A New Method for Determining Model Parameters of the k-Order Trend Curve

2019 ◽  
Vol 08 (08) ◽  
pp. 1384-1391
Author(s):  
凯 左
Keyword(s):  
New Method ◽  
Model Parameters ◽  
Trend Curve

scholarly journals Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Siyu Ji ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Combined Cycle ◽  
Training Data ◽  
New Method ◽  
Model Parameters ◽  
Evaluation Function ◽  
Data Set ◽  
Standard Data ◽  
True Value ◽  
The Mean

Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set.

Evaluation of a Statistically Targeted Forcing Method for Synthetic Turbulence Generation in Large-Eddy Simulations and Hybrid RANS-LES Simulations

2020 ◽  
Author(s):  
Olalekan O. Shobayo ◽  
D. Keith Walters
Keyword(s):  
Turbulent Flow ◽  
Momentum Equation ◽  
New Method ◽  
Navier Stokes ◽  
Model Parameters ◽  
Mean Velocity ◽  
Spectra Analysis ◽  
Large Eddy ◽  
Mesh Resolution ◽  
Turbulence Generation

Abstract Computational fluid dynamics (CFD) results are presented for synthetic turbulence generation by a proposed statistically targeted forcing (STF) method. The new method seeks to introduce a fluctuating velocity field with a distribution of first and second moments that match a user-specified target mean velocity and Reynolds stress tensor, by incorporating deterministic time-dependent forcing terms into the momentum equation for the resolved flow. The STF method is formulated to extend the applicability of previously documented methods and provide flexibility in regions where synthetic turbulence needs to be generated or damped, for use in engineering level large-eddy and hybrid large-eddy/Reynolds-averaged Navier-Stokes CFD simulations. The objective of this study is to evaluate the performance of the proposed STF method in LES simulations of isotropic and anisotropic homogeneous turbulent flow test cases. Results are interrogated and compared to target statistical velocity and turbulent stress distributions and evaluated in terms of energy spectra. Analysis of the influence of STF model parameters, mesh resolution, and LES subgrid stress model on the results is investigated. Results show that the new method can successfully reproduce desired statistical distributions in a homogeneous turbulent flow.

Physics-Driven Machine-Learning-Based Borehole Sonic Interpretation in the Presence of Casing and Drillpipe

2021 ◽  
pp. 1-15
Author(s):  
Lin Liang ◽  
Ting Lei ◽  
Adam Donald ◽  
Matthew Blyth
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Arrival Time ◽  
New Method ◽  
Time Range ◽  
Model Parameters ◽  
Traditional Methods ◽  
Steel Pipes ◽  
Dispersion Data ◽  
Logging While Drilling

Summary Interpretation of sonic data acquired by a logging-while-drilling (LWD) tool or wireline tool in cased holes is complicated by the presence of drillpipe or casing because those steel pipes can act as a strong waveguide. Traditional solutions, which rely on using a frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals coexist in the same frequency band or arrival-time range. We hence developed a physics-driven machine-learning-based method to overcome the challenge. In this method, two synthetic databases are generated from a general root-findingmode-search routine on the basis of two assumed models: One is defined as a cemented cased hole for a wireline scenario, and the other is defined as a steel pipe immersed in a fluid-filled borehole for the logging-while-drilling scenario. The synthetic databases are used to train neural network models, which are first used to perform global sensitivity analysis on all relevant model parameters so that the influence of each parameter on the dipole dispersion data can be well understood. A least-squares inversion scheme using the trained model was developed and tested on synthetic cases. The scheme showed good results, and a reasonable uncertainty estimate was made for each parameter. We then extended the application of the trained model to develop a method for automated labeling and extraction of the dipole flexural dispersion mode from other disturbances. The method combines the clustering technique with the neural-network-model-based inversion and an adaptive filter. Testing on field data demonstrates that the new method is superior to traditional methods because it introduces a mechanism from which unwanted pipe mode can be physically filtered out. This novel physics-driven machine-learning-based method improved the interpretation of sonic dipole dispersion data to cope with the challenge brought by the existence of steel pipes. Unlike data-driven machine learning methods, it can provide global service with just one-time offline training. Compared with traditional methods, the new method is more accurate and reliable because the processing is confined by physical laws. This method is less dependent on input parameters; hence, a fully automated solution could be achieved.

A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model

2014 ◽  
Vol 977 ◽  
pp. 349-352 ◽  
Author(s):  
Gang Yu ◽  
Jian Kang
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Stable Distribution ◽  
Estimation Algorithm ◽  
Rotating Machinery ◽  
Parameters Estimation ◽  
New Method ◽  
Distribution Model ◽  
Model Parameters ◽  
Alpha Stable Distribution

As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.

scholarly journals One New Method on ARMA Model Parameters Estimation

2009 ◽  
Vol 3 (5) ◽  
Author(s):  
Xiaoqin Cao ◽  
Rui Shan ◽  
Jing Fan ◽  
Peiliang Li
Keyword(s):  
Arma Model ◽  
Parameters Estimation ◽  
New Method ◽  
Model Parameters

On the Research of Identification Methods for Supercapacitor Model Parameters

2015 ◽  
Vol 740 ◽  
pp. 499-502
Author(s):  
Qi Li ◽  
Wen Bin Zhang ◽  
Ping Li ◽  
Shi Su ◽  
Yu Ting Yan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Equivalent Circuit Model ◽  
Simulation System ◽  
New Method ◽  
Model Parameters ◽  
Identification Methods ◽  
Simulation Results ◽  
Charge Discharge ◽  
Constant Charge

The accuracy of model becomes increasingly demanding in the simulation system with the development of supercapacitor. The traditional methods of parameters identification in supercapacitor modeling are very complicated. This paper presents an easy and new method by the Simulink tool. The experimental data for the identification of the model parameters was obtained through the constant charge-discharge experiments. Then the variable capacitor of the supercapacitor equivalent circuit model was modeled in Simscape Language, so parameters directly got with Parameter estimation in Sumulink. Simulation results were presented and compared experimental data. And the result showed that the new method was not only speeded the identification of parameters, but also improved the modeling precision up to 98%.

scholarly journals A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets

2020 ◽  
Vol 36 (1) ◽  
pp. 89-115 ◽  
Author(s):  
Harvey Goldstein ◽  
Natalie Shlomo
Keyword(s):  
Random Noise ◽  
Secondary Analysis ◽  
New Method ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Data Set ◽  
Second Stage ◽  
True Model ◽  
Consistent Parameter

AbstractThe requirement to anonymise data sets that are to be released for secondary analysis should be balanced by the need to allow their analysis to provide efficient and consistent parameter estimates. The proposal in this article is to integrate the process of anonymisation and data analysis. The first stage uses the addition of random noise with known distributional properties to some or all variables in a released (already pseudonymised) data set, in which the values of some identifying and sensitive variables for data subjects of interest are also available to an external ‘attacker’ who wishes to identify those data subjects in order to interrogate their records in the data set. The second stage of the analysis consists of specifying the model of interest so that parameter estimation accounts for the added noise. Where the characteristics of the noise are made available to the analyst by the data provider, we propose a new method that allows a valid analysis. This is formally a measurement error model and we describe a Bayesian MCMC algorithm that recovers consistent estimates of the true model parameters. A new method for handling categorical data is presented. The article shows how an appropriate noise distribution can be determined.

scholarly journals Bayesian Analysis of Evolutionary Divergence with Genomic Data Under Diverse Demographic Models

2016 ◽  
Author(s):  
Yujin Chung ◽  
Jody Hey
Keyword(s):  
Markov Chain ◽  
Dna Sequences ◽  
Simulated Data ◽  
Genomic Data ◽  
New Method ◽  
Model Parameters ◽  
Evolutionary Divergence ◽  
Posterior Density ◽  
Demographic Models ◽  
Pan Troglodytes Troglodytes

AbstractWe present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation with Migration framework. For the Markov chain Monte Carlo (MCMC) phase of the analysis, we use a reduced state space, consisting of simple coalescent trees without migration paths, and a simple importance sampling distribution without demography. Migration paths are analytically integrated using a Markov chain as a representation of genealogy. The new method is scalable to a large number of loci with excellent MCMC mixing properties. Once obtained, a single sample of trees is used to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. As implemented in the computer program MIST, we demonstrate the accuracy, scalability and other advantages of the new method using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes troglodytes (P. t.) and P. t. verus.

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