cost issue persists. Therefore, surrogate modeling has recently gained considerable attention in FEMU. Conventionally, surrogate models are constructed by identical samples for all outputs. It is very inefficient and subjective, if various response-surfaces exhibit even for identical parameters. Accordingly, we propose a sequential surrogate modeling for FEMU. It uses infill criteria to guide sampling for updating surrogate models automatically. The proposed method is successful to construct the different response-surfaces and apply FEMU. It is promising for constructing surrogate models with minimal user intervention and tremendous computational efficiency.

Compressed video matching: Frame-to-frame revisited

This paper presents an improved frame-to-frame (F-2-F) compressed video matching technique based on local features extracted from reduced size images, in contrast with previous F-2-F techniques that utilized global features extracted from full size frames. The revised technique addresses both accuracy and computational cost issues of the traditional F-2-F approach. Accuracy is improved through using local features, while computational cost issue is addressed through extracting those local features from reduced size images. For compressed videos, the DC-image sequence, without full decompression, is used. Utilizing such small size images (DC-images) as a base for the proposed work is important, as it pushes the traditional F-2-F from off-line to real-time operational mode. The proposed technique involves addressing an important problem: namely the extraction of enough local features from such a small size images to achieve robust matching. The relevant arguments and supporting evidences for the proposed technique are presented. Experimental results and evaluation, on multiple challenging datasets, show considerable computational time improvements for the proposed technique accompanied by a comparable or higher accuracy than state-of-the-art related techniques.