Airfoil flow-field prediction is important for aerodynamic design, but wind-tunnel testing and computational fluid dynamics (CFD) remain costly and time-consuming. Deep learning enables fast inference, yet many existing models still rely on fixed gri...
As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generati...
Monocular distance measurement estimates the target distance from the apparent size of a known target, and its accuracy is strongly affected by edge localization accuracy. Conventional pixel-level edge detectors provide only integer-pixel positions,...
The small-data regime remains one of the most important and pressing problems in machine learning. With only a few hundred sample points in the training set, conventional ensemble methods are potentially unreliable because they have high variance, lo...
Minimization of the Euclidean distance between output distribution and Dirac delta functions as a performance criterion is known to match the distribution of system output with delta functions. In the analysis of the algorithm developed based on that...
Synthetic oversampling methods for dealing with imbalanced classification problems have been widely studied. However, the current synthetic oversampling methods still cannot perform well when facing high-dimensional imbalanced financial data. The fai...
A key component of the new stochastic approach for discontinuity shear strength (referred to as StADSS) is characterising the roughness of natural rock discontinuities at full scale to mitigate well-known scale effects on shear strength predictions....
Gas mixture quantification is essential for the recording and reproducing odors, because an odor consists of multiple chemical compounds. Gas mixture quantification using field asymmetric ion mobility spectrometry (FAIMS) was studied. Acetone, ethano...
Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based...
Low back pain (LBP) is one of the leading causes of disability in the world's population, yet there are limitations in providing an objective clinical assessment due to its widespread nature. In this work, five machine learning models (LightGBM, XGBo...
A Generalized Additive Model (GAM) is proposed to predict the pressure drop in a gas–liquid two-phase flow at horizontal, vertical, and inclined pipes based on 21 different dimensionless numbers. It is fitted from 4605 points, considering a flu...
Generative deep learning-based synthetic aperture radar (SAR)-to-optical image translation (SOIT) has been widely employed for cloud removal. However, since cloud-contaminated regions reconstructed by SOIT inevitably contain prediction errors, an add...
In recent years, deep neural networks (DNN) have been widely used in many fields. Lots of effort has been put into training due to their numerous parameters in a deep network. Some complex optimizers with many hyperparameters have been utilized to ac...
This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical spec...
Gas turbines play a critical role in modern power systems, yet their transient operations (e.g., start-up, load mutation) induce significant thermal inertia in metal components, leading to deviations between simulation results and actual performance....
Deep Reinforcement Learning (DRL) is a promising approach for general artificial intelligence. However, most DRL methods suffer from the problem of data inefficiency. To alleviate this problem, DeepMind proposed Prioritized Experience Replay (PER). T...
Physics-Informed Neural Networks (PINNs) often struggle with stiff partial differential equations (PDEs) exhibiting sharp gradients and extreme nonlinearities. We propose a Curriculum-Enhanced (CE) Adaptive Sampling framework that integrates curricul...
Particle-based Variational Inference (ParVI) methods have been widely adopted in deep Bayesian inference tasks such as Bayesian neural networks or Gaussian Processes, owing to their efficiency in generating high-quality samples given the score of the...
In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampli...
The digital beamforming synthetic aperture radar (DBF-SAR) is proposed by scholars as a promising solution to overcome the constraint of the minimum antenna area of the traditional single-channel SAR to achieve high resolution and wide swath (HRWS) b...
The results of measuring gradient strain fields by embedded or mounted point fiber-optic sensors based on Bragg gratings and distributed fiber-optic sensors based on Rayleigh scattering are discussed. Along with the experiment, the results of numeric...
The neural implicit SLAM system performs excellently in static environments, offering higher-quality rendering and scene reconstruction capabilities compared to traditional dense SLAM. However, in dynamic real-world scenes, these systems often experi...
The prediction of soil organic matter is important for measuring the soil’s environmental quality and the degree of degradation. In this study, we combined China’s GF-6 remote sensing data with the organic matter content data obtained fro...
In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; v...
Accurate state-of-health (SOH) estimation is essential for the reliable operation of lithium-ion batteries. However, predicting SOH for previously unseen batteries remains challenging because degradation behavior varies among cells. This study propos...
This study proposes a unified stochastic framework for approximating and computing the gradient of every smooth function evaluated at non-independent variables, using
Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB),...
The globularization of the lamellar α phase by thermomechanical processing and subsequent annealing contributes to achieving the well-balanced strength and plasticity of titanium alloys. A high-throughput experimental method, wedge-shaped hot-r...
In recent years, the use of recycled aggregate (RA) in roller-compacted concrete (RCC) for pavement construction has been increasingly attractive due to various environmental and economic benefits. Early determination of the compressive strength (CS)...
Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacemen...
Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variabl...
Pinus massoniana is a pioneer reforestation tree species in China. It is crucial to evaluate the seedling vigor of Pinus massoniana for reforestation work, and leaf moisture and nitrogen content are key factors used to achieve it. In this paper, we p...
The generation of concentration gradients is an essential part of a wide range of laboratory settings. However, the task usually requires tedious and repetitive steps and it is difficult to generate concentration gradients at once. Here, we present a...
Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently, this fact is not considered either in laboratory experiments or field pol...
Reliable characterization of the upper and lower flammability limits of methane–hydrogen mixtures is imperative for the safe application of hydrogen-enriched fuel systems. However, experimental data under elevated-temperature and elevated-press...
The synthetic bandwidth technique is an effective method to achieve ultra-high range resolution in an SAR system. There are mainly two challenges in its implementation. The first one is the estimation and compensation of system errors, such as the ti...
Biochar, a carbon-rich material produced through the pyrolysis of wood residues and agricultural byproducts, has carbon storage capacity and potential as a low-carbon construction material. This study predicts the compressive strength of cementitious...
Heart rate variability (HRV) changes nonlinearly across the lifespan, and such nonlinearity is often taken to require machine learning (ML). Distinguishing explanatory from predictive modeling, we tested three propositions using 24 h Holter recording...
The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses, we introduce variational character...
With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing...
Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational prin...
In this study, a machine learning-based quality control algorithm for heavy rainfall was developed by integrating automatic weather station observations with remote sensing data, minute-level data, and metadata. Based on heavy rainfall samples from 1...
To address the need for massive connections in Internet-of-Vehicle communications, local wireless networks utilize non-orthogonal multiple access (NOMA). Scholars have introduced deep reinforcement learning networks for user grouping and power alloca...
This paper presents a novel semi-supervised joint dictionary learning (S2JDL) algorithm for hyperspectral image classification. The algorithm jointly minimizes the reconstruction and classification error by optimizing a semi-supervised dictionary lea...
We developed Variational Laplace for Bayesian neural networks (BNNs), which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational...
Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its per...
The strengthening of reinforced concrete (RC) beams with aluminum alloy was typically implemented in a symmetrical configuration. To evaluate the flexural performance of strengthened beams, four machine learning (ML)-based models, namely Random Fores...
In recent years, video recognition models have witnessed the rapid development of Deep Neural Networks (DNNs). However, these models remain not robust to adversarial examples that are created by adding imperceptible perturbations to clean samples. Re...
Deep learning (DL) techniques have significantly advanced wind power forecasting by enhancing accuracy. However, these DL models are vulnerable to adversarial attacks, which can lead to severely inaccurate forecasts. Existing studies in wind power fo...
Soil organic carbon (SOC) is an important indicator of plantation-forest soil quality, but Vis-NIR models calibrated at one site may show systematic bias at another. This study used 370 Gaofeng samples as the source domain and 119 Yachang samples as...
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