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3,592 Results Found

  • Article
  • Open Access
15 Citations
3,716 Views
21 Pages

Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking

  • Zhiyuan Yang,
  • Xiangqian Li,
  • Xianxun Yao,
  • Jinping Sun and
  • Tao Shan

21 June 2023

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...

  • Technical Note
  • Open Access
8 Citations
4,289 Views
15 Pages

21 January 2023

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...

  • Article
  • Open Access
5 Citations
4,471 Views
22 Pages

28 February 2022

Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas pr...

  • Article
  • Open Access
6 Citations
4,072 Views
26 Pages

Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems

  • Yiwei Liao,
  • Jiangqiong Xie,
  • Zhiguo Wang and
  • Xiaojing Shen

16 November 2019

The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability. Th...

  • Article
  • Open Access
10 Citations
2,812 Views
19 Pages

Decoherence Effects in a Three-Level System under Gaussian Process

  • Sultan M. Zangi,
  • Atta ur Rahman,
  • Zhao-Xo Ji,
  • Hazrat Ali and
  • Huan-Guo Zhang

23 November 2022

When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is in...

  • Article
  • Open Access
8 Citations
4,466 Views
18 Pages

A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring

  • Peng Wang,
  • Lyudmila Mihaylova,
  • Rohit Chakraborty,
  • Said Munir,
  • Martin Mayfield,
  • Khan Alam,
  • Muhammad Fahim Khokhar,
  • Zhengkai Zheng,
  • Chengxi Jiang and
  • Hui Fang

14 October 2021

The monitoring and forecasting of particulate matter (e.g., PM2.5) and gaseous pollutants (e.g., NO, NO2, and SO2) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data n...

  • Article
  • Open Access
4 Citations
1,815 Views
10 Pages

In this Article, we reconstruct the growth and evolution of the cosmic structure of the Universe using Markov chain Monte Carlo algorithms for Gaussian processes. We estimate the difference between the reconstructions that are calculated through a ma...

  • Article
  • Open Access
6 Citations
4,498 Views
12 Pages

21 November 2018

This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the...

  • Article
  • Open Access
4 Citations
3,363 Views
26 Pages

10 June 2022

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...

  • Article
  • Open Access
42 Citations
7,382 Views
20 Pages

22 June 2018

Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profit...

  • Article
  • Open Access
1 Citations
1,081 Views
16 Pages

18 February 2025

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...

  • Article
  • Open Access
5 Citations
2,943 Views
20 Pages

Gaussian Process Modeling of Specular Multipath Components

  • Anh Hong Nguyen,
  • Michael Rath,
  • Erik Leitinger,
  • Khang Van Nguyen and
  • Klaus Witrisal

29 July 2020

The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Proc...

  • Article
  • Open Access
4 Citations
2,733 Views
18 Pages

19 November 2023

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,...

  • Article
  • Open Access
2,453 Views
27 Pages

Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve a...

  • Article
  • Open Access
3 Citations
2,446 Views
18 Pages

Multi-Criterion Sampling Matting Algorithm via Gaussian Process

  • Yuan Yang,
  • Hongshan Gou,
  • Mian Tan,
  • Fujian Feng,
  • Yihui Liang,
  • Yi Xiang,
  • Lin Wang and
  • Han Huang

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results...

  • Article
  • Open Access
29 Citations
5,205 Views
18 Pages

21 October 2020

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that c...

  • Article
  • Open Access
1 Citations
1,283 Views
25 Pages

17 July 2025

This study introduces an intrinsic Gaussian Process Regression (iGPR) model for the first time, which incorporates non-Euclidean spatial covariates via a Gaussian process prior to analyzing the relationship between digitalization and carbon emission...

  • Feature Paper
  • Article
  • Open Access
2 Citations
3,159 Views
17 Pages

22 January 2025

This study introduces a novel Gaussian process (GP) regression framework that probabilistically enforces physical constraints, with a particular focus on equality conditions. The GP model is trained using the quantum-inspired Hamiltonian Monte Carlo...

  • Article
  • Open Access
26 Citations
5,860 Views
15 Pages

Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling tech...

  • Article
  • Open Access
1 Citations
1,114 Views
17 Pages

In this study, Gaussian process (GP) regression is used to normalize observed commodity data and produce predictions at densely interpolated time intervals. The methodology is applied to an empirical oil price dataset. A Gaussian kernel with data-dep...

  • Article
  • Open Access
5 Citations
3,776 Views
21 Pages

30 September 2019

Dimensionality Reduction (DR) models are highly useful for tackling Hyperspectral Images (HSIs) classification tasks. They mainly address two issues: the curse of dimensionality with respect to spectral features, and the limited number of labeled tra...

  • Article
  • Open Access
8 Citations
3,693 Views
17 Pages

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on...

  • Article
  • Open Access
38 Citations
5,397 Views
19 Pages

11 November 2020

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...

  • Feature Paper
  • Article
  • Open Access
207 Views
25 Pages

18 January 2026

High-dimensional surrogate modeling with limited high-fidelity data poses a major challenge in uncertainty quantification. Classical supervised dimension reduction methods often fail in this setting due to insufficient accurate observations, while lo...

  • Article
  • Open Access
2 Citations
1,755 Views
25 Pages

6 June 2023

As a reasonable statistical learning model for curve clustering analysis, the two-layer mixtures of Gaussian process functional regressions (TMGPFR) model has been developed to fit the data of sample curves from a number of independent information so...

  • Article
  • Open Access
38 Citations
8,820 Views
29 Pages

8 September 2015

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area netwo...

  • Article
  • Open Access
7 Citations
3,529 Views
17 Pages

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

  • Xianjian Jin,
  • Hang Yang,
  • Xin Liao,
  • Zeyuan Yan,
  • Qikang Wang,
  • Zhiwei Li and
  • Zhaoran Wang

23 June 2022

Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground seg...

  • Article
  • Open Access
4 Citations
2,590 Views
27 Pages

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation

  • Soojeong Lee,
  • Gyanendra Prasad Joshi,
  • Chang-Hwan Son and
  • Gangseong Lee

15 February 2023

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodolo...

  • Article
  • Open Access
14 Citations
6,329 Views
16 Pages

27 January 2020

Specialized Gaussian process regression is presented for data that are known to fulfill a given linear differential equation with vanishing or localized sources. The method allows estimation of system parameters as well as strength and location of po...

  • Article
  • Open Access
16 Citations
6,226 Views
15 Pages

20 May 2017

By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issu...

  • Article
  • Open Access
4 Citations
3,168 Views
13 Pages

Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation

  • Simon Verspeek,
  • Ivan De Boi,
  • Xavier Maldague,
  • Rudi Penne and
  • Gunther Steenackers

22 March 2022

We address the challenge of determining a valid set of parameters for a dynamic line scan thermography setup. Traditionally, this optimization process is labor- and time-intensive work, even for an expert skilled in the art. Nowadays, simulations in...

  • Article
  • Open Access
1 Citations
4,161 Views
10 Pages

Inventory Model with Stochastic Demand Using Single-Period Inventory Model and Gaussian Process

  • Jose Mejia,
  • Liliana Avelar-Sosa,
  • Boris Mederos and
  • Jorge L. García-Alcaraz

16 April 2022

Proper inventory management is vital to achieving sustainability within a supply chain and is also related to a company’s cash flow through the funds represented by the inventory. Therefore, it is necessary to balance excess inventory and insuf...

  • Article
  • Open Access
6 Citations
5,066 Views
21 Pages

9 March 2021

A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource...

  • Article
  • Open Access
22 Citations
6,067 Views
25 Pages

Gaussian-Process-Based Surrogate for Optimization-Aided and Process-Variations-Aware Analog Circuit Design

  • Adriana C. Sanabria-Borbón,
  • Sergio Soto-Aguilar,
  • Johan J. Estrada-López,
  • Douglas Allaire and
  • Edgar Sánchez-Sinencio

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...

  • Article
  • Open Access
3 Citations
6,391 Views
15 Pages

Multivariate Interpolation of Wind Field Based on Gaussian Process Regression

  • Miao Feng,
  • Weimin Zhang,
  • Xiangru Zhu,
  • Boheng Duan,
  • Mengbin Zhu and
  • De Xing

The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the...

  • Article
  • Open Access
31 Citations
4,451 Views
15 Pages

Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

  • Mahmood Ahmad,
  • Suraparb Keawsawasvong,
  • Mohd Rasdan Bin Ibrahim,
  • Muhammad Waseem,
  • Kazem Reza Kashyzadeh and
  • Mohanad Muayad Sabri Sabri

18 July 2022

In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process...

  • Article
  • Open Access
33 Citations
3,465 Views
14 Pages

17 October 2021

The system identification of a ship dynamics model is crucial for the intelligent navigation and design of the ship’s controller. The fluid dynamic effect and the complicated geometry of the hull surface cause a nonlinear or asymmetrical behavior, an...

  • Article
  • Open Access
1 Citations
3,208 Views
14 Pages

25 March 2022

Residual stress is closely related to the evolution process of the component fatigue state, but it can be affected by various sources. Conventional fatigue evaluation either focuses on the physical process, which is limited by the complexity of the p...

  • Article
  • Open Access
5,133 Views
17 Pages

22 December 2014

We prove a large deviation principle for a stationary Gaussian process over Rb,indexed by Ζd (for some positive integers d and b), with positive definite spectral density, andprovide an expression of the corresponding rate function in terms of the me...

  • Article
  • Open Access
12 Citations
3,025 Views
23 Pages

Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

  • Hanany Tolba,
  • Nouha Dkhili,
  • Julien Nou,
  • Julien Eynard,
  • Stéphane Thil and
  • Stéphane Grieu

13 August 2020

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse...

  • Article
  • Open Access
3 Citations
1,714 Views
23 Pages

14 November 2024

Aiming at the uncertainty of target motion and observation models in multi-maneuvering target tracking (MMTT), this study presents an innovative data-driven approach based on a Gaussian process (GP). Traditional multi-model (MM) methods rely on a pre...

  • Article
  • Open Access
659 Views
21 Pages

Improving Avatar Accuracy with Gaussian Process Regression Method in Mirror Metaverses

  • Mai Cong Huong,
  • Artem Volkov,
  • Ammar Muthanna,
  • Andrey Koucheryavy,
  • Dmitry Kozyrev and
  • János Sztrik

11 December 2025

This paper deals with unwanted spatial distortion in virtual environments and its impact on the construction of metaverse environments that require high precision, especially in fields with specific requirements, such as medicine. At the same time, i...

  • Article
  • Open Access
64 Citations
5,684 Views
20 Pages

Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression

  • Yu Peng,
  • Yandong Hou,
  • Yuchen Song,
  • Jingyue Pang and
  • Datong Liu

1 June 2018

The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance. The methods based on the physical models cannot perform satisfactorily as the systems...

  • Article
  • Open Access
13 Citations
4,438 Views
21 Pages

Predicting the Ultimate Tensile Strength of Friction Stir Welds Using Gaussian Process Regression

  • Roman Hartl,
  • Fabian Vieltorf,
  • Maximilian Benker and
  • Michael F. Zaeh

In the work described here, Gaussian process regression was applied to predict the ultimate tensile strength of friction stir welds through data evaluation and to therefore avoid destructive testing. For data generation, a total of 54 welding experim...

  • Article
  • Open Access
374 Views
17 Pages

Gaussian Process Modeling of EDM Performance Using a Taguchi Design

  • Dragan Rodić,
  • Milenko Sekulić,
  • Borislav Savković,
  • Anđelko Aleksić,
  • Aleksandra Kosanović and
  • Vladislav Blagojević

1 January 2026

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when...

  • Article
  • Open Access
16 Citations
5,901 Views
17 Pages

29 July 2016

Location data are among the most widely used context data in context-aware and ubiquitous computing applications. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning....

  • Feature Paper
  • Article
  • Open Access
256 Views
23 Pages

30 December 2025

Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these models hav...

  • Article
  • Open Access
6 Citations
2,164 Views
22 Pages

17 September 2022

The evaluation of rockburst damage potential plays a significant role in managing rockburst risk and guaranteeing the safety of personnel. However, it is still a challenging problem because of its complex mechanisms and numerous influencing factors....

  • Article
  • Open Access
5 Citations
3,858 Views
14 Pages

5 December 2020

Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test poi...

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