scholarly journals Fusion Retrieval of Sea Surface Barometric Pressure from the Microwave Humidity and Temperature Sounder and Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite

2022 ◽  
Vol 14 (2) ◽  
pp. 276
Author(s):  
Qiurui He ◽  
Zhenzhan Wang ◽  
Jiaoyang Li
Keyword(s):  
Deep Neural Network ◽  
Barometric Pressure ◽  
High Accuracy ◽  
Passive Microwave ◽  
Sea Surface ◽  
60 Ghz ◽  
Test Results ◽  
Retrieval Accuracy ◽  
Satellite Platform ◽  
Channel Combination

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.

Passive Microwave Measurements Of Sea Surface Roughness During The Saxon Experiment

2005 ◽  
Author(s):  
G.D. Sandlin ◽  
L.A. Rose ◽  
G.L. Geernaert ◽  
J.P. Hollinger ◽  
F.A. Hansen
Keyword(s):  
Surface Roughness ◽  
Passive Microwave ◽  
Sea Surface ◽  
Microwave Measurements ◽  
Sea Surface Roughness

scholarly journals Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables

2020 ◽  
Vol 10 (15) ◽  
pp. 5381
Author(s):  
Yi Zhang ◽  
Zaijun Wu ◽  
Cheng Qian ◽  
Xiao Tan ◽  
Jinggang Yang ◽  
...  
Keyword(s):  
Operating Time ◽  
Prediction Method ◽  
Hardness Test ◽  
High Accuracy ◽  
Future Research ◽  
Test Results ◽  
Scanning Calorimetry ◽  
Characterization Methods ◽  
Gel Content ◽  
Mechanical And Electrical Properties

In this paper, cross-linked polyethylene (XLPE) cables of the same batch from Factory A, which ran from 1 to 8 years in Jiangsu Province, are sampled. Some widely accepted aging characterization methods of XLPE cables such as the gel content test, differential scanning calorimetry (DSC) test, tensile test and hardness test are employed to obtain the physicochemical, mechanical and electrical properties of the samples. Then, some lifespan prediction parameters significantly correlated with operating time are obtained through correlation calculations. Finally, a prediction method is proposed to predict the operating time of XLPE cables from Factory A. The test results indicate that parameters including the gel content Cge, the crystallinity XC, tensile strength σ, ultimate elongation δ, the dielectric permittivity ε, and the dielectric loss Jtan are significantly correlated with operating time, which can be used in evaluating the aging degree of XLPE cables. Moreover, due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.

scholarly journals CH<sub>4</sub>, CO, and H<sub>2</sub>O spectroscopy for the Sentinel-5 Precursor mission: an assessment with the Total Carbon Column Observing Network measurements

2012 ◽  
Vol 5 (6) ◽  
pp. 1387-1398 ◽  
Author(s):  
A. Galli ◽  
A. Butz ◽  
R. A. Scheepmaker ◽  
O. Hasekamp ◽  
J. Landgraf ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Fourier Transform ◽  
Short Wave ◽  
Total Carbon ◽  
Satellite Mission ◽  
Spectral Window ◽  
Retrieval Accuracy ◽  
Satellite Platform ◽  
Constant Bias ◽  
Absorption Features

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) will be part of ESA's Sentinel-5 Precursor (S5P) satellite platform scheduled for launch in 2015. TROPOMI will monitor methane and carbon monoxide concentrations in the Earth's atmosphere by measuring spectra of back-scattered sunlight in the short-wave infrared (SWIR). S5P will be the first satellite mission to rely uniquely on the spectral window at 4190–4340 cm−1 (2.3 μm) to retrieve CH4 and CO. In this study, we investigated if the absorption features of the three relevant molecules CH4, CO, and H2O are adequately known. To this end, we retrieved total columns of CH4, CO, and H2O from absorption spectra measured by two ground-based Fourier transform spectrometers that are part of the Total Carbon Column Observing Network (TCCON). The retrieval results from the 4190–4340 cm−1 range at the TROPOMI resolution (0.45 cm−1) were then compared to the CH4 results obtained from the 6000 cm−1 region, and the CO results obtained from the 4190–4340 cm−1 region at the higher TCCON resolution (0.02 cm−1). For TROPOMI-like settings, we were able to reproduce the CH4 columns to an accuracy of 0.3% apart from a constant bias of 1%. The CO retrieval accuracy was, through interference, systematically influenced by the shortcomings of the CH4 and H2O spectroscopy. In contrast to CH4, the CO column error also varied significantly with atmospheric H2O content. Unaddressed, this would introduce seasonal and latitudinal biases to the CO columns retrieved from TROPOMI measurements. We therefore recommend further effort from the spectroscopic community to be directed at the H2O and CH4 spectroscopy in the 4190–4340 cm−1 region.

scholarly journals Design, Motivation and Evaluation of a Full-Resolution Optical Tactile Sensor

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 928 ◽  
Author(s):  
Carmelo Sferrazza ◽  
Raffaello D’Andrea
Keyword(s):  
High Resolution ◽  
Deep Neural Network ◽  
Normal Force ◽  
Tactile Sensor ◽  
High Accuracy ◽  
Image Sensors ◽  
Random Pattern ◽  
Full Resolution ◽  
Sense Of Touch ◽  
Modern Image

Human skin is capable of sensing various types of forces with high resolution and accuracy. The development of an artificial sense of touch needs to address these properties, while retaining scalability to large surfaces with arbitrary shapes. The vision-based tactile sensor proposed in this article exploits the extremely high resolution of modern image sensors to reconstruct the normal force distribution applied to a soft material, whose deformation is observed on the camera images. By embedding a random pattern within the material, the full resolution of the camera can be exploited. The design and the motivation of the proposed approach are discussed with respect to a simplified elasticity model. An artificial deep neural network is trained on experimental data to perform the tactile sensing task with high accuracy for a specific indenter, and with a spatial resolution and a sensing range comparable to the human fingertip.

scholarly journals Convolutional Neural Networks for On-Board Cloud Screening

2019 ◽  
Vol 11 (12) ◽  
pp. 1417 ◽  
Author(s):  
Sina Ghassemi ◽  
Enrico Magli
Keyword(s):  
Network Performance ◽  
State Of The Art ◽  
High Accuracy ◽  
Resource Consumption ◽  
Processing Unit ◽  
Spectral Bands ◽  
Input Size ◽  
Satellite Platform ◽  
Screening Unit ◽  
Cloud Screening

A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.

Smoking causes early biological aging: a deep neural network analysis of common blood test results

2018 ◽  
Author(s):  
Neil Skjodt ◽  
Polina Mamoshina ◽  
Kirilli Kochetov ◽  
Franco Cortese ◽  
Anna Kovalchuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Blood Test ◽  
Deep Neural Network ◽  
Biological Aging ◽  
Test Results ◽  
Neural Network Analysis

scholarly journals CH<sub>4</sub>, CO, and H<sub>2</sub>O spectroscopy for the Sentinel-5 Precursor mission: an assessment with the Total Carbon Column Observing Network measurements

2012 ◽  
Vol 5 (2) ◽  
pp. 2131-2167
Author(s):  
A. Galli ◽  
A. Butz ◽  
R. A. Scheepmaker ◽  
O. Hasekamp ◽  
J. Landgraf ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Fourier Transform ◽  
Short Wave ◽  
Total Carbon ◽  
Satellite Mission ◽  
Spectral Window ◽  
Retrieval Accuracy ◽  
Satellite Platform ◽  
Constant Bias ◽  
Absorption Features

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) will be part of ESA's Sentinel-5 Precursor (S5P) satellite platform scheduled for launch in 2015. TROPOMI will monitor methane and carbon monoxide concentrations in the Earth's atmosphere by measuring spectra of back-scattered sunlight in the short-wave infrared (SWIR). S5P will be the first satellite mission to rely uniquely on the spectral window at 4190–4340 cm−1 (2.3 μm) to retrieve CH4 and CO. In this study, we investigated if the absorption features of the three relevant molecules CH4, CO, and H2O are adequately known. To this end, we retrieved total columns of CH4, CO, and H2O from absorption spectra measured by two ground-based Fourier transform spectrometers that are part of the Total Carbon Column Observing Network (TCCON). The retrieval results from the 4190–4340 cm−1 range at the TROPOMI resolution (0.45 cm−1) were then compared to the CH4 results obtained from the 6000 cm−1 region, and the CO results obtained from the 4190–4340 cm−1 region at the higher TCCON resolution (0.02 cm−1). For TROPOMI-like settings, we were able to reproduce the CH4 columns to an accuracy of 0.3% apart from a constant bias of 1%. The CO retrieval accuracy was, through interference, systematically influenced by the shortcomings of the CH4 and H2O spectroscopy. In contrast to CH4, the CO column error also varied significantly with atmospheric H2O content. Unaddressed, this would introduce seasonal and latitudinal biases to the CO columns retrieved from TROPOMI measurements. We therefore recommend further effort from the spectroscopic community to be directed at the H2O and CH4 spectroscopy in the 4190–4340 cm−1 region.

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