scholarly journals Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

2021 ◽  
Author(s):  
Linh Nguyen ◽  
Karthick Thiyagarajan ◽  
Nalika Ulapane ◽  
sarath kodagoda
Keyword(s):  
Random Fields ◽  
Real Life ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Sensor Nodes ◽  
Approximate Algorithm ◽  
Selection Problem ◽  
Cross Covariance ◽  
Efficient Prediction ◽  
Selection For

The paper addresses the multimodal sensor selection problem where selected collocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matern cross-covariance function, cross covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.

scholarly journals Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

2021 ◽  
Author(s):  
Linh Nguyen ◽  
Karthick Thiyagarajan ◽  
Nalika Ulapane ◽  
sarath kodagoda
Keyword(s):  
Random Fields ◽  
Real Life ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Sensor Nodes ◽  
Approximate Algorithm ◽  
Selection Problem ◽  
Cross Covariance ◽  
Efficient Prediction ◽  
Selection For

The paper addresses the multimodal sensor selection problem where selected collocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matern cross-covariance function, cross covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.

scholarly journals Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Random Fields ◽  
Covariance Function ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Spatial Processes ◽  
Selection Problems ◽  
Cross Covariance ◽  
Field Estimation ◽  
Spatial Fields ◽  
Selection For

<pre>The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat\'ern cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding \textit{univariate} and \textit{multivariate sensor selection} problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.</pre>

scholarly journals Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Random Fields ◽  
Covariance Function ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Spatial Processes ◽  
Selection Problems ◽  
Cross Covariance ◽  
Field Estimation ◽  
Spatial Fields ◽  
Selection For

<pre>The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat\'ern cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding \textit{univariate} and \textit{multivariate sensor selection} problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.</pre>

scholarly journals Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

2021 ◽  
Author(s):  
Linh Nguyen ◽  
Karthick Thiyagarajan ◽  
Nalika Ulapane ◽  
sarath kodagoda
Keyword(s):  
Random Fields ◽  
Covariance Function ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Spatial Processes ◽  
Selection Problems ◽  
Cross Covariance ◽  
Field Estimation ◽  
Spatial Fields ◽  
Selection For

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat´ern cross covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

scholarly journals Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

2021 ◽  
Author(s):  
Linh Nguyen ◽  
Karthick Thiyagarajan ◽  
Nalika Ulapane ◽  
sarath kodagoda
Keyword(s):  
Random Fields ◽  
Covariance Function ◽  
Covariance Matrices ◽  
Sensor Selection ◽  
Spatial Processes ◽  
Selection Problems ◽  
Cross Covariance ◽  
Field Estimation ◽  
Spatial Fields ◽  
Selection For

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat´ern cross covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

On Laplacian energy of picture fuzzy graphs in site selection problem

2021 ◽  
pp. 1-18
Author(s):  
Mahima Poonia ◽  
Rakesh Kumar Bajaj
Keyword(s):  
Decision Making ◽  
Site Selection ◽  
Upper Bounds ◽  
Selection Problem ◽  
Hydropower Plant ◽  
Fuzzy Graph ◽  
Fuzzy Information ◽  
Fuzzy Graphs ◽  
Laplacian Energy ◽  
Selection For

In the present work, the adjacency matrix, the energy and the Laplacian energy for a picture fuzzy graph/directed graph have been introduced along with their lower and the upper bounds. Further, in the selection problem of decision making, a methodology for the ranking of the available alternatives has been presented by utilizing the picture fuzzy graph and its energy/Laplacian energy. For the shake of demonstrating the implementation of the introduced methodology, the task of site selection for the hydropower plant has been carried out as an application. The originality of the introduced approach, comparative remarks, advantageous features and limitations have also been studied in contrast with intuitionistic fuzzy and Pythagorean fuzzy information.

Matérn Cross-Covariance Functions for Multivariate Random Fields

2010 ◽  
Vol 105 (491) ◽  
pp. 1167-1177 ◽  
Author(s):  
Tilmann Gneiting ◽  
William Kleiber ◽  
Martin Schlather
Keyword(s):  
Random Fields ◽  
Covariance Functions ◽  
Cross Covariance
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