Predicting the thermophysical properties of skin tumor based on the surface temperature and deep learning

2021 ◽  
Vol 180 ◽  
pp. 121804
Author(s):  
Haolong Chen ◽  
Kaijie Wang ◽  
Zhibo Du ◽  
Weiming Liu ◽  
Zhanli Liu
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Thermophysical Properties ◽  
Skin Tumor

scholarly journals Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

2021 ◽  
Vol 13 (4) ◽  
pp. 744
Author(s):  
J. Xavier Prochaska ◽  
Peter C. Cornillon ◽  
David M. Reiman
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Space Distribution ◽  
Western Boundary ◽  
Learning Modules ◽  
Ocean Science ◽  
The Many ◽  
Future Work

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color).

scholarly journals Thermophysical Properties of Thermosoftening Nasotracheal Tubes

2018 ◽  
Vol 65 (2) ◽  
pp. 100-105
Author(s):  
Yoshihiro Takasugi ◽  
Koichi Futagawa ◽  
Takashi Umeda ◽  
Kouhei Kazuhara ◽  
Satoshi Morishita
Keyword(s):  
Body Temperature ◽  
Surface Temperature ◽  
Infrared Thermography ◽  
Polyvinyl Chloride ◽  
Thermophysical Properties ◽  
Burn Injury ◽  
Nasotracheal Intubation ◽  
Posterior Wall

Thermosoftening treatment of polyvinyl chloride (PVC) nasotracheal tubes (NTTs) can reduce the incidence and amount of epistaxis during nasotracheal intubation. The optimal thermal setting for thermosoftening treatment of NTTs without burn injury was investigated. Two composite types of PVC NTTs were used. Following withdrawal of the PVC NTTs from a bottle of water at 45 or 60°C, the changes in the surface temperature of the NTTs were measured by infrared thermography. Hardness of the NTTs at 25, 30, 35, and 40°C was measured. The incidence of epistaxis during nasotracheal intubation using thermosoftened NTTs was evaluated retrospectively. The surface temperature of both PVC NTTs dipped in 45 and 60°C water decreased to below body temperature 30 seconds after withdrawing them from the bottles. Although thermosoftening treatment proportionally decreased the hardness of both types of NTTs, the degrees differed according to their composition. When avoiding impingement of the NTT on the posterior wall of the nasopharynx, the incidence of mild and moderate epistaxis was 2.3%. Flexibility of PVC NTTs could be obtained by thermosoftening treatment at 60°C without burn injury. Thermosoftening treatment of PVC NTTs may be useful to avoid epistaxis during nasotracheal intubation.

Deep Learning for Sea Temperature Eddy signature Classification

2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Imagery ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Temperature ◽  
Cloud Coverage

<p>Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years <strong>[1]</strong>. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.</p><p>Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.</p><p>We introduce a novel Deep Learning approach to classify sea temperature eddy signatures <strong>[2]</strong>. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas <strong>[3]</strong>. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.</p><p>We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.</p><p><strong>[1]</strong> <em>Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.</em></p><p><strong>[2]</strong> <em>E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.</em></p><p><strong>[3]</strong> <em>https://www.lmd.polytechnique.fr/dyned/</em></p>

scholarly journals Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

2021 ◽  
Vol 13 (24) ◽  
pp. 5003
Author(s):  
Elisa Castelli ◽  
Enzo Papandrea ◽  
Alessio Di Roma ◽  
Ilaria Bloise ◽  
Mattia Varile ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
State Of The Art ◽  
Spectral Response ◽  
High Spectral Resolution ◽  
Learning Techniques

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

scholarly journals Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3383
Author(s):  
Uzair Sajjad ◽  
Imtiyaz Hussain ◽  
Muhammad Imran ◽  
Muhammad Sultan ◽  
Chi-Chuan Wang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Deep Learning ◽  
Thermophysical Properties ◽  
Boiling Heat Transfer ◽  
Parameters Estimation ◽  
Learning Method ◽  
Working Fluids ◽  
Coated Surfaces ◽  
Nanoporous Surfaces

The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.

scholarly journals Wind-driven snow conditions control the occurrence of contemporary marginal mountain permafrost in the Chic-Choc Mountains, south-eastern Canada: a case study from Mont Jacques-Cartier

2017 ◽  
Vol 11 (3) ◽  
pp. 1351-1370 ◽  
Author(s):  
Gautier Davesne ◽  
Daniel Fortier ◽  
Florent Domine ◽  
James T. Gray
Keyword(s):  
Surface Temperature ◽  
Snow Cover ◽  
Thermophysical Properties ◽  
Ground Surface ◽  
Climatic Conditions ◽  
Eastern Canada ◽  
Cold Surface ◽  
Ground Surface Temperature ◽  
South Eastern ◽  
The Impact

Abstract. We present data on the distribution and thermophysical properties of snow collected sporadically over 4 decades along with recent data of ground surface temperature from Mont Jacques-Cartier (1268 m a.s.l.), the highest summit in the Appalachians of south-eastern Canada. We demonstrate that the occurrence of contemporary permafrost is necessarily associated with a very thin and wind-packed winter snow cover which brings local azonal topo-climatic conditions on the dome-shaped summit. The aims of this study were (i) to understand the snow distribution pattern and snow thermophysical properties on the Mont Jacques-Cartier summit and (ii) to investigate the impact of snow on the spatial distribution of the ground surface temperature (GST) using temperature sensors deployed over the summit. Results showed that above the local treeline, the summit is characterized by a snow cover typically less than 30 cm thick which is explained by the strong westerly winds interacting with the local surface roughness created by the physiography and surficial geomorphology of the site. The snowpack structure is fairly similar to that observed on windy Arctic tundra with a top dense wind slab (300 to 450 kg m−3) of high thermal conductivity, which facilitates heat transfer between the ground surface and the atmosphere. The mean annual ground surface temperature (MAGST) below this thin and wind-packed snow cover was about −1 °C in 2013 and 2014, for the higher, exposed, blockfield-covered sector of the summit characterized by a sporadic herbaceous cover. In contrast, for the gentle slopes covered with stunted spruce (krummholz), and for the steep leeward slope to the south-east of the summit, the MAGST was around 3 °C in 2013 and 2014. The study concludes that the permafrost on Mont Jacques-Cartier, most widely in the Chic-Choc Mountains and by extension in the southern highest summits of the Appalachians, is therefore likely limited to the barren wind-exposed surface of the summit where the low air temperature, the thin snowpack and the wind action bring local cold surface conditions favourable to permafrost development.

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