scholarly journals Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

2021 ◽  
Vol 13 (9) ◽  
pp. 225
Author(s):  
Taghreed Alghamdi ◽  
Khalid Elgazzar ◽  
Taysseer Sharaf
Keyword(s):  
Gaussian Process ◽  
Bayesian Modeling ◽  
Spatiotemporal Analysis ◽  
Ar Model ◽  
Hierarchical Bayesian ◽  
Accuracy And Precision ◽  
Modeling Data ◽  
Predictive Processes ◽  
Gp Model

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

2012 ◽  
Vol 512-515 ◽  
pp. 803-808
Author(s):  
Ji Long Tong ◽  
Zeng Bao Zhao ◽  
Wen Yu Zhang
Keyword(s):  
Wind Speed ◽  
Original Data ◽  
Ar Model ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Prediction Time ◽  
New Strategy ◽  
Speed Prediction ◽  
Auto Regression

This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.

scholarly journals Molecular Epidemiology of Tuberculosis: Current Insights

2006 ◽  
Vol 19 (4) ◽  
pp. 658-685 ◽  
Author(s):  
Barun Mathema ◽  
Natalia E. Kurepina ◽  
Pablo J. Bifani ◽  
Barry N. Kreiswirth
Keyword(s):  
Vaccine Development ◽  
Epidemiologic Studies ◽  
Molecular Techniques ◽  
Tb Control ◽  
Molecular Approaches ◽  
Accuracy And Precision ◽  
Outbreak Investigations ◽  
Short And Long Term ◽  
Exogenous Reinfection

SUMMARY Molecular epidemiologic studies of tuberculosis (TB) have focused largely on utilizing molecular techniques to address short- and long-term epidemiologic questions, such as in outbreak investigations and in assessing the global dissemination of strains, respectively. This is done primarily by examining the extent of genetic diversity of clinical strains of Mycobacterium tuberculosis. When molecular methods are used in conjunction with classical epidemiology, their utility for TB control has been realized. For instance, molecular epidemiologic studies have added much-needed accuracy and precision in describing transmission dynamics, and they have facilitated investigation of previously unresolved issues, such as estimates of recent-versus-reactive disease and the extent of exogenous reinfection. In addition, there is mounting evidence to suggest that specific strains of M. tuberculosis belonging to discrete phylogenetic clusters (lineages) may differ in virulence, pathogenesis, and epidemiologic characteristics, all of which may significantly impact TB control and vaccine development strategies. Here, we review the current methods, concepts, and applications of molecular approaches used to better understand the epidemiology of TB.

scholarly journals A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1492
Author(s):  
Hanita Daud ◽  
Muhammad Naeim Mohd Aris ◽  
Khairul Arifin Mohd Noh ◽  
Sarat Chandra Dass
Keyword(s):  
Gaussian Process ◽  
Inverse Modeling ◽  
Mean Squared Error ◽  
Computational Effort ◽  
Percentage Error ◽  
Depth Prediction ◽  
Receiver System ◽  
Realistic Representation ◽  
And Performance ◽  
Gp Model

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 the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

Combustor Wall Surface Temperature and Heat Flux Measurement Using a Fiber-Coupled Long Wave Infrared Hyperspectral Sensor

2021 ◽  
Author(s):  
Aravind Chandh ◽  
Oleksandr Bibik ◽  
Subodh Adhikari ◽  
David Wu ◽  
Tim Lieuwen ◽  
...  
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Flux Measurement ◽  
Heat Fluxes ◽  
Long Wavelength ◽  
Combustion Gases ◽  
Acoustic Perturbation ◽  
Accuracy And Precision ◽  
Long Wavelength Infrared

Abstract In this paper, we discuss the development of a non-intrusive surface temperature sensor based on long-wavelength infrared (LWIR) hyperspectral technology. The LWIR detection enables to minimize optical interferences from hot combustion gases (emission mostly within UV-MWIR region). Utilization of hyperspectral detection allows to further improve temperature measurement accuracy and precision. The developed sensor with fiber coupling provides the required flexibility to be maneuvered around/through combustor hardware. The LWIR fiber probe is fully protected by the custom-designed water-cooled probe housing. This device is designed to sustain temperature of 2400 K at pressure of 50 bar, which enables long-term optical diagnostics inside the practical high-pressure combustion facilities where extreme thermal acoustic perturbation and intense heat fluxes are present. The housing featured a diamond window to selectively measure spectra in the LWIR region to get accurate surface temperature exclusively of the combustor wall. The probe was installed into a RQL style combustor to get surface temperature of both hot and cold side of the combustor wall. Further, pointwise heat flux estimates across the combustion liner wall was derived using the temperature measurements.

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