scholarly journals Using Bayesian Inference Framework towards Identifying Gas Species and Concentration from High Temperature Resistive Sensor Array Data

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yixin Liu ◽  
Kai Zhou ◽  
Yu Lei
Keyword(s):  
High Temperature ◽  
Bayesian Inference ◽  
Sensor Array ◽  
Response Pattern ◽  
Data Set ◽  
Array Data ◽  
Reducing Gas ◽  
Benchmark Database ◽  
Resistive Sensor ◽  
Bayesian Inference Framework

High temperature gas sensors have been highly demanded for combustion process optimization and toxic emissions control, which usually suffer from poor selectivity. In order to solve this selectivity issue and identify unknown reducing gas species (CO, CH4, and CH8) and concentrations, a high temperature resistive sensor array data set was built in this study based on 5 reported sensors. As each sensor showed specific responses towards different types of reducing gas with certain concentrations, based on which calibration curves were fitted, providing benchmark sensor array response database, then Bayesian inference framework was utilized to process the sensor array data and build a sample selection program to simultaneously identify gas species and concentration, by formulating proper likelihood between input measured sensor array response pattern of an unknown gas and each sampled sensor array response pattern in benchmark database. This algorithm shows good robustness which can accurately identify gas species and predict gas concentration with a small error of less than 10% based on limited amount of experiment data. These features indicate that Bayesian probabilistic approach is a simple and efficient way to process sensor array data, which can significantly reduce the required computational overhead and training data.

scholarly journals Spline approximation-based data compression for sensor arrays in the wireless hydrologic monitoring system

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769258
Author(s):  
Danyang Li ◽  
Wei Huangfu ◽  
Keping Long
Keyword(s):  
Data Compression ◽  
Monitoring System ◽  
Sensor Array ◽  
Spline Approximation ◽  
Sensor Arrays ◽  
Huffman Coding ◽  
Long Distance ◽  
Data Set ◽  
Array Data ◽  
Hydrologic Monitoring

A sensor array produces lots of bits of data every sample period, which may cause a heavy burden on the long-distance wireless data transmission, especially in the scenarios of wireless sensor networks. A lossy but error-bounded sensor array data compression algorithm is proposed, in which the major part of the sensor array data are approximated by the Catmull-Rom spline curve and the residual errors are quantized and encoded with Huffman coding. The performance of this algorithm has been evaluated with a real data set from the wireless hydrologic monitoring system deployed in Qinhuangdao Port of China. The results show that the algorithm performs well for the hydrologic sensor array data.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals Quantification of leaf greenness and leaf spectral profile in plant diagnosis using an optical scanner

2012 ◽  
Vol 36 (3) ◽  
pp. 309-317 ◽  
Author(s):  
Ryoichi Doi
Keyword(s):  
Spectral Data ◽  
Crop Production ◽  
Response Pattern ◽  
Quadratic Growth ◽  
Spectral Profile ◽  
Growth Data ◽  
Data Set ◽  
Optical Scanner ◽  
Color Scale ◽  
Management Measures

Observation of leaf spectral profile (color) enables suitable management measures to be taken for crop production. An optical scanner was used: 1) to obtain an equation to determine the greenness of plant leaves and 2) to examine the power to discriminate among plants grown under different nutritional conditions. Sweet basil seedlings grown on vermiculite were supplemented with one-fifth-strength Hoagland solutions containing 0, 0.2, 1, 5, 20, and 50 mM NH4+. The 5 mM treatment resulted in the greatest leaf and shoot weights, indicating a quadratic growth response pattern to the NH4+ gradient. An equation involving b*, black and green to describe the greenness of leaves was provided by the spectral profiling of a color scale for rice leaves as the standard. The color scale values for the basil leaves subjected to 0.2 and 1 mM NH4+ treatments were 1.00 and 1.12, respectively. The other treatments resulted in significantly greater values of 2.25 to 2.42, again indicating a quadratic response pattern. Based on the spectral data set consisting of variables of red-green-blue and other color models and color scale values, in discriminant analysis, 81% of the plants were correctly classified into the six NH4+ treatment groups. Combining the spectral data set with the growth data set consisting of leaf and shoot weights, 92% of the plant samples were correctly classified whereas, using the growth data set, only 53% of plants were correctly classified. Therefore, the optical scanning of leaves and the use of spectral profiles helped plant diagnosis when biomass measurements were not effective.

3-sensor array for hand held breath diagnostic tool

2013 ◽  
Vol 1533 ◽  
Author(s):  
P. Gouma ◽  
S. Sood
Keyword(s):  
Diagnostic Tool ◽  
Sensor Array ◽  
Gas Sensing ◽  
Orthorhombic Phase ◽  
Structural Transformations ◽  
Polymorphic Transitions ◽  
Heat Treated ◽  
Gas Sensing Properties ◽  
Resistive Sensor ◽  
Resistance Value

ABSTRACTPolymorphic transitions in nanocrystalline metal oxides leads to structural transformations resulting in differing properties at varying operating temperatures. Nanocrystalline MoO3 transforms from a metastable monoclinic phase to stable orthorhombic phase when heat treated in the temperature range of 420C to 500C. Gas sensing results have shown that at 420C MoO3 is sensitive to Isoprene, a 450C it shows sensitivity to CO2 and to ammonia at 500C. DSC data has proved that MoO3 changes crystal structure to monoclinic at 420C and to orthorhombic at about485C. This confirms a correlation between structure and gas sensing properties of MoO3. Using this knowledge a hand-held diagnostic tool is developed to monitor specific breath gases which can be biomarkers for diseases. The device consists of three sensors, the read-out gives a real time resistance value for each resistive sensor which is stored in a microprocessor. This is a one of a kind handheld tool for disease detection using ceramic sensors as detectors for gases which are known to be biomarkers for diseases.

Design and Crosstalk Error Analysis of the Circuit for the 2-D Networked Resistive Sensor Array

2015 ◽  
Vol 15 (2) ◽  
pp. 1020-1026 ◽  
Author(s):  
JianFeng Wu ◽  
Lei Wang ◽  
JianQing Li
Keyword(s):  
Error Analysis ◽  
Sensor Array ◽  
Resistive Sensor

Likelihood-free Bayesian inference framework for sizing kiwifruit from orchard imaging surveys

2021 ◽  
Author(s):  
Louis Ranjard ◽  
James Bristow ◽  
Zulfikar Hossain ◽  
Alvaro Orsi ◽  
Henry J. Kirkwood ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Bayesian Inference Framework

Aging Response Curves

2004 ◽  
pp. 133-173
Keyword(s):  
Tensile Strength ◽  
High Temperature ◽  
Ultimate Tensile Strength ◽  
Yield Strength ◽  
Casting Alloy ◽  
Response Curves ◽  
Data Set ◽  
Casting Alloys ◽  
Aging Response ◽  
Wide Range

Abstract This data set presents aging response curves for a wide range of aluminum casting alloys. The aging response curves are of two types: room-temperature, or "natural," curves and artificial, or "high-temperature," curves. The curves in each group are presented in the numeric sequence of the casting alloy designation. The curves included are the results of measurements on individual lots considered representative of the respective alloys and tempers. The properties considered are yield strength, ultimate tensile strength, elongation, and Brinell hardness.

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