The Inverse Gaussian process is a useful stochastic process to model the monotonous degradation process of a certain component. Owing to the phenomenon that the degradation processes often exhibit multi-stage characteristics because of the internal d...
In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision....
Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with...
Reliability estimation for key components of a mechanical system is of great importance in prognosis and health management in aviation industry. Both degradation data and failure time data contain abundant reliability information from different sourc...
Support load fluctuations in lithographic objectives can induce additional surface figure errors in lenses. Conventional Monte Carlo-based tolerance analysis is computationally expensive in high-dimensional load spaces, and surrogate models based onl...
Degradation data plays a crucial role in the reliability assessment and condition monitoring of engineering systems. The stage-wise changes in degradation rates often signal turning points in system performance or potential fault risks. To address th...
In manufacturing and service industries, monitoring processes with correlated input variables and inverse Gaussian (IG)-distributed quality characteristics is challenging due to the limitations of maximum likelihood estimator (MLE)-based control char...
In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the...
Markov chain Monte Carlo (MCMC) stands out as an effective method for tackling Bayesian inverse problems. However, when dealing with computationally expensive forward models and high-dimensional parameter spaces, the challenge of repeated sampling be...
The acquisition of geophysical data is becoming increasingly important in the context of critical mineral exploration. Geophysical data and inversion products are essential to map many components of the critical mineral system by detecting geophysica...
This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming a...
This paper mainly considers the parameter estimation problem for several types of differential equations controlled by linear operators, which may be partial differential, integro-differential and fractional order operators. Under the idea of data-dr...
Tool wear directly affects the accuracy and reliability of machined workpieces; accurately predicting wear trends is crucial for developing reasonable tool-change strategies and avoiding sudden tool failure. Traditional parametric Inverse Gaussian (I...
Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal fi...
We model the logarithm of the spot price of electricity with a normal inverse Gaussian (NIG) process and the wind speed and wind power production with two Ornstein–Uhlenbeck processes. In order to reproduce the correlation between the spot pric...
Reliable mechanical parameters are needed to predict deformation during staged slope excavation, yet deep inclinometer profiles are high-dimensional and repeated numerical inversion is costly. This study integrates principal component analysis, Gauss...
Accurate prediction and inversion of time-dependent gap deformation between a passenger aircraft door and its frame remain difficult under slowly varying pressurization because the response is governed by coupled geometry, lock restraint, and nonline...
When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameter...
Analytical micromechanical schemes predict the effective stiffness of two-phase composites accurately and cheaply, yet they are opaque to the influence of individual descriptors, are not analytically invertible for target-driven design, and grow oner...
There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpr...
This study presents a unified finite element and surrogate-assisted computational framework for comparative analysis and design exploration of sheet-based triply periodic minimal surface (TPMS) and Re-entrant auxetic lattice architectures. Four TPMS...
The helicopter tail drive shaft is vulnerable to failure from projectile impacts during low-altitude flight. Stress wave-based inversion of impact parameters and quantitative damage assessment remain insufficiently explored. To address small-sample a...
The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis. Regression functions for the process and threshold parameters in these mode...
In this work, we have analyzed data sets from various fields using a coupled Ornstein–Uhlenbeck (OU) system of equations driven by Lévy processes. The Ornstein–Uhlenbeck model is well known for its ability to capture stochastic behaviors when used as...
The aim of the paper is to propose, and give an example of, a strategy for managing insurance risk in continuous time to protect a portfolio of non-life insurance contracts against unwelcome surplus fluctuations. The strategy combines the characteris...
Optimization algorithms have been successfully applied to the automatic design of analog integrated circuits. However, many of the existing solutions rely on expensive circuit simulations or use fully customized surrogate models for each particular c...
Sensor networks, which are increasingly being used in a broad range of applications, constitute a measurement paradigm involving ensembles of sensors measuring possibly different quantities at a discrete sample of spatial locations and temporal point...
Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded phys...
The COVID-19 pandemic has highlighted the need for finding mathematical models to forecast the evolution of the contagious disease and evaluate the success of particular policies in reducing infections. In this work, we perform Bayesian inference for...
The step-stress accelerated degradation test (ADT) provides a feasible method for assessing the storage life of high-reliability, long-life products. However, this method results in a slower rate of performance degradation at the beginning of the tes...
I illustrate an approach that can be exploited for constructing neural networks that a priori obey physical laws. We start with a simple single-layer neural network (NN) but refrain from choosing the activation functions yet. Under certain conditions...
The seismic data inversion from observations contaminated by spurious measures (outliers) remains a significant challenge for the industrial and scientific communities. This difficulty is due to slow processing work to mitigate the influence of the o...
Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data...
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price mo...
A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module...
Superstatistical approaches have played a crucial role in the investigations of mixtures of Gaussian processes. Such approaches look to describe non-Gaussian diffusion emergence in single-particle tracking experiments realized in soft and biological...
Data-centric inverse problems are a process of inferring physical attributes from indirect measurements. Full-waveform inversion (FWI) is a non-linear inverse problem that attempts to obtain a quantitative physical model by comparing the wave equatio...
The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired...
The Tsallis q-Gaussian distribution is a powerful generalization of the standard Gaussian distribution and is commonly used in various fields, including non-extensive statistical mechanics, financial markets and image processing. It belongs to the q-...
The monitoring and forecasting of particulate matter (e.g.,
This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets...
This paper presents a framework for inverse modeling of microwave filters based on a conditional diffusion model developed to address the intrinsic non-uniqueness of reconstructing coupling matrices from specified S-parameter responses. In the forwar...
Data clustering is one area of data mining that falls into the data mining class of unsupervised learning. Cluster analysis divides data into different classes by discovering the internal structure of data set objects and their relationship. This pap...
It has previously been shown that prior physics knowledge can be incorporated into the structure of an artificial neural network via neural activation functions based on (i) the correspondence under the infinite-width limit between neural networks an...
This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inve...
Analogous to Kolmogorov’s theorem for the existence of stochastic processes describing random functions, we consider theorems for the existence of stochastic processes describing random measures as limits of inverse measure systems. Specificall...
A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and me...
Important processes within the shipbuilding and ship repair industry include metal cutting, welding, surface preparation, and painting. When dealing with ship repair, ships are brought into drydocks to carry out necessary repairs. Typical repairs inc...
Electrospinning is a versatile technique for producing polymer nanofibers whose morphology strongly influences their properties. This work developed predictive and inferential models for fiber diameter based on solution and process parameters. Polyme...
In the complex underwater environment, it is hard to obtain accurate system noise prior information. If uncertainty system noise model is used in state determination, the precision will decrease. To address the problem, this paper proposes a novel in...
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