Circumventing the pole problem of reduced lat‐lon grids with local schemes. Part II: Validation experiments

2019 ◽  
Vol 145 (721) ◽  
pp. 1392-1405 ◽  
Author(s):  
Michael R. Glinton ◽  
Pierre Bénard
Keyword(s):  
Local Schemes ◽  
Validation Experiments

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

scholarly journals Interclass Interference Suppression in Multi-Class Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 450
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Na Jiang ◽  
Ran Cheng ◽  
Xianling Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Validation ◽  
Selection Process ◽  
Interference Suppression ◽  
Generalization Ability ◽  
Suppression Effect ◽  
Binary Classifiers ◽  
The One ◽  
Fold Cross Validation ◽  
Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

scholarly journals Genotypic Variation in Nutrient Uptake Requirements of Rice Using the QUEFTS Model

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Tao Sun ◽  
Xin Yang ◽  
Sheng Tang ◽  
Kefeng Han ◽  
Ping He ◽  
...  
Keyword(s):  
Nutrient Uptake ◽  
Field Experiments ◽  
Hybrid Rice ◽  
Genotypic Variation ◽  
Tropical Soils ◽  
Study Data ◽  
Japonica Rice ◽  
Rice Varieties ◽  
Indica Hybrid Rice ◽  
Validation Experiments

Nutrient requirements for single-season rice using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model in China have been estimated in a previous study, which involved all the rice varieties; however, it is unclear whether a similar result can be obtained for different rice varieties. In this study, data were collected from field experiments conducted from 2016 to 2019 in Zhejiang Province, China. The dataset was separated into two parts: japonica/indica hybrid rice and japonica rice. To produce 1000 kg of grain, 13.5 kg N, 3.6 kg P, and 20.4 kg K were required in the above-ground plant dry matter for japonica/indica hybrid rice, and the corresponding internal efficiencies (IEs) were 74.0 kg grain per kg N, 279.1 kg grain per kg P, and 49.1 kg grain per kg K. For japonica rice, 17.6 kg N, 4.1 kg P, and 23.0 kg K were required to produce 1000 kg of grain, and the corresponding IEs were 56.8 kg grain per kg N, 244.6 kg grain per kg P, and 43.5 kg grain per kg K. Field validation experiments indicated that the QUEFTS model could be used to estimate nutrient uptake of different rice varieties. We suggest that variety should be taken into consideration when estimating nutrient uptake for rice using the QUEFTS model, which would improve this model.

First validation experiments of the prototype solid state RF system for IFMIF-DONES

2021 ◽  
Vol 168 ◽  
pp. 112396
Author(s):  
Cristina de la Morena ◽  
David Regidor ◽  
Daniel Iriarte ◽  
Francisco Sierra ◽  
Eduardo Ugarte ◽  
...  
Keyword(s):  
Solid State ◽  
Rf System ◽  
Validation Experiments

A Novel Gap Narrowing Process for Creating High Aspect Ratio Transduction Gaps for MEM HF Resonators

2008 ◽  
Vol 1139 ◽  
Author(s):  
Steve Stoffels ◽  
George Bryce ◽  
Rita Van Hoof ◽  
Bert Du Bois ◽  
Robert Mertens ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Silicon Germanium ◽  
High Frequency ◽  
Air Gaps ◽  
Processing Step ◽  
Process Methodology ◽  
Thick Layers ◽  
Validation Experiments ◽  
Structural Layer

AbstractIn this work a novel technique to create nanometer sized air gaps for high frequency (HF) mechanical resonators will be presented. The technique is based on the narrowing of initially wide gaps with a conformal “narrowing” layer. The novelty of this technique is that it enables the creation of narrow high-aspect ratio gaps (e.g. 100nm gaps in 10μm thick layers) without the need for complex lithography or high aspect ratio etching. Furthermore, the electrodes and the resonator itself can be patterned in a single processing step. The process methodology will be explained and validation experiments in a silicon-germanium (SiGe) based technology will be shown. This technology uses low temperature (∼450°C) poly silicon-germanium (SiGe) as the structural layer, which can be processed above CMOS, and therefore allows the fabrication of MEM devices above CMOS.

CFD Analysis of the BeVERLI Hill Turbulence Model Validation Experiments

2022 ◽  
Author(s):  
Thomas A. Ozoroski ◽  
Aldo Gargiulo ◽  
Julie E. Duetsch-Patel ◽  
Vignesh Sundarraj ◽  
Christopher J. Roy ◽  
...  
Keyword(s):  
Model Validation ◽  
Turbulence Model ◽  
Cfd Analysis ◽  
Validation Experiments

Simultaneous estimation of simvstatin and piperine in its formulation by UV spectrophotometric method

2021 ◽  
Vol 11 (1) ◽  
pp. 1-4
Author(s):  
Vijayamma G ◽  
Panneerselvam P ◽  
Siddeswari T ◽  
Nithya Kalyani K ◽  
Jeslin ◽  
...  
Keyword(s):  
Spectrophotometric Method ◽  
Black Pepper ◽  
The Body ◽  
Simultaneous Estimation ◽  
Chemical Release ◽  
Standard Solution ◽  
Experimental Findings ◽  
The Brain ◽  
Herbal Formulations ◽  
Validation Experiments

The active ingredient, called piperine, is present in black pepper. The ions are very small so they are easily consumed by the tissue and nervous system, causing the chemical release within the brain. Piperine has been shown to help ease gastrointestinal ailments, help with vomiting, and has the ability to help with inflammation of the body. This explains to us how simvastatin can help expedite piperine in the body. The new, clear, effective, quick, accurate ultraviolet spectrophotometric method has to be validated and developed for the study of simvastatin and piperine in bulk and poly-herbal formulations. Data from validation experiments was tested using methodological techniques. Since processing at a wavelength of 285nm, the standard solution appeared to have a far higher absorbance than at other wavelengths. Normal simvastatin and piperine have been measured in varying amounts, and they make spectrums of overlays. In Beer Law, the concentration (C) of a solvent is plotted against the absorbance (A) from a calibration curve, as a result. A linearity range of between 14and 39μg/mL was observed. The sample was tested by prorating the standard deviation and standard error of the approximate means with the sample size, demonstrating the accuracy and the precision of the methods used in the analysis. Based on the experimental findings, it can be easily inferred that for UV spectrometry estimation of simvastatin and piperine from pharmaceutical intravenous liquid formulation, the proposed method is very simple, fast, accurate, precise, economical and reproducible.

Deformations and stresses prediction of cantilever structures fabricated by selective laser melting process

2021 ◽  
Vol 27 (3) ◽  
pp. 453-464
Author(s):  
Lan Li ◽  
Tan Pan ◽  
Xinchang Zhang ◽  
Yitao Chen ◽  
Wenyuan Cui ◽  
...  
Keyword(s):  
Residual Stress ◽  
Residual Stresses ◽  
Selective Laser Melting ◽  
Thermal Distortion ◽  
Cantilever Beams ◽  
Support Structures ◽  
Laser Melting ◽  
Content Type ◽  
Two Samples ◽  
Validation Experiments

Purpose During the powder bed fusion process, thermal distortion is one big problem owing to the thermal stress caused by the high cooling rate and temperature gradient. For the purpose of avoiding distortion caused by internal residual stresses, support structures are used in most selective laser melting (SLM) process especially for cantilever beams because they can assist the heat dissipation. Support structures can also help to hold the work piece in its place and reduce volume of the printing materials. The mitigation of high thermal gradients during the manufacturing process helps to reduce thermal distortion and thus alleviate cracking, curling, delamination and shrinkage. Therefore, this paper aims to study the displacement and residual stress evolution of SLMed parts. Design/methodology/approach The objective of this study was to examine and compare the distortion and residual stress properties of two cantilever structures, using both numerical and experimental methods. The part-scale finite element analysis modeling technique was applied to numerically analyze the overhang distortions, using the layer-by-layer model for predicting a part scale model. The validation experiments of these two samples were built in a SLM platform. Then average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model. Findings The validation experiments results of average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model. It was found that they matched well with each other. From displacement and residual stress standpoint, by introducing two different support structure, two samples with the same cantilever beam can be successfully printed. In terms of reducing wasted support materials, print time and high surface quality, sample with less support will need less post-processing and waste energy. Originality/value Numerical modeling in this work can be a very useful tool to parametrically study the feasibility of support structures of SLM parts in terms of residual stresses and deformations. It has the capability for fast prediction in the SLMed parts.

scholarly journals Nonmonotonic Generalization Bias of Gaussian Mixture Models

2000 ◽  
Vol 12 (6) ◽  
pp. 1411-1427 ◽  
Author(s):  
Shotaro Akaho ◽  
Hilbert J. Kappen
Keyword(s):  
Symmetry Breaking ◽  
Gaussian Mixture Models ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
General Tendency ◽  
Adaptive Parameters ◽  
Neural Information ◽  
The Difference ◽  
Training Error ◽  
Validation Experiments

Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective number of adaptive parameters increases and the generalization bias decreases. We compute the dependence of the neural information criterion on temperature around the symmetry breaking. Our results are confirmed by numerical cross-validation experiments.

scholarly journals Isothermal Air-Ingress Validation Experiments

2013 ◽  
Vol 181 (1) ◽  
pp. 68-80
Author(s):  
Chang H. Oh ◽  
Eung Soo Kim
Keyword(s):  
Air Ingress ◽  
Validation Experiments
Sign in / Sign up

Export Citation Format

Share Document