scholarly journals Wavelet-based Post-processing Methods for the Enhancement of Non-invasive Fetal ECG

2019 ◽  
Author(s):  
Giulia Baldazzi ◽  
Eleonora Sulas ◽  
Elisa Brungiu ◽  
Monica Urru ◽  
Roberto Tumbarello ◽  
...  
Keyword(s):  
Post Processing ◽  
Processing Methods ◽  
Non Invasive ◽  
Fetal Ecg

scholarly journals Post-Processing and Surface Characterization of Additively Manufactured Stainless Steel 316L Lattice: Implications for BioMedical Use

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1376
Author(s):  
Alex Quok An Teo ◽  
Lina Yan ◽  
Akshay Chaudhari ◽  
Gavin Kane O’Neill
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Characterization ◽  
High Surface ◽  
Surface Characteristics ◽  
Post Processing ◽  
Stainless Steel 316L ◽  
Processing Methods ◽  
Particulate Debris

Additive manufacturing of stainless steel is becoming increasingly accessible, allowing for the customisation of structure and surface characteristics; there is little guidance for the post-processing of these metals. We carried out this study to ascertain the effects of various combinations of post-processing methods on the surface of an additively manufactured stainless steel 316L lattice. We also characterized the nature of residual surface particles found after these processes via energy-dispersive X-ray spectroscopy. Finally, we measured the surface roughness of the post-processing lattices via digital microscopy. The native lattices had a predictably high surface roughness from partially molten particles. Sandblasting effectively removed this but damaged the surface, introducing a peel-off layer, as well as leaving surface residue from the glass beads used. The addition of either abrasive polishing or electropolishing removed the peel-off layer but introduced other surface deficiencies making it more susceptible to corrosion. Finally, when electropolishing was performed after the above processes, there was a significant reduction in residual surface particles. The constitution of the particulate debris as well as the lattice surface roughness following each post-processing method varied, with potential implications for clinical use. The work provides a good base for future development of post-processing methods for additively manufactured stainless steel.

scholarly journals Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

2011 ◽  
Vol 59 (5) ◽  
pp. 2112-2123 ◽  
Author(s):  
Daniel S. Weller ◽  
Vivek K Goyal
Keyword(s):  
Post Processing ◽  
Processing Methods

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

scholarly journals Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

Solar Energy ◽  
2019 ◽  
Vol 191 ◽  
pp. 138-150 ◽  
Author(s):  
Kilian Bakker ◽  
Kirien Whan ◽  
Wouter Knap ◽  
Maurice Schmeits
Keyword(s):  
Solar Radiation ◽  
Post Processing ◽  
Processing Methods

Method to Quantify Flow Reduction in Aneurysmal Cavities of Lateral Wall Aneurysms Produced by Stent Implants Used for Flow Diversion

1995 ◽  
Vol 8 (6) ◽  
pp. 197-200
Author(s):  
N. Fujimura ◽  
M. Ohta ◽  
G. Abdo ◽  
H. Ylmaz ◽  
K.-O Lovblad ◽  
...  
Keyword(s):  
Flow Measurement ◽  
Video Recording ◽  
Digital Camera ◽  
Cerebral Aneurysms ◽  
Lateral Wall ◽  
Post Processing ◽  
Processing Methods ◽  
Flow Reduction ◽  
Comparative Examination ◽  
Quantitative Flow

Stent implants placed across the neck of cerebral aneurysms are capable of reducing aneurysmal flow when coils are not used for filling the aneurysms. It is important to evaluate the effects of flow reduction caused by stent implants used for the treatment of cerebral aneurysms. Subtracted vortex centers path line method (SVC method) is one of the image post processing methods employed for quantitative flow measurement. We developed a modified SVC method by employing Cinematic Angiography (25 frames/s) and digital video recording (30 frames/s) with a commercial digital camera. We successfully compared the flow effectiveness using a tubular silicon model with a sidewall aneurysm. The result suggests that our modified SVC method is useful for a comparative examination of the effect of aneurysmal flow reduction caused by stent implants.

scholarly journals Organic Electronics: Non-conventional Processing and Post-processing Methods for the Nanostructuring of Conjugated Materials for Organic Electronics (Adv. Funct. Mater. 7/2011)

2011 ◽  
Vol 21 (7) ◽  
pp. 1206-1206 ◽  
Author(s):  
Giovanna De Luca ◽  
Wojciech Pisula ◽  
Dan Credgington ◽  
Emanuele Treossi ◽  
Oliver Fenwick ◽  
...  
Keyword(s):  
Organic Electronics ◽  
Post Processing ◽  
Conjugated Materials ◽  
Processing Methods
Sign in / Sign up

Export Citation Format

Share Document