PROJECT TITLE :

Manifold Regression Framework for Characterizing Source Zone Architecture

ABSTRACT:

During this paper, we develop machine learning approaches for estimating quantitative features (or metrics) characterizing subsurface zones of chemical contamination, focusing on issues involving dense nonaqueous-section liquid (DNAPL). Source zone characterization, a necessary 1st step in the development of a remediation strategy, is difficult due to practical constraints related to the information out there for processing. Our ways specialize in the employment of manifold regression techniques for estimating supply zone metrics connected to the distribution of contaminant mass in highly saturated pool regions, with more diffuse ganglia regions, based mostly on downgradient measurements of dissolved contaminant concentration at a outlined time. We have a tendency to use manifold ways for jointly representing labeled coaching knowledge composed of known supply zone metrics, also features derived from the corresponding dissolved concentration knowledge sets. We tend to then propose a new integrated approach to the problems of one) robustly embedding test data (downgradient dissolved concentration) into the manifold when the source zone metrics aren't out there and 2) constructing a regression operate operating directly within the manifold space for source zone metric estimation. The utility of the approach is enhanced by the express incorporation of physical constraints associated with the metrics into the matter formulation. Results primarily based upon simulated knowledge demonstrate the potential effectiveness of the manifold regression approaches, and significant improvement in performance relative to the case where the algorithmic parts are designed serially.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources - 2018ABSTRACT:By using the manifold separation technique (MST), we develop a computationally efficient nonetheless
PROJECT TITLE :A Manifold Alignment Approach for Hyperspectral Image Visualization With Natural ColorABSTRACT:The trichromatic visualization of tons of bands in a very hyperspectral image (HSI) has been a full of life research
PROJECT TITLE :Multimodal Manifold Analysis by Simultaneous Diagonalization of LaplaciansABSTRACT:We have a tendency to construct an extension of spectral and diffusion geometry to multiple modalities through simultaneous diagonalization
PROJECT TITLE :Feature Extraction for Hyperspectral Imagery via Ensemble Localized Manifold LearningABSTRACT:A feature extraction approach for hyperspectral image classification has been developed. Multiple linear manifolds are
PROJECT TITLE :5G Multi-RAT LTE-WiFi Ultra-Dense Small Cells: Performance Dynamics, Architecture, and TrendsABSTRACT:The ongoing densification of little cells yields an unprecedented paradigm shift in user expertise and network

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry