Dynamic Model Validation Metric Based on Wavelet Thresholded Signals

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Andrew D. Atkinson ◽  
Raymond R. Hill ◽  
Joseph J. Pignatiello ◽  
G. Geoffrey Vining ◽  
Edward D. White ◽  
...  
Keyword(s):  
Model Validation ◽  
Development Process ◽  
Experimental System ◽  
Wavelet Thresholding ◽  
Transient Phase ◽  
Validation Process ◽  
System Data ◽  
Wavelet Decompositions ◽  
Automobile Crash ◽  
Specific Concern

Model validation is a vital step in the simulation development process to ensure that a model is truly representative of the system that it is meant to model. One aspect of model validation that deserves special attention is when validation is required for the transient phase of a process. The transient phase may be characterized as the dynamic portion of a signal that exhibits nonstationary behavior. A specific concern associated with validating a model's transient phase is that the experimental system data are often contaminated with noise, due to the short duration and sharp variations in the data, thus hiding the underlying signal which models seek to replicate. This paper proposes a validation process that uses wavelet thresholding as an effective method for denoising the system and model data signals to properly validate the transient phase of a model. This paper utilizes wavelet thresholded signals to calculate a validation metric that incorporates shape, phase, and magnitude error. The paper compares this validation approach to an approach that uses wavelet decompositions to denoise the data signals. Finally, a simulation study and empirical data from an automobile crash study illustrates the advantages of our wavelet thresholding validation approach.

Advances in standardizing and validating research techniques for pathogen monitoring in water

2004 ◽  
Vol 4 (2) ◽  
pp. 23-30
Author(s):  
K. Connell ◽  
M. Pope ◽  
K. Miller ◽  
J. Scheller ◽  
J. Pulz
Keyword(s):  
Validation Studies ◽  
Development Process ◽  
Monitoring Methods ◽  
Microbiological Method ◽  
Validation Process ◽  
Method Performance ◽  
Target Analytes ◽  
Interlaboratory Validation ◽  
Quality Control Criteria ◽  
Control Criteria

Designing and conducting standardized microbiological method interlaboratory validation studies is challenging because most methods are manual, rather than instrument-based, and results from the methods are typically subjective. Determinations of method recovery, in particular, are problematic, due to difficulties in assessing the true spike amount. The standardization and validation process used for the seven most recent USEPA 1600-series pathogen monitoring methods has begun to address these challenges. A staged development process was used to ensure that methods were adequately tested and standardized before resources were dedicated to interlaboratory validation. The interlaboratory validation studies for USEPA Method 1622, for Cryptosporidium, USEPA Method 1601 for coliphage, and USEPA Method 1605 for Aeromonas assessed method performance using different approaches, due the differences in the nature of the target analytes and the data quality needs of each study. However, the use of enumerated spikes in all of the studies allowed method recovery and precision to be assessed, and also provided the data needed to establish quantitative quality control criteria for the methods.

The Eye of the Beholder: A Case Example of Changing Clients' Perspectives Through Involvement in the Model Validation Process

2015 ◽  
Vol 32 (4) ◽  
pp. 437-449 ◽  
Author(s):  
Lambertus P. J. van Nistelrooij ◽  
Etiënne A.J.A. Rouwette ◽  
Ilse M. Verstijnen ◽  
Jac A.M. Vennix
Keyword(s):  
Model Validation ◽  
Validation Process

scholarly journals Validação de Modelos Internos no Brasil: Análise de Metodologias de Backtesting de VaR

2006 ◽  
Vol 4 (1) ◽  
pp. 97
Author(s):  
Alan Cosme Rodrigues da Silva ◽  
Claudio Henrique Da Silveira Barbedo ◽  
Gustavo Silva Araújo ◽  
Myrian Beatriz Eiras das Neves
Keyword(s):  
Low Power ◽  
Model Validation ◽  
Internal Model ◽  
Real Data ◽  
Small Samples ◽  
Limited Data ◽  
Traffic Light ◽  
Validation Process ◽  
Data Set ◽  
Kuiper Test

The purpose of this paper is to analyze backtesting methodologies of VaR, focusing on aspects as suitability to volatile markets and limited data set. We verify, from regulatory standpoint, tests to complement the Basel traffic light results, using simulated and real data. The results indicate that tests based on failures proportion are not adequate for small samples even fro 1,000 observations. The Basel criterion is conservative and has low power, which does not invalidate its application, as the criterion is only one of the procedures adopted in internal model validation process. Thus, it is suggested using tests that capture the shape of returns distribution, as the Kuiper test, in addition to the Basel criterion.

Research of Experimental System for Pressure Reducing Valve Performance and Life Test

2012 ◽  
Vol 588-589 ◽  
pp. 129-133 ◽  
Author(s):  
Fei Wang ◽  
Tao Tao Zhang ◽  
Zhi Jun Zou ◽  
Hao Li
Keyword(s):  
Control System ◽  
Experimental System ◽  
Flow Coefficient ◽  
Life Test ◽  
Computer Communication ◽  
Electrical Control ◽  
Test Pressure ◽  
System Data ◽  
Test Flow ◽  
Pressure Reducing Valve

This paper addressed the completed researching and developing procedure for the whole system with below parts： The pipe part of the whole system which according to the principle of the pressure reducing valve and the related standard; The electrical control system, adjusting control system, data collection system; We simulated customer’s operating on the valve based on PLC controlling the action of the Solenoid Valve which achieved the goal for life test .Meanwhile the paper introduced PPI protocol with which PLC computer communication each other. The experimental data of current capacity test, pressure characteristic test, flow coefficient (Cv) test and life test show the performance of the whole system is good.

scholarly journals Validation of computational models in biomechanics

2009 ◽  
Vol 224 (7) ◽  
pp. 801-812 ◽  
Author(s):  
H B Henninger ◽  
S P Reese ◽  
A E Anderson ◽  
J A Weiss
Keyword(s):  
Fluid Mechanics ◽  
Model Validation ◽  
Patient Outcomes ◽  
Basic Science ◽  
Computational Models ◽  
Peer Acceptance ◽  
Biological Systems ◽  
Verification And Validation ◽  
Validation Process ◽  
Computational Biomechanics

The topics of verification and validation have increasingly been discussed in the field of computational biomechanics, and many recent articles have applied these concepts in an attempt to build credibility for models of complex biological systems. Verification and validation are evolving techniques that, if used improperly, can lead to false conclusions about a system under study. In basic science, these erroneous conclusions may lead to failure of a subsequent hypothesis, but they can have more profound effects if the model is designed to predict patient outcomes. While several authors have reviewed verification and validation as they pertain to traditional solid and fluid mechanics, it is the intent of this paper to present them in the context of computational biomechanics. Specifically, the task of model validation will be discussed, with a focus on current techniques. It is hoped that this review will encourage investigators to engage and adopt the verification and validation process in an effort to increase peer acceptance of computational biomechanics models.

scholarly journals MODERN METHODS OF PRODUCT DEVELOPMENT IN THE FACE OF MARKET VOLATILITY

2021 ◽  
Vol 9 (9) ◽  
pp. 271-275
Author(s):  
E. A. Morev
Keyword(s):  
Product Development ◽  
Development Process ◽  
World Market ◽  
Business Environment ◽  
Market Volatility ◽  
Product Development Process ◽  
Validation Process ◽  
The World ◽  
Modern Methods ◽  
The Face

This paper examines modern methods and approaches to product development in the context of the volatility of the world market. To achieve maximum customer focus and successful delivery of their products, services and services, more and more companies are focusing on flexible approaches to product development. The changing business environment and the emergence of new mechanisms of interaction with customers lead to the need to create approaches such as "Lean Startup". Its core principle is a validation process in each product development process, which will increase the ability to create a better product and reduce the time spent on the path from development to market.

Model Validation

2009 ◽  
pp. 231-241
Author(s):  
Ben Kei Daniel
Keyword(s):  
Social Capital ◽  
Sensitivity Analysis ◽  
Model Validation ◽  
Real World ◽  
Bayesian Model ◽  
Computational Models ◽  
Lessons Learned ◽  
Practical Application ◽  
Validation Process ◽  
The World

Though computational models take a lot of effort to build, a model is generally not useful unless it can help people to understand the world being modelled, or the problem the model is intended to solve. A useful model allows people to make useful predictions about how the world will behave now and possibly tomorrow. Validation is the last step required in developing a useful Bayesian model. The goal of validation is to gain confidence in a model and to demonstrate and prove that a model produces reliable results that are closely related to the problems or issues in which the model is intended to address. The goal of the Chapter is to provide the reader with a basic understanding of the validation process and to share with them key lessons learned from the model of social capital presented in the book. While sensitivity analysis is intended to ensure that a Bayesian model is theoretically consistent with goals and assumptions of the modeller (how the modeller views the world) or the accuracy of sources of data used for building the model, the goal of validation is to demonstrate the practical application of the model in real world settings. This Chapter presents the main steps involved in the process of validating a Bayesian model. It illustrates this process by using examples drawn from the Bayesian model of social capital.

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