Browsing by Subject "Calibration"

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  • Lampsijärvi, Eetu (Helsingin yliopisto, 2020)
    The feasibility of quantitatively measuring ultrasound in air with a Schlieren arrangement has been demonstrated before, but previous work demonstrating calibration of the system combined with computation to yield the 3D pressure field does not exist. The present work demonstrates the feasibility of this both in theory and practice, and characterizes the setup used to gain the results. Elementary ray optical and Schlieren theory is exhibited to support the claims. Derivation of ray optical equations related to quantitative Schlieren measurements are shown step by step to help understand the basics. A numerical example based on the theoretical results is then displayed: Synthetic Schlieren images are computed for a theoretical ultrasonic field using direct numerical integration, then the ultrasonic field is recovered from the Synthetic Schlieren images using the inverse Abel transform. Accuracy of the inverse transform is evaluated in presence of synthetic noise. The Schlieren arrangement, including the optics, optomechanics, and electronics, to produce the results is explained along with the stroboscopic use of the light source to freeze ultrasound in the photographs. Postprocessing methods such as background subtraction and median and Gaussian filtering are used. The repeatability and uncertainty of the calibration is examined by performing repeated calibration while translating or rotating the calibration targets. The ultrasound fields emitted by three transducers (100 kHz, 175 kHz, and 300 kHz) when driven by 5 cycle sine bursts at 400 Vpp are measured at two different points in time. The measured 3D pressure fields measured for each transducer are shown along with a line profile near the acoustic axis. Pressure amplitudes range near 1 kPa, which is near the acoustic pressure, are seen. Nonlinearity is seen in the waveforms as expected for such high pressures. Noise estimates from the numerical example suggest that the pressure amplitudes have an uncertainty of 10% due to noise in the photographs. Calibration experiments suggest that additional uncertainty of about 2% per degree of freedom (Z, X, rotation) is to be expected unless especial care is taken. The worst-case uncertainty is estimated to be 18%. Limitations and advantages of the method are discussed. As Schlieren is a non-contacting method it is advantageous over microphone measurements, which may affect the field they are measuring. As every photograph measures the whole field, no scanning of the measurement device is required, such as with a microphone or with an LDV. Suggestions to improve the measurement setup are provided.
  • Ruiz-Jimenez, Jose; Lan, Hangzhen; Leleev, Yevgeny; Hartonen, Kari; Riekkola, Marja-Liisa (2020)
    Several calibration approaches were evaluated for the quantitation of volatile organic compounds in air using miniaturized exhaustive and non-exhaustive sampling techniques, such as in-tube extraction (ITEX) and solid phase microextraction (SPME) Arrow. Eleven compounds, 2-ethyl-hexanol, hexanal, nonanal, toluene, ethyl-benzene, methyl isobutyl ketone, acetophenone, p-cymene, alpha-pinene, trimethylamine and triethylamine, all them found in the natural air samples, were selected as model analytes. Liquid injection, liquid standard addition to the sorbent bed and gas phase standards provided by an automatic permeation system, were evaluated in the case of ITEX packed with laboratory-made 10% polyacrylonitrile (PAN) material. Two different approaches, based on sampling of gas phase compounds from the permeation system and from sample vial containing gas phase standards, were evaluated for SPME Arrow with two different coatings, commercial divinylbenzene-poly(dimethylsiloxane) (DVB-PDMS) and laboratory-made mesoporous Mobil Composition of Matter No. 41 (MCM-41). In addition, interface model approach was used for the calculation of the real concentration of the target analytes in the sample from the total amount of analytes injected into the GC-MS in the case of SPME Arrow. Similar results were obtained with the different approaches used for the quantitation by ITEX and SPME Arrow. However, the use of gas phase standards with sample matrix similar to the natural samples, allowed the permeation system to provide the most reliable results for the quantitation of the target analytes. For this approach, linearity (expressed as r(2) values) ranged between 0.991 and 0.999. The limit of detection ranged from 0.5 mu g/m(3) (trimethylamine, MCM-41) to 2.2 x 10(-4) mu g/m(3) (methyl isobutyl ketone, MCM-41). In addition, the use of the fully automated permeation system provided good reproducibility values that were between 1.4% (acetophenone, MCM-41) and 7.8% (methyl isobutyl ketone, 10% PAN). The linear ranges were at least 3 order of magnitude for all the studied analytes with the exception of the calibration curve developed for trimethylamine with SPME Arrow (linear ranges between LOQ and 4.9 mu g/m(3) (DVB-PDMS) and LOQ and 9.8 mu g/m(3) (MCM-41)). (C) 2019 Elsevier B.V. All rights reserved.
  • Su, Xiang; Liu, Xiaoli; Hossein Motlagh, Naser; Cao, Jacky; Su, Peifeng; Pellikka, Petri; Liu, Yongchun; Petäjä, Tuukka; Kulmala, Markku; Hui, Pan; Tarkoma, Sasu (2021)
    Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.
  • Zaidan, Martha Arbayani; Hossein Motlagh, Naser; Fung, Pak Lun; Lu, David; Timonen, Hilkka; Kuula, Joel; Niemi, Jarkko V; Tarkoma, Sasu; Petäjä, Tuukka; Kulmala, Markku; Hussein, Tareq (2020)
    This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.
  • Naafs, B. D. A.; Inglis, G. N.; Zheng, Y.; Amesbury, M. J.; Biester, H.; Bindler, R.; Blewett, J.; Burrows, M. A.; del Castillo Torres, D.; Chambers, F. M.; Cohen, A. D.; Evershed, R. P.; Feakins, S. J.; Galka, M.; Gallego-Sala, A.; Gandois, L.; Gray, D. M.; Hatcher, P. G.; Honorio Coronado, E. N.; Hughes, P. D. M.; Huguet, A.; Kononen, M.; Laggoun-Defarge, F.; Lahteenoja, O.; Lamentowicz, M.; Marchant, R.; McClymont, E.; Pontevedra-Pombal, X.; Ponton, C.; Pourmand, A.; Rizzuti, A. M.; Rochefort, L.; Schellekens, J.; De Vleeschouwer, F.; Pancost, R. D. (2017)
    Glycerol dialkyl glycerol tetraethers (GDGTs) are membrane-spanning lipids from Bacteria and Archaea that are ubiquitous in a range of natural archives and especially abundant in peat. Previous work demonstrated that the distribution of bacterial branched GDGTs (brGDGTs) in mineral soils is correlated to environmental factors such as mean annual air temperature (MAAT) and soil pH. However, the influence of these parameters on brGDGT distributions in peat is largely unknown. Here we investigate the distribution of brGDGTs in 470 samples from 96 peatlands around the world with a broad mean annual air temperature (-8 to 27 degrees C) and pH (3-8) range and present the first peat-specific brGDGT-based temperature and pH calibrations. Our results demonstrate that the degree of cyclisation of brGDGTs in peat is positively correlated with pH, pH = 2.49 x CBTpeat + 8.07 (n = 51, R-2 = 0.58, RMSE = 0.8) and the degree of methylation of brGDGTs is positively correlated with MAAT, MAAT(peat) (degrees C) = 52.18 x MBT'(5me) - 23.05 (n = 96, R-2 = 0.76, RMSE = 4.7 degrees C). These peat-specific calibrations are distinct from the available mineral soil calibrations. In light of the error in the temperature calibration (similar to 4.7 degrees C), we urge caution in any application to reconstruct late Holocene climate variability, where the climatic signals are relatively small, and the duration of excursions could be brief. Instead, these proxies are well-suited to reconstruct large amplitude, longer-term shifts in climate such as deglacial transitions. Indeed, when applied to a peat deposit spanning the late glacial period (similar to 15.2 kyr), we demonstrate that MAAT(peat) yields absolute temperatures and relative temperature changes that are consistent with those from other proxies. In addition, the application of MAAT(peat) to fossil peat (i.e. lignites) has the potential to reconstruct terrestrial climate during the Cenozoic. We conclude that there is clear potential to use brGDGTs in peats and lignites to reconstruct past terrestrial climate. (C) 2017 The Authors. Published by Elsevier Ltd.
  • Chattopadhyay, Arnab; Huertas Suarez, Andres Felippe; Rebeiro-Hargrave, Andrew; Fung, Pak Lun; Varjonen, Samu; Hieta, Tuomas; Tarkoma, Sasu; Petäjä, Tuukka (2022)
    Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which fail to capture their complex behaviour. We evaluated different low-cost gas sensors to ascertain a suitable component to create a mobile sensing node and built a calibration algorithm to correct it. We compared the performance of 2 electrochemical sensors and 3 metal oxide sensors in a controlled chamber against a photo-acoustic reference device. In the chamber the formaldehyde concentrations, temperature and humidity were varied to assess the sensors in diverse environments. Pre-calibration, the electrochemical sensors (mean absolute error (MAE) = 70.8 ppb) outperformed the best performing metal oxide sensor (MAE = 335 ppb). A two-stage calibration model was built, using linear regression followed by random forest, where the residual of the first stage acted as a input for the second. Post-calibration, the metal oxide sensors (MAE = 154 ppb) improved compared to their electrochemical counterparts (MAE = 78.8 ppb). Nevertheless, the uncalibrated electrochemical sensor showed overall superior performance hence was selected for the mobile sensing node.
  • Kiselev, Nikolai; Rosenbush, Vera; Muinonen, Karri; Kolokolova, Ludmilla; Savushkin, Aleksandr; Karpov, Nikolai (2022)
  • Uusheimo, Sari Anneli; Tulonen, Tiina Valpuri; Arvola, Lauri Matti Juhani; Arola, Hanna; Linjama, Jarmo; Huttula, Timo (2017)
    Compared with sporadic conventional watersampling, continuous water-quality monitoring with opti-cal sensors has improved our understanding of freshwaterdynamics. The basic principle in photometric measure-ments is the incident light at a given wavelength that iseither reflected, scattered, or transmitted in the body ofwater. Here, we discuss the transmittance measurements.The amount of transmittance is inversely proportional tothe concentration of the substance measured. However, thetransmittance is subject to interference, because it can beaffected by factors other than the substance targeted in thewater. In this study, interference with the UV/Vis sensornitrate plus nitrite measurements caused by organic carbonwas evaluated. Total or dissolved organic carbon as well asnitrate plus nitrite concentrations were measured in variousboreal waters with two UV/Vis sensors (5-mm and 35-mmpathlengths), using conventional laboratory analysis re-sults as references. Organic carbon increased the sensornitrate plus nitrite results, not only in waters with highorganic carbon concentrations, but also at the lower con-centrations (< 10 mg C L−1) typical of boreal stream, river,and lake waters. Our results demonstrated that local cali-bration with multiple linear regression, including bothnitrate plus nitrite and dissolved organic carbon, can cor-rect the error caused by organic carbon. However, high-frequency optical sensors continue to be excellent tools forenvironmental monitoring when they are properly calibrat-ed for the local water matrix.
  • Markelin, L.; Honkavaara, E.; Näsi, R.; Viljanen, N.; Rosnell, T.; Hakala, T.; Vastaranta, M.; Koivisto, T.; Holopainen, M. (2017)
    Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5% to 25%. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates. © Authors 2017.
  • Nyberg, Otto; Klami, Arto (Springer International Publishing AG, 2021)
    Lecture Notes in Artificial Intelligence
    Using classifiers for decision making requires well-calibrated probabilities for estimation of expected utility. Furthermore, knowledge of the reliability is needed to quantify uncertainty. Outputs of most classifiers can be calibrated, typically by using isotonic regression that bins classifier outputs together to form empirical probability estimates. However, especially for highly imbalanced problems it produces bins with few samples resulting in probability estimates with very large uncertainty. We provide a formal method for quantifying the reliability of calibration and extend isotonic regression to provide reliable calibration with guarantees for width of credible intervals of the probability estimates. We demonstrate the method in calibrating purchase probabilities in e-commerce and achieve significant reduction in uncertainty without compromising accuracy.
  • Järvinen, Miikka (Helsingin yliopisto, 2020)
    Two different bio transfer standards (BTS), composed of fatty acid bilayers, NanoRuler and NanoStar were developed. NanoRuler consists of a nanometer scale staircase with eight steps that are 5 nm tall each and NanoStar is designed to have topological structure with sharp edge and three height planes 5 nm elevated with respect to each other. With NanoRuler nanometer vertical calibration from 5 nm to 40 nm is possible and NanoStar allows the evaluation of the instrument transfer function (ITF). Due to the soft nature of the standards, the topographical stability was researched. Thus, an investigation of the topographical stability of three NanoRulers and one NanoStar across 24 months was done by measuring the surface topography with a custom-built Scanning White Light Interferometer (SWLI). The BTS were measured over 100 times during the experiments and were stored in laboratory conditions. The step heights of the structures were calculated with a histogram method and the surface roughness of the samples was evaluated using the Sq parameter. The step height analysis method was compared to the standard method (ISO 5436-1) where applicable and no notable differences were found. In both roughness and step height data no linear or non-linear trends were found, and the step heights compared well with the literature values. For NanoRuler the step heights were 4.9 nm, 10.1 nm, 15.1 nm, 20.1 nm, 25 nm, 30.1 nm, 35.1 nm and 40.2 nm and the respective stabilities were 0.3 nm, 0.3 nm, 0.6 nm, 0.9 nm, 1.3 nm, 1.6 nm, 2.1 nm, and 2.5 nm. For NanoStar the step heights were -5.1 nm and 5.2 nm with stabilities 0.3 nm and 0.4 nm respectively. The NanoRuler had a surface roughness stability of 0.02 nm whereas NanoStar had a roughness stability of 0.01 nm. After 24 months both BTS types preserved their topographical structure and no issues with surface topographical stability were observed.