Computation Time Research Articles

Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.

The path planning algorithm for UUV based on the fusion of grid obstacles of artificial potential field

The artificial potential field approach (APFA) has the advantages of short computation time and easy implementation, widely used in path planning. The problems of local oscillation and susceptibility to local minima for APFA are considered in three dimensions (3D) path planning application of unmanned underwater vehicle (UUV), and the artificial potential field grid method (APFG) is proposed. The core principles of APFG include three key methods: firstly, obstacles are rasterized in a 3D map, and repulsive points are attached to the grid surface to alter the range of repulsive influence, thereby compensating for the issue of longer planned paths caused by the idealized modeling in traditional APFA; secondly, the repulsion function of the grid surface is modified to ensure that the potential field is parallel to the grid surface, thus addressing the problem of local oscillation in UUV path planning. Additionally, the introduction of virtual target points helps prevent the UUV from getting trapped in local optima, effectively overcoming the drawbacks of local oscillation and convergence to local minima in 3D path planning for UUV. The simulations show that APFG effectively overcomes the shortcomings of local oscillations and the tendency to fall into local minima for UUV in 3D path planning, and the path planning is smooth and efficient.

Shell element-based prediction of process-induced deformation considering the different fabric parameters and stacking sequences of CFRP woven composites

Herein, we present a finite element method (FEM) based curing analysis for predicting the process-induced deformation (PID) of CFRP woven composites using shell-type elements. This method can efficiently consider various fabric parameters and stacking sequences, even for complex structures. For woven composite plates and L-shaped flanges, FEM-based PID prediction using shell elements reduced computation times by 50–59% compared to solid elements, while maintaining acceptable accuracy with relative errors of 0.17–7.35%. Furthermore, the impacts of various fabric parameters and stacking sequences on the PID of woven composites are analyzed in detail. The main finding is that the variation in material properties due to fabric parameters and stacking sequences significantly impacts the PID of woven composite structures. This prediction method can be applied in the design of composite structures and molds, thereby enhancing the dimensional precision and performance of composite structures.

Experimental and numerical investigation on detection of BVID in CFRP composite plates using vibro-acoustic modulation

ABSTRACT Carbon fibre reinforced plastic (CFRP) composite has gained popularity due to the advantages such as its high strength-to-weight ratio and high corrosion resistance, but susceptibility to barely visible impact damage limits their use in engineering. This study experimentally and numerically investigates the detection of BVID in CFRP composite plates using Vibro-acoustic modulation (VAM) technique. An experimental system of VAM was constructed for measuring sidebands induced by the nonlinear effects of BVID. An integrated finite element (FE) model is developed to further explore the nonlinear interaction between the BVID, low-frequency pump, and high-frequency probe waves. To address this issue of the mesh size and computational time step mismatch caused by the large difference in frequency between the pump and probe waves, a regionally refined mesh was employed at the BVID region. Furthermore, the effects of BVID’s size and depth on the modulation were discussed. Through the Hilbert-Huang Transform (HHT), the amplitude modulation (AM) and frequency modulation (FM) indexes were extracted from the VAM responses to quantitatively characterise the impact damages. The results demonstrate that the MIA index increases steadily with the exciting voltage and damage degree, making it a suitable indicator to accurately assess the impact damage in CFRP composites.

A method to shorten the time of high-dimensional matrix operations and convolution operations

Serverless computing, or Function-as-a-Service (FaaS), is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers which is a new way to solve the problem that the need of client.By serverless computing clients can easily solve their problems anytime,anywhere.However, the complex convolution operations and high-dimensional matrix operations can still consume a lot of resources such as computation time and memory occupancy in serverless computing. In this paper, firstly, the author selects two frequently used models in serverless computing, and then divide them into different numbers of partitions respectively to study the impact of partitioning methods on the computation time and memory occupancy. The author show that different partitioning methods can make an obviously impact on the computation time and memory occupancy,whitch shows that the proper partitioning of high-dimensional matrices and tensors can make a significantly reduction of calculation time and memory occupancy.

Effect of plasma initialization on 3D PIC simulation of Hall thruster azimuthal instability

Abstract The lack of understanding of the azimuthal instability and the resulting electron anomalous transport limits further improvement of Hall thrusters. Compared to theoretical and experimental approaches, the numerical particle-in-cell (PIC) simulation is a suitable and powerful tool, which has been widely applied to investigate the azimuthal instability, and great progress has been made in the past decades. However, PIC simulations are intrinsically computationally expensive, and it is realized that the Hall thruster azimuthal instability has a three dimensional nature. Therefore, massive 3D PIC simulation must be carried out to completely reveal the mechanism of the instability. In this paper, the effect of plasma initialization on 3D PIC simulation of Hall thruster azimuthal instability is studied as a starting point. It is found that by initializing with ion density and velocity fitting functions to the steady-state simulation results, a faster convergence can be obtained and the computational time can be reduced by about 1.5 times. Typical fitting functions of ion density, drifting velocity, and temperature are given, and the influence of different initialization profiles is presented.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems.

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

AbstractThis paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure.

Fourier-Based Function Generation of Four-Bar Linkages With an Improved Sampling Points Adjustment and Sylvester's Dialytic Elimination Method

Abstract Four-bar linkages are critical fundamental elements of many mechanical systems, and their design synthesis is often mathematically complicated with iterative numerical solutions. Analytical methods based on Fourier coefficients can circumvent these difficulties but have difficulties with sampling points adjustment and solutions of the design equations in previous studies. In this paper, an improved Fourier-based analytical synthesis method is presented, which transforms the function generation synthesis of planar four-bar linkages into a problem of solving design equations. Calculation of the Fourier coefficients is discussed, including the discretization of the prescribed function and an improved sampling points adjustment method. It is shown that the Fourier coefficients can be computed efficiently and accurately by discretizing the prescribed function with a small number of sampling points. The proposed sampling adjustment method overcomes the difficulty of easily resulting in non-Grashof solutions by considering the complete period of the prescribed function. An improved Sylvester's dialytic elimination method is presented to solve design equations. The method reduces the computation time and avoids cumbersome procedures without generating additional invalid solutions. Several examples are presented to demonstrate the advantages of the proposed synthesis method, which is easy-understanding and efficient, and yields more accurate solutions than available synthesis methods.

Research on motion planning system for wall-climbing mobile manipulator for large steel structures welding operation

Purpose The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility. Design/methodology/approach A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling. Findings Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions. Originality/value The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (”conv5-FC3”, ResNet and ”SECNN”) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the ”conv5-FC3” network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted.

Improvement of local outlier factor algorithms for lithium-ion battery fault diagnosis

Fault diagnosis is one of the most important active strategies to protect lithium-ion batteries (LIBs) from safety accidents. The tasks of fault diagnosis usually can be divided into three levels, i.e., (1) fault detection, (2) fault isolation, and (3) fault estimation. The local outlier factor (LOF) method has been proved effective in conducting fault detection (level 1 of fault diagnosis) for LIB energy storage systems (ESSs). However, the original LOF algorithm (OLOF) may fail to make correct fault estimation (level 3 of fault diagnosis). Therefore, in this study, two novel LOF algorithms, i.e., the improved LOF algorithm (ILOF) and the simplified LOF algorithm (SLOF), are put forward to enhance the capability of fault estimation. The performance of the three LOF algorithms: OLOF, ILOF and SLOF, are firstly compared via three mathematical cases. Their capabilities in fault diagnosis are further investigated based on the simulated data from an air-cooled LIB ESS and the experimental data from a water-cooled LIB ESS. Results prove the effectiveness of all the three algorithms in fault detection; nevertheless, when the OLOF algorithm fails to make correct fault estimation, both of the two proposed novel algorithms, i.e., the ILOF algorithm and the SLOF algorithm, present the capability in making correct fault estimation. It is found as well that the ILOF algorithm is better than the SLOF algorithm in robustness though the latter is more efficient than the former in memory usage and computational time.

Robust optimization strategies for contour uncertainties in online adaptive radiation therapy

Objective. Online adaptive radiation therapy requires fast and automated contouring of daily scans for treatment plan re-optimization. However, automated contouring is imperfect and introduces contour uncertainties. This work aims at developing and comparing robust optimization strategies accounting for such uncertainties. Approach. A deep-learning method was used to predict the uncertainty of deformable image registration, and to generate a finite set of daily contour samples. Ten optimization strategies were compared: two baseline methods, five methods that convert contour samples into voxel-wise probabilities, and three methods accounting explicitly for contour samples as scenarios in robust optimization. Target coverage and organ-at-risk (OAR) sparing were evaluated robustly for simplified proton therapy plans for five head-and-neck cancer patients. Results. We found that explicitly including target contour uncertainty in robust optimization provides robust target coverage with better OAR sparing than the baseline methods, without increasing the optimization time. Although OAR doses first increased when increasing target robustness, this effect could be prevented by additionally including robustness to OAR contour uncertainty. Compared to the probability-based methods, the scenario-based methods spared the OARs more, but increased integral dose and required more computation time. Significance. This work proposed efficient and beneficial strategies to mitigate contour uncertainty in treatment plan optimization. This facilitates the adoption of automatic contouring in online adaptive radiation therapy and, more generally, enables mitigation also of other sources of contour uncertainty in treatment planning.

Enhanced thermal management strategy for supercapacitors using phase change materials: A numerical investigation

The integration of phase change materials (PCMs) presents a promising possibility for enhancing the thermal management of supercapacitors (SCs), which are vital components in various energy storage systems. This research focuses on investigating the thermal behavior of SCs with integrated PCM, employing finite element method (FEM) simulations to solve the transient thermal problem. Through a comparative analysis at various time instants, the transient thermal response of SCs is examined, shedding light on the efficacy of PCM-based thermal management strategies. Moreover, parametric studies are conducted to investigate the influence of PCM key characteristics, like melting temperature and layer thickness on the SC thermal response. Additionally, the impact of enhanced PCM thermal conductivity is explored by integrating high-conductive short carbon fibers (CF) within the PCM matrix. This investigation encompasses various PCM melting points, allowing for a comprehensive understanding of the interplay between PCM properties and SC thermal response. The results indicate that a notable decline in the highest temperatures of the SC can be achieved, leading to a reduction of about 9 °C depending on the PCM melting temperature and the improved thermal conductivity. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time. The findings of this study not only provide insights into optimizing PCM-based thermal management strategies for SCs but also contribute to advancing the design and performance of energy storage systems by addressing crucial thermal challenges.

DEVELOPMENT OF AN ELECTRONIC EXAMINATION PLATFORM USING FACE RECOGNITION METHODS

Online systems face a major challenge in efficiently monitoring participants and students throughout lectures, particularly during exams. Establishing robust approaches and technologies is essential for identifying unjust, unethical, and illegal conduct in educational environments, particularly during academic courses and examinations. This study introduces a novel online proctoring system that employs deep learning to continuously supervise physical locations without requiring a human proctor. The system utilizes face detection and recognition algorithms to implement biometric procedures, including facial recognition. This work presents a novel approach for face recognition training, which involves an incremental training procedure. This eliminates the requirement for an additional step, resulting in reduced computation cost and time. In order to achieve high accuracy, the suggested model evaluated three distinct face detectors: HOG, MTCNN, and Yoloface. The assessment of the suggested model demonstrates that the HOG approach has surpassed the others. The proposed approach yields a significantly high accuracy rate.

Introducing Field-Programmable Gate Arrays in Genotype Phasing and Imputation

Abstract We recently developed EagleImp, a free software that combines genotype phasing and imputation in a single tool. By introducing algorithmic and technical improvements we accelerated the classical two-step approach using Eagle2 and PBWT. Here, we demonstrate how to use Field-Programmable Gate Arrays (FPGAs) to accelerate EagleImp even further by a factor of up to 93% without loss of phasing and imputation quality. Due to the speed advantage over a not accelerated processor-based implementation, the FPGA-extension of EagleImp offers the user the ability to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality. EagleImp and its FPGA-extension are freely availabe at https://github.com/ikmb/eagleimp and https://github.com/ikmb/eagleimp-fpga.

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

In this paper, we propose Data-driven simulation-assisted Physics-learned Artificial Intelligence (DPAI), a deep-learning algorithm to simulate heat diffusion in large-grain polycrystalline materials. The DPAI model captures the spatio-temporal representation of heat diffusion in the material from input sequences from the training dataset. The training dataset consists of various temperature plots of polycrystalline materials taken from Finite Element (FE) simulations having varying numbers of grains oriented in random directions with a single-point heat source at the center. The arbitrary plane of the 3D microstructure of these materials is represented using 2D Voronoi tessellations. Voronoi configurations are used to model the geometry of the 2D Computer-Aided Design (CAD) model. Each cell of the Voronoi tessellation represents one grain of the microstructure. This CAD model is used as an input to the FE for solving heat diffusion equations. To model the near-realistic material anisotropy and accurately measure temperature differences at cell boundaries, a smaller mesh size is required in FE modeling, which takes considerable solver time. Therefore, the proposed Deep learning model significantly reduces the computational time while maintaining accuracy as compared to conventional numerical techniques. After training, the effectiveness of the trained DPAI model is examined by modeling larger domain problems involving a greater number of grains and varying material properties. The simulation result is qualitatively compared with the experiment. A scaled-up version of the microstructure is represented using Unidirectional Carbon Fiber laminate. The laminate is heated with a point heat source and the temperature plots are captured using Infrared Camera.

Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.

An approach to modelling defocusing and keyhole reflectivity in keyhole laser processes

AbstractKeyhole laser-based processes have been adopted widely due to the high-quality welds and cuts that they produce and the accurately controlled dimensions of the end part. Fast-running simulation tools are required in order to estimate the dependence of process outputs, efficiency, and stability on the process variables. This work presents a fast-running simulation tool for keyhole laser-based processes with inert or no assisting gas that takes into account the effect of laser beam defocusing and the alteration of the effective reflectivity of the material due to the keyhole cavity. The model is built upgrading a successful model for the thermal phenomena based on finite differences and the enthalpy method. The presented model is compared to experimental results for the laser cutting process with both CO$$_2$$ 2 and fiber laser sources and inert assisting gas. The model accurately predicts cutting depth and kerf, heat-affected zone width, and the dependence of the cutting depth on the position of the focal plane. The model manages to achieve reduced computational time enabling its utilization in process digital twins.

Modified Vector-Controlled DFIG Wind Energy System Using Robust Model Predictive Rotor Current Control

AbstractAs wind energy (WE) technologies become more prevalent, there are significant concerns about the electrical grid’s stability. Despite their many advantages, a WE system based on a doubly fed induction generator is vulnerable to power grid disruptions. Due to being built on traditional controllers, the generator systems with standard vector control (VC) cannot resist disturbances. This paper seeks to provide a novel VC that is resistant to outer perturbations. For this purpose, a finite state space model predictive control (FS-MPC) is utilized instead of the internal current loop of the standard VC. The objective of the proposed system is to minimize the error between the measured currents and their reference values and, also, reduces the total harmonic distortion (THD) of the current. The cost function can optimize this requirement, which reduces the computation time. The VC-FS-MPC was implemented using the MATLAB, where a 1.5-MW generator operating under different conditions was used. The necessary graphical and numerical results were extracted to show the efficiency, effectiveness, and ability of the VC-FS-MPC to improve the characteristics of the studied energy system. The results show the flexibility and distinctive performance of the VC-FS-MPC in the various tests used, as the THD of stator current was reduced in the second test compared to the first test by an estimated percentage of 61.79%. Moreover, the THD of rotor current was reduced compared to the first test by an estimated percentage of 23.56%. These ratios confirm the effectiveness of the VC-FS-MPC in improving the characteristics of the proposed system.