A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance.
Ontology highlight
ABSTRACT:
SUBMITTER: Szenicer A
PROVIDER: S-EPMC6774717 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance.
Szenicer Alexandre A Fouhey David F DF Munoz-Jaramillo Andres A Wright Paul J PJ Thomas Rajat R Galvez Richard R Jin Meng M Cheung Mark C M MCM
Science advances 20191002 10
Measurements of the extreme ultraviolet (EUV) solar spectral irradiance (SSI) are essential for understanding drivers of space weather effects, such as radio blackouts, and aerodynamic drag on satellites during periods of enhanced solar activity. In this paper, we show how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA's Solar Dynamics Observatory obtained between 2010 to 2014. We describe a protocol and baselines for measuring the performa ...[more]
Similar Datasets
Project description:Plants commonly respond to UV radiation through the accumulation of flavonoids and related phenolic compounds which potentially ameliorate UV-damage to crucial internal structures. However, the seasonal dynamics of leaf flavonoids corresponding to epidermal UV absorbance is highly variable in nature, and it remains uncertain how environmental factors combine to govern flavonoid accumulation and degradation. We studied leaf UV-A absorbance of species composing the understorey plant community throughout two growing seasons under five adjacent tree canopies in southern Finland. We compared the relationship between leaf flavonol index (Iflav-repeatedly measured with an optical leaf clip Dualex) and measured spectral irradiance, understorey and canopy phenology, air temperature and snowpack variables, whole leaf flavonoid extracts, and leaf age. Strong seasonal patterns and stand-related differences were apparent in Iflav of both understorey plant communities and individual species, including divergent trends in Iflav during spring and autumn. Comparing the heterogeneity of the understorey light environment and its spectral composition in looking for potential drivers of seasonal changes in Iflav, we found that unweighted UV-A irradiance, or the effective UV dose calculated according to the biological spectral weighting function (BSWF) for plant growth (PG action spectrum), in understorey shade had a strong relationship with Iflav. Furthermore, understorey species seemed to adjust Iflav to low background diffuse irradiance rather than infrequent high direct-beam irradiance in sunflecks during summer, since leaves produced during or after canopy closure had low Iflav. In conclusion, we found the level of epidermal flavonoids in forest understorey species to be plastic, adjusting to climatic conditions, and differing according to species' leaf retention strategy and new leaf production, all of which contribute to the seasonal trends in leaf flavonoids found within forest stands.
| S-EPMC7040076 | biostudies-literature
Project description:As part of the UV-Indien project, a station for measuring ultraviolet radiation and the cloud fraction was installed in December 2019 in Moroni, the capital of the Comoros, situated on the west coast of the island of Ngazidja. A ground measurement campaign was also carried out on 12 January 2020 during the ascent of Mount Karthala, located in the center of the island of Ngazidja. In addition, satellite estimates (Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument) and model outputs (Copernicus Atmospheric Monitoring Service and Tropospheric Ultraviolet Model) were combined for this same region. On the one hand, these different measurements and estimates make it possible to quantify, evaluate, and monitor the health risk linked to exposure to ultraviolet radiation in this region, and, on the other, they help to understand how cloud cover influences the variability of UV-radiation on the ground. The measurements of the Ozone Monitoring Instrument onboard the EOS-AURA satellite, being the longest timeseries of ultraviolet measurements available in this region, make it possible to quantify the meteorological conditions in Moroni and to show that more than 80% of the ultraviolet indices are classified as high and that 60% of these are classified as extreme. The cloud cover measured in Moroni by an All Sky Camera was used to distinguish between the cases of UV index measurements taken under clear or cloudy sky conditions. The ground-based measurements thus made it possible to describe the variability of the diurnal cycle of the UV index and the influence of cloud cover on this parameter. They also permitted the satellite measurements and the results of the simulations to be validated. In clear sky conditions, a relative difference of between 6 and 11% was obtained between satellite or model estimates and ground measurements. The ultraviolet index measurement campaign on Mount Karthala showed maximum one-minute standard erythemal doses at 0.3 SED and very high daily cumulative erythemal doses at more than 80 SED. These very high levels are also observed throughout the year and all skin phototypes can exceed the daily erythemal dose threshold at more than 20 SED.
| S-EPMC8508397 | biostudies-literature
Project description:To analyze intensities of ocular exposure to direct (Eo,dir), reflected (Eo,refl), and diffuse (Eo,diff) ultraviolet (UV) irradiance at different solar elevation angles (SEAs), a rotating manikin and dual-detector spectrometer were used to monitor the intensity of ocular exposure to UV irradiation (Eo) and ambient UV radiation (UVR) under clear skies in Sanya, China. Eo,dir was derived as the difference between maximum and minimum measured Eo values. Eo,refl was converted from the value measured at a height of 160 cm. Eo,diff was calculated as the minimum measured Eo value minus Eo,refl. Regression curves were fitted to determine distributions of intensities and growth rates at different wavelengths and SEAs. Eo,dir differed from ambient UVR exposure. Linear, quadratic, and linear Eo,dir distributions were obtained in SEA ranges of 14°-30°, 30°-50°, and 50°-90°, respectively, with maximum Eo,dir at 32°-38° SEA. Growth rates of Eo,dir with increasing wavelength were fitted with quadratic functions in all SEA ranges. Distributions and growth rate of Eo,refl values were fitted with quadratic functions. Maximum Eo,diff was achieved at the same SEA for all fitted quadratic functions. Growth rate of Eo,diff with increasing wavelength was fitted with a linear function. Eo,dir distributions were fitted with linear or quadratic functions in different SEA ranges. All Eo,refl and Eo,diff distributions were fitted with quadratic functions. As SEA increased, the Eo,dir portion of Eo increased and then decreased; the Eo,refl portion increased from an initial minimum; and the Eo,diff portion first decreased and then increased. The findings may provide data supporting on construction of a mathematical model of ocular UV exposure.
| S-EPMC5112793 | biostudies-literature
Project description:PurposeTo evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space.MethodsWe designed and manufactured a model eye in which we recorded choreographed videos of many surgical instruments moving throughout the eye. We labeled each frame of the videos to describe the surgical tool characteristics: tool type, location, depth, and insertional laterality. We trained two different deep learning models to predict each of the tool characteristics and evaluated model performances on a subset of images.ResultsThe accuracy of the classification model on the training set is 84% for the x-y region, 97% for depth, 100% for instrument type, and 100% for laterality of insertion. The accuracy of the classification model on the validation dataset is 83% for the x-y region, 96% for depth, 100% for instrument type, and 100% for laterality of insertion. The close-up detection model performs at 67 frames per second, with precision for most instruments higher than 75%, achieving a mean average precision of 79.3%.ConclusionsWe demonstrated that trained models can track surgical instrument movement in three-dimensional space and determine instrument depth, tip location, instrument insertional laterality, and instrument type. Model performance is nearly instantaneous and justifies further investigation into application to real-world surgical videos.Translational relevanceDeep learning offers the potential for software-based safety feedback mechanisms during surgery or the ability to extract metrics of surgical technique that can direct research to optimize surgical outcomes.
| S-EPMC9851279 | biostudies-literature
Project description:Recent reconstructions of total solar irradiance (TSI) postulate that quiet-Sun variations could give significant changes to the solar power input to Earth's climate (radiative climate forcings of 0.7-1.1 W m-2 over 1700-2019) arising from changes in quiet-Sun magnetic fields that have not, as yet, been observed. Reconstructions without such changes yield solar forcings that are smaller by a factor of more than 10. We study the quiet-Sun TSI since 1995 for three reasons: (i) this interval shows rapid decay in average solar activity following the grand solar maximum in 1985 (such that activity in 2019 was broadly equivalent to that in 1900); (ii) there is improved consensus between TSI observations; and (iii) it contains the first modelling of TSI that is independent of the observations. Our analysis shows that the most likely upward drift in quiet-Sun radiative forcing since 1700 is between +0.07 and -0.13 W m-2. Hence, we cannot yet discriminate between the quiet-Sun TSI being enhanced or reduced during the Maunder and Dalton sunspot minima, although there is a growing consensus from the combinations of models and observations that it was slightly enhanced. We present reconstructions that add quiet-Sun TSI and its uncertainty to models that reconstruct the effects of sunspots and faculae.
| S-EPMC7428030 | biostudies-literature
Project description:The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.
| S-EPMC7988281 | biostudies-literature
Project description:Deep convolutional neural networks are emerging as the state of the art method for supervised classification of images also in the context of taxonomic identification. Different morphologies and imaging technologies applied across organismal groups lead to highly specific image domains, which need customization of deep learning solutions. Here we provide an example using deep convolutional neural networks (CNNs) for taxonomic identification of the morphologically diverse microalgal group of diatoms. Using a combination of high-resolution slide scanning microscopy, web-based collaborative image annotation and diatom-tailored image analysis, we assembled a diatom image database from two Southern Ocean expeditions. We use these data to investigate the effect of CNN architecture, background masking, data set size and possible concept drift upon image classification performance. Surprisingly, VGG16, a relatively old network architecture, showed the best performance and generalizing ability on our images. Different from a previous study, we found that background masking slightly improved performance. In general, training only a classifier on top of convolutional layers pre-trained on extensive, but not domain-specific image data showed surprisingly high performance (F1 scores around 97%) with already relatively few (100-300) examples per class, indicating that domain adaptation to a novel taxonomic group can be feasible with a limited investment of effort.
| S-EPMC7468105 | biostudies-literature
Project description:Various space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010-2014) and the Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission Solar and Heliospheric Observatory, which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a manual process based on assumed knowledge of the sensor hardware. Here we present a new data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e [Formula: see text] W/m[Formula: see text]) between the TSI levels during the two most recent solar minima in 2008/09 and 2019/20. The new algorithm can be applied to any TSI experiment that employs a multi-channel philosophy for degradation tracking. It does not require deep technical knowledge of the individual radiometers.
| S-EPMC8035225 | biostudies-literature
Project description:The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen grains are deposited on adhesive-coated tape, and slides are then prepared, which require further analysis by specialized personnel. Deep learning can be used to recognize pollen taxa based on microscopic images. This paper presents a method for recognizing a taxon based on microscopic images of pollen grains, allowing the pollen monitoring process to be automated. In this research, a deep CNN (convolutional neural network) model was built from scratch. Publicly available deep neural network models, pre-trained on image data (not including microscopic pictures), were also used. The results show that even a simple deep learning model produces quite good results when the classification of pollen grain taxa is performed directly from the images. The best deep learning model achieved 97.88% accuracy in the difficult task of recognizing three types of pollen grains (birch, alder, and hazel) with similar structures. The derived models can be used to build a system to support pollen monitoring experts in their work.
| S-EPMC8159113 | biostudies-literature
Project description:Identifying the environmental factors driving larval settlement processes is crucial to understand the population dynamics of marine invertebrates. This work aims to go a step ahead and predict larval presence and intensity. For this purpose we consider the influence of solar irradiance, wind regime and continental runoff on the settlement processes. For the first time, we conducted a 5-years weekly monitoring of Mytilus galloprovincialis settlement on artificial suspended substrates, which allowed us to search for interannual variability in the settlement patterns. Comparison between the seasonal pattern of larval settlement and solar irradiance, as well as the well-known effect of solar irradiance on water temperature and food availability, suggest that solar irradiance indirectly influences the settlement process, and support the use of this meteorological variable to predict settlement occurrence. Our results show that solar irradiance allows predicting the beginning and end of the settlement cycle a month in advance: Particularly we have observed that solar irradiance during late winter indirectly drives the timing and intensity of the settlement onset, Finally, a functional generalise additive model, which considers the influence of solar irradiance and continental runoff on the settlement process, provides an accurate prediction of settlement intensity a fortnight in advance.
| S-EPMC4935941 | biostudies-literature