Frequency-domain validation test for uncertainty models having white noise and unmodeled dynamics

2002 ◽  
Author(s):  
H. Fukushima ◽  
T. Sugie
Keyword(s):  
White Noise ◽  
Frequency Domain ◽  
Unmodeled Dynamics ◽  
Validation Test ◽  
Uncertainty Models

CHARACTERIZATION OF NUCLEAR IMAGES BASED ON ANALYSIS OF FRACTIONAL SUMMATION SYSTEMS

Fractals ◽  
2004 ◽  
Vol 12 (02) ◽  
pp. 157-169 ◽  
Author(s):  
HAI-SHAN WU ◽  
ANDREW J. EINSTEIN ◽  
LIANE DELIGDISCH ◽  
TAMARA KALIR ◽  
JOAN GIL
Keyword(s):  
White Noise ◽  
Frequency Domain ◽  
Discrete System ◽  
Fractional Integral ◽  
Continuous System ◽  
Time Range ◽  
System Function ◽  
Autocovariance Function ◽  
Frequency Domain Methods

While frequency-based methods for the characterization of fractals are popular and effective in many applications, they have limitations when applied to irregularly shaped images, such as nuclear images. The irregularity renders texture characterization by frequency domain methods, based upon Fourier transform, problematic. To address this situation, this paper presents an algorithm based upon the signal analysis in the spatial domain. An autocovariance function can be estimated regardless of the shape and size of regions where the image is defined. As in the continuous fractional Brownian motion (FBM) that results from inputting white noise into a specific fractional integral system, a discrete FBM can be related to white noise by a specific fractional summation system (FSS) that is linear, causal and shift-invariant. Although the method of direct sampling is not valid for converting a continuous fractional integral to a discrete fractional summation, discrete fractional summations similar to the sampled system functions can be obtained through an iterative process. While the continuous system function of a fractional integral is linear in the frequency domain when plotted in log-log scales, unfortunately, it is not true for the comparable discrete system function. The discrete system function is actually approximately linear in the log-log scales over a very limited range. The slope of the straight line that approximates the function curve in the mean-square-error (MSE) sense in a specific time range provides a description of the autocovariance function that reveals the statistical relations among the local textures. Applications to characterization of ovary nuclear images in groups of normal, atypical and cancer cases are studied and presented.

Identification of a Driver Steering Model, and Model Uncertainty, From Driving Simulator Data

2001 ◽  
Vol 123 (4) ◽  
pp. 623-629 ◽  
Author(s):  
Liang-Kuang Chen ◽  
A. Galip Ulsoy
Keyword(s):  
Driving Simulator ◽  
Driver Model ◽  
Unmodeled Dynamics ◽  
Active Safety ◽  
Control Loop ◽  
Structured Uncertainty ◽  
Active Safety Systems ◽  
Safety Systems ◽  
Uncertainty Models ◽  
Adaptation Scheme

For active safety systems that function while the driver is still in the control loop, driver uncertainty can affect system performance significantly. In this paper, an approach to obtain both the driver model and its uncertainty from driving simulator data is presented. The structured uncertainty is used to represent the driver’s time-varying behavior, and the unstructured uncertainty for unmodeled dynamics. The uncertainty models can represent both the uncertainty within one driver and the uncertainty across multiple drivers. The structured uncertainty suggests that an estimation and adaptation scheme might be applicable for the design of controllers for active safety systems.

scholarly journals Mathematical model of parametrically controlled matched filters

2017 ◽  
Vol 1 (1) ◽  
pp. 27-31
Author(s):  
Borislav Georgiev Naydenov ◽  
Antim Hristov Yordanov ◽  
Lyubomir Petrov Kamburov
Keyword(s):  
Mathematical Model ◽  
White Noise ◽  
Frequency Domain ◽  
Mobile Communication ◽  
Communication Systems ◽  
Noise Resistance ◽  
Matched Filters ◽  
Variable Parameters ◽  
Radar Systems ◽  
Auto Correlation

A one model of parametrically controlled coherent filters is described and analyzed, applied also in radar systems and mobile communication systems to improve noise resistance. Application of the Nyquist-Shannon theorem in the frequency domain to obtain a set of frequency filters with variable parameters. The conversion of the signal at the output of the parameter filter using the auto correlation feature is shown when a normal white noise occurs.

A DSP-Based Design for Generating Limited White Noise Wave

2010 ◽  
Vol 34-35 ◽  
pp. 1228-1232
Author(s):  
Ming Ma ◽  
Run Jie Shen ◽  
Bing Fang ◽  
Ming Wei Sun ◽  
Wen He
Keyword(s):  
White Noise ◽  
Frequency Domain ◽  
Simulation Experiment ◽  
Amplitude Spectrum ◽  
Random Numbers ◽  
Zero Section ◽  
Signal Source ◽  
External Data ◽  
Noise Simulation ◽  
Data Memory

In this paper, based on the analysis of white noise generation, equably distributed pseudo-random numbers are generated by a method of mixed congruency. In the case that the pseudo-random numbers are taken as the phase spectrum and the amplitude spectrum is equally set, the waveform array of frequency-domain is made up. The array is transformed from frequency-domain to time-domain by the IFFT before stored into the external data memory. Based on the wave generating technology of DDS, the timer interruption of DSP2812 is installed to change the frequency for generating wave .Then the number is taken out from the external memory. The limited white noise is formed by the way of D/A and zero-section maintenance .This generator can be used as signal source in the noise simulation experiment.

On the Use of the White-Noise Approximation for Modelling the Slow-Drifts of a FOWT: An Example Using FAST

2018 ◽  
Author(s):  
Alexandre N. Simos ◽  
Lucas Henrique Souza do Carmo ◽  
Ewerton Carlos Camargo
Keyword(s):  
White Noise ◽  
Frequency Domain ◽  
Time Domain ◽  
Significant Loss ◽  
Second Order ◽  
Preliminary Design ◽  
Original Formulation ◽  
Slow Drift ◽  
Wave Induced

When designing the mooring system of a floating unit, performing extensive time-domain simulations in several sea conditions is common practice. For this, the second-order wave induced forces, expressed by QTF matrices, are most often precomputed in frequency domain diffraction codes. However, the computation of the full QTFs is quite demanding and it is also not uncommon for the designer to be in doubt as to the frequency limits and resolution required for their construction. Among the approximations that can be used to ease this burden, the most well-known is Newman’s approximation, which performs quite well as long as the natural periods of drift and the water depth are sufficiently large. The white noise approach, on the other hand, leads to an approximation of a different kind. Taking advantage of the fact that the slow-drift response is narrow-banded, it approximates the second-order force spectrum where it contributes the most, and in a way that is independent of the natural periods and depth. However, its original formulation, based on the force spectra, is certainly more convenient in frequency domain. This article presents an easy way to make use of the white noise approach in time domain simulations. For this, the well-known OC4 semi-submersible FOWT is taken as a case-study. Simulations in different wave conditions are performed with the software FAST using both, the original full QTFs and new ones, simplified according to the principle of the white noise approximation. It is shown that, with the latter, the simulations can be performed without significant loss of accuracy, indicating that the white-noise approach indeed is an interesting option for preliminary design stages.

Frequency Domain Design for Robust Performance Under Parametric, Unstructured, or Mixed Uncertainties

1993 ◽  
Vol 115 (2B) ◽  
pp. 439-451 ◽  
Author(s):  
Suhada Jayasuriya
Keyword(s):  
Frequency Domain ◽  
Design Methodology ◽  
Linear Time ◽  
Mathematical Framework ◽  
Robust Performance ◽  
Linear Time Invariant ◽  
Loop Transfer Function ◽  
Uncertainty Models ◽  
Time Invariant ◽  
Mixed Uncertainties

This article looks at direct frequency domain design for satisfying robust performance objectives in uncertain, linear time invariant (LTI) plants embedded in a single feedback loop. The uncertain plants may be described by parametric, nonparametric (or unstructured), or mixed uncertain models. Quantitative Feedback Theory (QFT) is one frequency domain design methodology that is direct and is equally effective with any of these models. It can be separated from other frequency domain robust control methods such as H∞ optimal control, μ synthesis, and LQG/LTR for at least (i) its emphasis on cost of feedback measured in terms of controller bandwidth, (ii) its ability to deal nonconservatively with parametric, nonparametric and mixed uncertainty models, and (iii) its utilization of both amplitude and phase of the loop transfer function, pointwise in frequency, for the quantification of robust performance. An exposition of these attributes, unique to QFT, and the basic design methodology, coupled with a recently developed mathematical framework and some existence results for the standard single-loop QFT problem are the salient features of this paper.

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