It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the funct...
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation w...
The rotation error is the most important quality characteristic index of a rotate vector (RV) reducer, and it is difficult to accurately optimize the design of a RV reducer, such as the Taguchi type, due to the many factors affecting the rotation err...
Accurate lens distortion correction is important for calibration, registration, image stitching, and 3D reconstruction, especially in low-data device-specific settings where disposable or specialised cameras cannot provide large calibration datasets....
Accurate forecasting of ratio and phase errors of 10 kV current transformers (CTs) is difficult under distorted and time-varying conditions due to mixed harmonics, ambient-driven drifts, and limited reference-grade field data. This paper proposes a r...
Despite numerous works over the past two decades, friction-induced vibrations, especially braking noises, are a major issue for transportation manufacturers as well as for the scientific community. To study these fugitive phenomena, the engineers nee...
Robotic manipulation of objects with diverse shapes, sizes, and properties, especially deformable ones, remains a significant challenge in automation, necessitating human-like dexterity through the integration of perception, learning, and control. Th...
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for prognostics and health management. However, standard Gaussian processes (GPs) face challenges in scalability and capturing complex global degradation tre...
In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian...
Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and b...
With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor pos...
The discriminability and transferability of models are two important factors for the success of domain adaptation methods. Recently, some domain adaptation methods have improved models by adding a discriminant information extraction module. However,...
China has vast amounts of deep coalbed methane resources but is still in the early stage of deep coalbed methane development; thus, it lacks mature gas exploitation and development theories and technologies, particularly effective methods for evaluat...
Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framewo...
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep...
Otosclerosis is a common middle ear disease that requires a combination of examinations for its diagnosis in routine. In a previous study, we showed that this disease could be potentially diagnosed by wideband tympanometry (WBT) coupled with a convol...
Sound signals generated during rock failure contain useful information about crack development. A sound-signal-based identification method for crack types is proposed. In this method, the sound signals of tensile cracks, using the Brazilian splitting...
Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of s...
Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yie...
Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume...
Atmospheric correction is the processes of converting radiance values measured at a spectral sensor to the reflectance values of the materials in a multispectral or hyperspectral image. This is an important step for detecting or identifying the mater...
This work investigates how surrogate-model behavior changes with increasing input dimensionality, focusing on Gaussian process regression (GPR), polynomial chaos expansion (PCE), and deep neural networks (DNNs). The central objective is to find out h...
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However...
Groundwater in Zhengzhou has experienced substantial changes under the combined effects of long-term abstraction, water-source substitution by the South-to-North Water Diversion Project, and ecological replenishment. During the 13th Five-Year Plan pe...
Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important informa...
Screw piles (often referred to as helical piles) are widely used to resist axial and lateral loads as deep foundations. Multi-helix piles experience complex interactions between the plates which depend on the soil properties, pile stiffness, helix di...
Accurate prediction of reservoir temperature is critical for optimizing geothermal energy systems, yet the complexity of geothermal data poses significant challenges for traditional modeling approaches. This study conducts a comprehensive comparative...
Vertical hydraulic-fracture propagation controls the effective stimulated thickness of deep layered shale, yet conventional fracability indices omit this response. Coupled hydromechanical cohesive-zone models were built for nine sublayers in the nort...
There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards...
Tornado damage estimation is important for providing insights into tornado studies and assisting rapid disaster response. However, it is challenging to precisely estimate tornado damage because of the large volumes of perishable data. This study pres...
The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is nece...
This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable...
In this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build...
In recent years, unmanned aerial vehicles (UAVs) have shown substantial application value in continuous target tracking tasks in complex environments. Due to the target’s movement behavior and the complexities of the surrounding environment, th...
Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algo...
Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely u...
Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous veh...
In clinical practice, fracture age estimation is commonly required, particularly in children with suspected non-accidental injuries. It is usually done by radiologically examining the injured body part and analyzing several indicators of fracture hea...
A precise microclimate control for dynamic climate changes in greenhouses allows the industry and researchers to develop a simple, robust, reliable, and intelligent model. Accordingly, the objective of this investigation was to develop a method that...
Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), es...
Recently, AI has been used in industry for very precise quality control of various products or in the automation of production processes through the use of trained artificial neural networks (ANNs) which allow us to completely replace a human in ofte...
Structural health monitoring (SHM) of additively manufactured (AM) small and complex components is investigated using a sensor-based signal processing and machine-learning framework. Guided-wave responses acquired from piezoelectric transducers are a...
Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic s...
Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species and electromagnetic emissions generated through gas plasma jets have attracted significant attention due to their s...
This study investigates the effectiveness of Bayesian probabilistic methods for stock price forecasting on the Johannesburg Stock Exchange by implementing and comparing Gaussian process regression (GPR), Bayesian long short-term memory (Bayesian LSTM...
Reliability assessments of subsea systems are generally performed at two levels: structural reliability analysis of individual components and functional reliability analysis of the overall system using generic failure-rate databases. This study devel...
Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for the process data with unknown mode, traditional clustering methods can hardly identify the number of modes automatically. Furth...
Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM stu...
The material removal rate (MRR) is an important variable but difficult to measure in the chemical–mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in...
The umbilical cable is an important component of the deep-sea mining system, serving as the sole connection between the surface support vessel and the seabed mining system. The harsh marine environment poses significant challenges to umbilical cable...
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