scholarly journals Inverse method for quantitative characterisation of breast tumours from surface temperature data

2017 ◽  
pp. 1-17 ◽  
Author(s):  
R. Hatwar ◽  
C. Herman
Keyword(s):  
Surface Temperature ◽  
Inverse Method ◽  
Temperature Data ◽  
Breast Tumours ◽  
Quantitative Characterisation ◽  
Surface Temperature Data

High-output diesel engine heat transfer: Part 1 - comparison between piston heat flux and global energy balance

2021 ◽  
pp. 146808742110170
Author(s):  
Eric Gingrich ◽  
Michael Tess ◽  
Vamshi Korivi ◽  
Jaal Ghandhi
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Diesel Engine ◽  
Surface Temperature ◽  
Fast Response ◽  
Temperature Data ◽  
Start Of Injection ◽  
Local Measurements ◽  
Surface Temperature Data ◽  
High Output

High-output diesel engine heat transfer measurements are presented in this paper, which is the first of a two-part series of papers. Local piston heat transfer, based on fast-response piston surface temperature data, is compared to global engine heat transfer based on thermodynamic data. A single-cylinder research engine was operated at multiple conditions, including very high-output cases – 30 bar IMEPg and 250 bar in-cylinder pressure. A wireless telemetry system was used to acquire fast-response piston surface temperature data, from which heat flux was calculated. An interpolation and averaging procedure was developed and a method to recover the steady-state portion of the heat flux based on the in-cylinder thermodynamic state was applied. The local measurements were spatially integrated to find total heat transfer, which was found to agree well with the global thermodynamic measurements. A delayed onset of the rise of spatially averaged heat flux was observed for later start of injection timings. The dataset is internally consistent, for example, the local measurements match the global values, which makes it well suited for heat transfer correlation development; this development is pursued in the second part of this paper.

Approximating Lake Ice Phenology with Satellite Surface Temperature Data

2021 ◽  
Author(s):  
Sophia Karina Skoglund ◽  
Abdou Rachid Bah ◽  
Hamidreza Norouzi ◽  
Kathleen C Weathers ◽  
Holly A Ewing ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Lake Ice ◽  
Surface Temperature Data ◽  
Ice Phenology

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

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