scholarly journals A review of flux identification methods for models of sedimentation

2020 ◽  
Vol 81 (8) ◽  
pp. 1715-1722 ◽  
Author(s):  
R. Bürger ◽  
J. Careaga ◽  
S. Diehl
Keyword(s):  
Partial Differential Equations ◽  
Traditional Method ◽  
Synthetic Data ◽  
Single Experiment ◽  
Hindered Settling ◽  
Identification Methods ◽  
Flux Function ◽  
New Methods ◽  
Experimental Errors ◽  
Flux Curve

Abstract Most models of sedimentation contain the nonlinear hindered-settling flux function. If one assumes ideal conditions and no compression, then there exist several theoretically possible ways of identifying a large portion of the flux function from only one experiment by means of formulas derived from the theory of solutions of partial differential equations. Previously used identification methods and recently published such, which are based on utilizing conical vessels or centrifuges, are reviewed and compared with synthetic data (simulated experiments). This means that the identification methods are evaluated from a theoretical viewpoint without experimental errors or difficulties. The main contribution of the recent methods reviewed is that they, in theory, can identify a large portion of the flux function from a single experiment, in contrast to the traditional method that provides one point on the flux curve from each test. The new methods lay the foundation of rapid flux identification; however, experimental procedures need to be elaborated.

Directional observation on lipid of male nectary of Lindera megaphylla hemsl. by the rapid embedding method with polyethylene glycol (PEG)

1990 ◽  
Vol 48 (3) ◽  
pp. 718-719
Author(s):  
Dai Dalin ◽  
Guo Jianmin
Keyword(s):  
Electron Microscope ◽  
Polyethylene Glycol ◽  
Chemical Reaction ◽  
Traditional Method ◽  
Osmium Tetroxide ◽  
Unsaturated Lipid ◽  
Embedding Method ◽  
Long Period ◽  
New Methods

Lipid cytochemistry has not yet advanced far at the EM level. A major problem has been the loss of lipid during dehydration and embedding. Although the adoption of glutaraldehyde and osmium tetroxide accelerate the chemical reaction of lipid and osmium tetroxide can react on the double bouds of unsaturated lipid to from the osmium black, osmium tetroxide can be reduced in saturated lipid and subsequently some of unsaturated lipid are lost during dehydration. In order to reduce the loss of lipid by traditional method, some researchers adopted a few new methods, such as the change of embedding procedure and the adoption of new embedding media, to solve the problem. In a sense, these new methods are effective. They, however, usually require a long period of preparation. In this paper, we do research on the fiora nectary strucure of lauraceae by the rapid-embedding method wwith PEG under electron microscope and attempt to find a better method to solve the problem mentioned above.

scholarly journals Estimating the hindered-settling flux function from a batch test in a cone

2018 ◽  
Vol 192 ◽  
pp. 244-253 ◽  
Author(s):  
Raimund Bürger ◽  
Julio Careaga ◽  
Stefan Diehl ◽  
Ryan Merckel ◽  
Jesús Zambrano
Keyword(s):  
Batch Test ◽  
Hindered Settling ◽  
Flux Function

A Multiclass Classification Method Based on Decoding of Binary Classifiers

2009 ◽  
Vol 21 (7) ◽  
pp. 2049-2081 ◽  
Author(s):  
Takashi Takenouchi ◽  
Shin Ishii
Keyword(s):  
Probabilistic Models ◽  
Experimental Studies ◽  
Synthetic Data ◽  
Multiclass Classification ◽  
Data Sets ◽  
Classification Methods ◽  
Boltzmann Machine ◽  
Data Set ◽  
New Methods ◽  
Binary Classifiers

In this letter, we present new methods of multiclass classification that combine multiple binary classifiers. Misclassification of each binary classifier is formulated as a bit inversion error with probabilistic models by making an analogy to the context of information transmission theory. Dependence between binary classifiers is incorporated into our model, which makes a decoder a type of Boltzmann machine. We performed experimental studies using a synthetic data set, data sets from the UCI repository, and bioinformatics data sets, and the results show that the proposed methods are superior to the existing multiclass classification methods.

scholarly journals A hierarchical Bayesian approach for calibration of stochastic material models

2021 ◽  
Vol 2 ◽  
Author(s):  
Nikolaos Papadimas ◽  
Timothy Dodwell
Keyword(s):  
Synthetic Data ◽  
Material Model ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Experiment Data ◽  
Single Experiment ◽  
Inference Problem ◽  
Material Models ◽  
3D Printed ◽  
Approximate Likelihood

Abstract This article recasts the traditional challenge of calibrating a material constitutive model into a hierarchical probabilistic framework. We consider a Bayesian framework where material parameters are assigned distributions, which are then updated given experimental data. Importantly, in true engineering setting, we are not interested in inferring the parameters for a single experiment, but rather inferring the model parameters over the population of possible experimental samples. In doing so, we seek to also capture the inherent variability of the material from coupon-to-coupon, as well as uncertainties around the repeatability of the test. In this article, we address this problem using a hierarchical Bayesian model. However, a vanilla computational approach is prohibitively expensive. Our strategy marginalizes over each individual experiment, decreasing the dimension of our inference problem to only the hyperparameter—those parameter describing the population statistics of the material model only. Importantly, this marginalization step, requires us to derive an approximate likelihood, for which, we exploit an emulator (built offline prior to sampling) and Bayesian quadrature, allowing us to capture the uncertainty in this numerical approximation. Importantly, our approach renders hierarchical Bayesian calibration of material models computational feasible. The approach is tested in two different examples. The first is a compression test of simple spring model using synthetic data; the second, a more complex example using real experiment data to fit a stochastic elastoplastic model for 3D-printed steel.

scholarly journals SUCCESSFUL TREATMENT OF SEVERE PURULENT PERITONITIS WITH INTRAABDOMINAL HYPERTENSION COMPLICATIONS (CLINICAL CASE)

2021 ◽  
pp. 62-65
Author(s):  
V. V. Grubnik ◽  
Е. А. Koychev ◽  
V.M. Kosovan ◽  
M. M. Chernov
Keyword(s):  
Traditional Method ◽  
Clinical Case ◽  
Open Abdomen ◽  
Abdominal Cavity ◽  
Integrated Approach ◽  
Vac Therapy ◽  
New Methods ◽  
Purulent Peritonitis ◽  
Intraabdominal Hypertension ◽  
Abdominal Compartment

The widely used traditional method of surgical treatment of patients with widespread purulent peritonitis failed to establish itself as universal and has a large number of disadvantages, which prompts the use of new methods of managing patients in the postoperative period in surgical practice. The case described in the work illustrates the possibilities of a successful integrated approach in the treatment of diffuse purulent peritonitis against the background of Abdominal Compartment Syndrome, which includes the «Open abdomen» and «VAC-therapy» techniques, the use of which leads to a persistent decrease in both IАP and relief of the phenomena of purulent inflammation in the abdominal cavity. Conclusions. The use of VAC-therapy in combination with the «Open abdomen» technique leads to a persistent decrease in both ICP and relief of the phenomena of purulent inflammation in the abdominal cavity.

Study on Back-Propagation Neural Networks in Hydrological Forecast

2014 ◽  
Vol 687-691 ◽  
pp. 2153-2156
Author(s):  
Ri Jun Zhang ◽  
Zhong Sheng Li
Keyword(s):  
Neural Networks ◽  
Traditional Method ◽  
Back Propagation ◽  
Projection Pursuit ◽  
Forecasting Model ◽  
Bp Network ◽  
Hydrological Forecasting ◽  
New Methods ◽  
Hydrological Forecast ◽  
Bp Model

The hydrological forecasting model are established respectively by the traditional method and the new methods, BP network and projection pursuit, in order to study the feasibility and practicality. The result shows that the accuracy of the BP model is within 10%, has better forecasting effect and more practical value than the others.

scholarly journals Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series

2015 ◽  
Vol 25 (01) ◽  
pp. 1550013 ◽  
Author(s):  
Ricardo Araújo Rios ◽  
Michael Small ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Time Series ◽  
Gaussian Processes ◽  
Synthetic Data ◽  
Surrogate Data ◽  
Stationary Time Series ◽  
Decomposition Techniques ◽  
High Frequencies ◽  
New Methods ◽  
Linear And Nonlinear ◽  
The Fourier Transform

Surrogate data methods have been widely applied to produce synthetic data, while maintaining the same statistical properties as the original. By using such methods, one can analyze certain properties of time series. In this context, Theiler's surrogate data methods are the most commonly considered approaches. These are based on the Fourier transform, limiting them to be applied only on stationary time series. Consequently, time series including nonstationary behavior, such as trend, produces spurious high frequencies with Theiler's methods, resulting in inconsistent surrogates. To solve this problem, we present two new methods that combine time series decomposition techniques and surrogate data methods. These new methods initially decompose time series into a set of monocomponents and the trend. Afterwards, traditional surrogate methods are applied on those individual monocomponents and a set of surrogates is obtained. Finally, all individual surrogates plus the trend signal are combined in order to create a single surrogate series. Using this method, one can investigate linear and nonlinear Gaussian processes in time series, irrespective of the presence of nonstationary behavior.

Relationship among Soil Surface Roughness, Topsoil Porosity and Rainfall in Tilled Field

2012 ◽  
Vol 195-196 ◽  
pp. 1212-1216 ◽  
Author(s):  
Jian Hui Lin ◽  
Yu Rui Sun ◽  
Hui Juan Zhang ◽  
Peter Schulze Lammers
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Natural Condition ◽  
Traditional Method ◽  
Soil Surface ◽  
Soil Porosity ◽  
Soil Physics ◽  
New Methods ◽  
Soil Surface Roughness ◽  
Over Time

Topsoil porosity (TSP) is an important parameter for the research of soil physics, agriculture and environmental protection. However, the traditional method for measuring porosity is time consuming. Conversely, a series of new methods measuring soil surface roughness (SSR) are increased and become more and more quickly. Some researchers propose to predict TSP by SSR. In this study, two fields cultivated by different tillage type were investigated under natural condition during four years (2006-2009). The results of this study show that (i) both of soil roughness and porosity are decreased over time; (ii) there are strong correlation between soil porosity and roughness effected by rainfall; (iii) after introduce the index of accumulative mean rainfall (AMR), a model of multiple linear regression for presenting the correlation among SSR, TSP and rainfall was built using sampling data of 2006-2009 with R2>0.7.

Sign in / Sign up

Export Citation Format

Share Document