The Experts below are selected from a list of 287316 Experts worldwide ranked by ideXlab platform
Staf Roels - One of the best experts on this subject based on the ideXlab platform.
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Experimental Data Set for validation of heat air and moisture transport models of building envelopes
Building and Environment, 2011Co-Authors: Tadiwos Zerihun Desta, Jelle Langmans, Staf RoelsAbstract:Abstract This paper reports Experimental studies on heat, air and moisture (HAM) transfer through a full scale light weight building envelope wall under real atmospheric boundary conditions. The main objective of the article is to generate informative Data so that it can be used for numerical validation of HAM models. The considered wall is a multilayered structure built up from outside to inside of external board, vented cavity, fibreboard sheathing, mineral wool between wooden studs and interior finishing. The global wall has a surface area of (1.80 × 2.68) m2; and is subdivided into three vertical parts. The parts differ from each other by the applied interior finishing. Between the different layers of each part and on the surfaces of the wall humidity, temperature and heat flux sensors are placed in a 3D matrix. At the outer surface of the wall, the applied sheathing is a bituminous wood board. In the board nine removable specimens are included. By regularly weighing the fibreboard samples, their moisture content could be quantified. Using Data collected over a total time span of about two years, insight about the hygrothermal behaviour of the different envelope parts is obtained and at the same time a well-documented Data Set is generated that can be used for hygrothermal envelope model validation purposes.
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numerical and Experimental Data Set for benchmarking hygroscopic buffering models
International Journal of Heat and Mass Transfer, 2010Co-Authors: Chris James, Carey J Simonson, Prabal Talukdar, Staf RoelsAbstract:This paper presents Experimental Data measured in a bed of gypsum boards in the transient moisture transfer (TMT) facility at the University of Saskatchewan. The relative humidity and temperature were measured at two depths in a gypsum bed and the moisture accumulation was measured for the entire bed. Experiments were conducted for both coated (acrylic and latex paint) and uncoated gypsum. These Experimental Data are compared to simulated Data from eight different numerical models. The agreement between the Experimental and numerical Data is good and often within the Experimental uncertainty bounds. A sensitivity analysis was performed to show the influence of material properties such as sorption, vapour permeability and the transfer coefficients on the simulated results. One model examines hysteresis of the sorption isotherm.
Carey J Simonson - One of the best experts on this subject based on the ideXlab platform.
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numerical and Experimental Data Set for benchmarking hygroscopic buffering models
International Journal of Heat and Mass Transfer, 2010Co-Authors: Chris James, Carey J Simonson, Prabal Talukdar, Staf RoelsAbstract:This paper presents Experimental Data measured in a bed of gypsum boards in the transient moisture transfer (TMT) facility at the University of Saskatchewan. The relative humidity and temperature were measured at two depths in a gypsum bed and the moisture accumulation was measured for the entire bed. Experiments were conducted for both coated (acrylic and latex paint) and uncoated gypsum. These Experimental Data are compared to simulated Data from eight different numerical models. The agreement between the Experimental and numerical Data is good and often within the Experimental uncertainty bounds. A sensitivity analysis was performed to show the influence of material properties such as sorption, vapour permeability and the transfer coefficients on the simulated results. One model examines hysteresis of the sorption isotherm.
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an Experimental Data Set for benchmarking 1 d transient heat and moisture transfer models of hygroscopic building materials part ii Experimental numerical and analytical Data
International Journal of Heat and Mass Transfer, 2007Co-Authors: Prabal Talukdar, Olalekan F Osanyintola, Stephen O Olutimayin, Carey J SimonsonAbstract:This paper presents the Experimental results on spruce plywood and cellulose insulation using the transient moisture transfer (TMT) facility presented in Part I [P. Talukdar, S.O. Olutmayin, O.F. Osanyintola, C.J. Simonson, An Experimental Data Set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials-Part-I: Experimental facility and property Data, Int. J. Heat Mass Transfer, in press, doi:10.1016/j.ijheatmasstransfer.2007.03.026] of this paper. The temperature, relative humidity and moisture accumulation distributions within both materials are presented following different and repeated step changes in air humidity and different airflow Reynolds numbers above the materials. The Experimental Data are compared with numerical Data, numerical sensitivity studies and analytical solutions to increase the confidence in the Experimental Data Set.
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an Experimental Data Set for benchmarking 1 d transient heat and moisture transfer models of hygroscopic building materials part i Experimental facility and material property Data
International Journal of Heat and Mass Transfer, 2007Co-Authors: Prabal Talukdar, Stephen O Olutmayin, Olalekan F Osanyintola, Carey J SimonsonAbstract:As numerical models of heat and moisture transfer in porous building materials advance and numerical investigations increase in the literature, there remains a need for simple accurate and well-documented Experimental Data for model validation. The aim of this two part paper is to provide such Experimental Data for two hygroscopic building materials (cellulose insulation and spruce plywood) exposed to 1-D and transient boundary conditions. Part I of this paper describes the transient moisture transfer (TMT) facility used to generate the Experimental Data as well as the uncertainty and repeatability of the measured Data. The measured material properties are also presented to fully document the Experimental Data Set and permit its use by other researchers.
Frazer K Noble - One of the best experts on this subject based on the ideXlab platform.
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autonomous fingerprinting and large Experimental Data Set for visible light positioning
Sensors, 2021Co-Authors: Tyrel Glass, Fakhrul Alam, Mathew Legg, Frazer K NobleAbstract:This paper presents an autonomous method of collecting Data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large Set of Experimental Data. Received Signal Strength (RSS) Data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS Data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the Data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small Data Set is available for calibration and training. However, if a large Data Set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.
Prabal Talukdar - One of the best experts on this subject based on the ideXlab platform.
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numerical and Experimental Data Set for benchmarking hygroscopic buffering models
International Journal of Heat and Mass Transfer, 2010Co-Authors: Chris James, Carey J Simonson, Prabal Talukdar, Staf RoelsAbstract:This paper presents Experimental Data measured in a bed of gypsum boards in the transient moisture transfer (TMT) facility at the University of Saskatchewan. The relative humidity and temperature were measured at two depths in a gypsum bed and the moisture accumulation was measured for the entire bed. Experiments were conducted for both coated (acrylic and latex paint) and uncoated gypsum. These Experimental Data are compared to simulated Data from eight different numerical models. The agreement between the Experimental and numerical Data is good and often within the Experimental uncertainty bounds. A sensitivity analysis was performed to show the influence of material properties such as sorption, vapour permeability and the transfer coefficients on the simulated results. One model examines hysteresis of the sorption isotherm.
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an Experimental Data Set for benchmarking 1 d transient heat and moisture transfer models of hygroscopic building materials part ii Experimental numerical and analytical Data
International Journal of Heat and Mass Transfer, 2007Co-Authors: Prabal Talukdar, Olalekan F Osanyintola, Stephen O Olutimayin, Carey J SimonsonAbstract:This paper presents the Experimental results on spruce plywood and cellulose insulation using the transient moisture transfer (TMT) facility presented in Part I [P. Talukdar, S.O. Olutmayin, O.F. Osanyintola, C.J. Simonson, An Experimental Data Set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials-Part-I: Experimental facility and property Data, Int. J. Heat Mass Transfer, in press, doi:10.1016/j.ijheatmasstransfer.2007.03.026] of this paper. The temperature, relative humidity and moisture accumulation distributions within both materials are presented following different and repeated step changes in air humidity and different airflow Reynolds numbers above the materials. The Experimental Data are compared with numerical Data, numerical sensitivity studies and analytical solutions to increase the confidence in the Experimental Data Set.
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an Experimental Data Set for benchmarking 1 d transient heat and moisture transfer models of hygroscopic building materials part i Experimental facility and material property Data
International Journal of Heat and Mass Transfer, 2007Co-Authors: Prabal Talukdar, Stephen O Olutmayin, Olalekan F Osanyintola, Carey J SimonsonAbstract:As numerical models of heat and moisture transfer in porous building materials advance and numerical investigations increase in the literature, there remains a need for simple accurate and well-documented Experimental Data for model validation. The aim of this two part paper is to provide such Experimental Data for two hygroscopic building materials (cellulose insulation and spruce plywood) exposed to 1-D and transient boundary conditions. Part I of this paper describes the transient moisture transfer (TMT) facility used to generate the Experimental Data as well as the uncertainty and repeatability of the measured Data. The measured material properties are also presented to fully document the Experimental Data Set and permit its use by other researchers.
Tyrel Glass - One of the best experts on this subject based on the ideXlab platform.
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autonomous fingerprinting and large Experimental Data Set for visible light positioning
Sensors, 2021Co-Authors: Tyrel Glass, Fakhrul Alam, Mathew Legg, Frazer K NobleAbstract:This paper presents an autonomous method of collecting Data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large Set of Experimental Data. Received Signal Strength (RSS) Data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS Data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the Data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small Data Set is available for calibration and training. However, if a large Data Set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.
