scholarly journals Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi

2019 ◽  
Author(s):  
Tongshu Zheng ◽  
Michael H. Bergin ◽  
Ronak Sutaria ◽  
Sachchida N. Tripathi ◽  
Robert Caldow ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Gaussian Process ◽  
Large Scale ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Operating Conditions ◽  
Local Source ◽  
Model Parameters ◽  
Manual Labor ◽  
Source Contributions

Abstract. Wireless low-cost particulate matter sensor networks (WLPMSNs) are transforming air quality monitoring by providing PM information at finer spatial and temporal resolutions; however, large-scale WLPMSN calibration and maintenance remain a challenge because the manual labor involved in initial calibration by collocation and routine recalibration is intensive, the transferability of the calibration models determined from initial collocation to new deployment sites is questionable as calibration factors typically vary with urban heterogeneity of operating conditions and aerosol optical properties, and the stability of low-cost sensors can develop drift or degrade over time. This study presents a simultaneous Gaussian Process regression (GPR) and simple linear regression pipeline to calibrate and monitor dense WLPMSNs on the fly by leveraging all available reference monitors across an area without resorting to pre-deployment collocation calibration. We evaluated our method for Delhi where the PM2.5 measurements of all 22 regulatory reference and 10 low-cost nodes were available in 59 valid days from 1 January 2018 to 31 March 2018 (PM2.5 averaged 138 ± 31 μg m−3 among 22 reference stations) using a leave-one-out cross-validation (CV) over the 22 reference nodes. We showed that our approach can achieve an overall 30 % prediction error (RMSE: 33 μg m−3) at a 24 h scale and is robust as underscored by the small variability in the GPR model parameters and in the model-produced calibration factors for the low-cost nodes among the 22-fold CV. We revealed that the accuracy of our calibrations depends on the degree of homogeneity of PM concentrations, and decreases with increasing local source contributions. As by-products of dynamic calibration, our algorithm can be adapted for automated large-scale WLPMSN monitoring as simulations proved its capability of differentiating malfunctioning or singular low-cost nodes within a network via model-generated calibration factors with the aberrant nodes having slopes close to 0 and intercepts close to the global mean of true PM2.5 and of tracking the drift of low-cost nodes accurately within 4 % error for all the simulation scenarios. The simulation results showed that ~20 reference stations are optimum for our solution in Delhi and confirmed that low-cost nodes can extend the spatial precision of a network by decreasing the extent of pure interpolation among only reference stations. Our solution has substantial implications in reducing the amount of manual labor for the calibration and surveillance of extensive WLPMSNs, improving the spatial comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.

scholarly journals Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

2019 ◽  
Vol 12 (9) ◽  
pp. 5161-5181 ◽  
Author(s):  
Tongshu Zheng ◽  
Michael H. Bergin ◽  
Ronak Sutaria ◽  
Sachchida N. Tripathi ◽  
Robert Caldow ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Gaussian Process ◽  
Prediction Error ◽  
Large Scale ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Operating Conditions ◽  
Model Parameters ◽  
Manual Labor ◽  
The Mean

Abstract. Wireless low-cost particulate matter sensor networks (WLPMSNs) are transforming air quality monitoring by providing particulate matter (PM) information at finer spatial and temporal resolutions. However, large-scale WLPMSN calibration and maintenance remain a challenge. The manual labor involved in initial calibration by collocation and routine recalibration is intensive. The transferability of the calibration models determined from initial collocation to new deployment sites is questionable, as calibration factors typically vary with the urban heterogeneity of operating conditions and aerosol optical properties. Furthermore, the stability of low-cost sensors can drift or degrade over time. This study presents a simultaneous Gaussian process regression (GPR) and simple linear regression pipeline to calibrate and monitor dense WLPMSNs on the fly by leveraging all available reference monitors across an area without resorting to pre-deployment collocation calibration. We evaluated our method for Delhi, where the PM2.5 measurements of all 22 regulatory reference and 10 low-cost nodes were available for 59 d from 1 January to 31 March 2018 (PM2.5 averaged 138±31 µg m−3 among 22 reference stations), using a leave-one-out cross-validation (CV) over the 22 reference nodes. We showed that our approach can achieve an overall 30 % prediction error (RMSE: 33 µg m−3) at a 24 h scale, and it is robust as it is underscored by the small variability in the GPR model parameters and in the model-produced calibration factors for the low-cost nodes among the 22-fold CV. Of the 22 reference stations, high-quality predictions were observed for those stations whose PM2.5 means were close to the Delhi-wide mean (i.e., 138±31 µg m−3), and relatively poor predictions were observed for those nodes whose means differed substantially from the Delhi-wide mean (particularly on the lower end). We also observed washed-out local variability in PM2.5 across the 10 low-cost sites after calibration using our approach, which stands in marked contrast to the true wide variability across the reference sites. These observations revealed that our proposed technique (and more generally the geostatistical technique) requires high spatial homogeneity in the pollutant concentrations to be fully effective. We further demonstrated that our algorithm performance is insensitive to training window size as the mean prediction error rate and the standard error of the mean (SEM) for the 22 reference stations remained consistent at ∼30 % and ∼3 %–4 %, respectively, when an increment of 2 d of data was included in the model training. The markedly low requirement of our algorithm for training data enables the models to always be nearly the most updated in the field, thus realizing the algorithm's full potential for dynamically surveilling large-scale WLPMSNs by detecting malfunctioning low-cost nodes and tracking the drift with little latency. Our algorithm presented similarly stable 26 %–34 % mean prediction errors and ∼3 %–7 % SEMs over the sampling period when pre-trained on the current week's data and predicting 1 week ahead, and therefore it is suitable for online calibration. Simulations conducted using our algorithm suggest that in addition to dynamic calibration, the algorithm can also be adapted for automated monitoring of large-scale WLPMSNs. In these simulations, the algorithm was able to differentiate malfunctioning low-cost nodes (due to either hardware failure or under the heavy influence of local sources) within a network by identifying aberrant model-generated calibration factors (i.e., slopes close to zero and intercepts close to the Delhi-wide mean of true PM2.5). The algorithm was also able to track the drift of low-cost nodes accurately within 4 % error for all the simulation scenarios. The simulation results showed that ∼20 reference stations are optimum for our solution in Delhi and confirmed that low-cost nodes can extend the spatial precision of a network by decreasing the extent of pure interpolation among only reference stations. Our solution has substantial implications in reducing the amount of manual labor for the calibration and surveillance of extensive WLPMSNs, improving the spatial comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.

scholarly journals Hardware-in-the-loop simulation of wave energy converters based on dielectric elastomer generators

Meccanica ◽  
2021 ◽  
Vol 56 (5) ◽  
pp. 1223-1237
Author(s):  
Giacomo Moretti ◽  
Andrea Scialò ◽  
Giovanni Malara ◽  
Giovanni Gerardo Muscolo ◽  
Felice Arena ◽  
...  
Keyword(s):  
Wave Energy ◽  
Large Scale ◽  
Low Cost ◽  
Test Site ◽  
Dielectric Elastomer ◽  
Operating Conditions ◽  
Polymer Materials ◽  
Hardware In The Loop ◽  
Wave Energy Converters ◽  
Energy Converters

AbstractDielectric elastomer generators (DEGs) are soft electrostatic generators based on low-cost electroactive polymer materials. These devices have attracted the attention of the marine energy community as a promising solution to implement economically viable wave energy converters (WECs). This paper introduces a hardware-in-the-loop (HIL) simulation framework for a class of WECs that combines the concept of the oscillating water columns (OWCs) with the DEGs. The proposed HIL system replicates in a laboratory environment the realistic operating conditions of an OWC/DEG plant, while drastically reducing the experimental burden compared to wave tank or sea tests. The HIL simulator is driven by a closed-loop real-time hydrodynamic model that is based on a novel coupling criterion which allows rendering a realistic dynamic response for a diversity of scenarios, including large scale DEG plants, whose dimensions and topologies are largely different from those available in the HIL setup. A case study is also introduced, which simulates the application of DEGs on an OWC plant installed in a mild real sea laboratory test-site. Comparisons with available real sea-test data demonstrated the ability of the HIL setup to effectively replicate a realistic operating scenario. The insights gathered on the promising performance of the analysed OWC/DEG systems pave the way to pursue further sea trials in the future.

scholarly journals In Situ EPR Characterization of a Cobalt Oxide Water Oxidation Catalyst at Neutral pH

Catalysts ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 926 ◽  
Author(s):  
Yury Kutin ◽  
Nicholas Cox ◽  
Wolfgang Lubitz ◽  
Alexander Schnegg ◽  
Olaf Rüdiger
Keyword(s):  
Cobalt Oxide ◽  
Water Oxidation ◽  
Large Scale ◽  
Low Cost ◽  
Operating Conditions ◽  
Oxidation Catalyst ◽  
O2 Evolution ◽  
Ligand Field ◽  
Neutral Ph

Here we report an in situ electron paramagnetic resonance (EPR) study of a low-cost, high-stability cobalt oxide electrodeposited material (Co-Pi) that oxidizes water at neutral pH and low over-potential, representing a promising system for future large-scale water splitting applications. Using CW X-band EPR we can follow the film formation from a Co(NO3)2 solution in phosphate buffer and quantify Co uptake into the catalytic film. As deposited, the film shows predominantly a Co(II) EPR signal, which converts into a Co(IV) signal as the electrode potential is increased. A purpose-built spectroelectrochemical cell allowed us to quantify the extent of Co(II) to Co(IV) conversion as a function of potential bias under operating conditions. Consistent with its role as an intermediate, Co(IV) is formed at potentials commensurate with electrocatalytic O2 evolution (+1.2 V, vs. SHE). The EPR resonance position of the Co(IV) species shifts to higher fields as the potential is increased above 1.2 V. Such a shift of the Co(IV) signal may be assigned to changes in the local Co structure, displaying a more distorted ligand field or more ligand radical character, suggesting it is this subset of sites that represents the catalytically ‘active’ component. The described spectroelectrochemical approach provides new information on catalyst function and reaction pathways of water oxidation.

scholarly journals Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators

2019 ◽  
Vol 39 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Tiago M. de Carvalho ◽  
Eveline A. M. Heijnsdijk ◽  
Luc Coffeng ◽  
Harry J. de Koning
Keyword(s):  
Prostate Cancer ◽  
Simulation Model ◽  
Gaussian Process ◽  
Prediction Error ◽  
Life Histories ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Computational Effort ◽  
Sensitivity Analyses ◽  
Model Parameters

Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

scholarly journals Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4413 ◽  
Author(s):  
Adar Vit ◽  
Guy Shani
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Manual Labor ◽  
Automated Systems ◽  
Light Conditions ◽  
Current State ◽  
Field Phenotyping ◽  
Outdoor Experiment ◽  
Close Range ◽  
Plant Attributes

Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.

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