Multidimensional system identification through blind deconvolution

1990 ◽  
Vol 1 (2) ◽  
pp. 127-142 ◽  
Author(s):  
R. H. T. Bates ◽  
Hong Jiang ◽  
B. L. K. Davey
Keyword(s):  
System Identification ◽  
Blind Deconvolution ◽  
Multidimensional System

scholarly journals Multidimensional System Identification and Active Vibration Control of a Piezoelectric-Based Sting System Used in Wind Tunnel

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi Yu ◽  
Xing Shen ◽  
Yun Huang
Keyword(s):  
Control System ◽  
System Identification ◽  
Wind Tunnel ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Low Frequency ◽  
Vibration Monitoring ◽  
Multidimensional System ◽  
Monitoring Method ◽  
Active Vibration

In wind tunnel tests, the cantilever sting is usually used to support aircraft models because of its simple structure and low aerodynamic interference. However, in some special conditions, big-amplitude and low-frequency vibration would occur easily on the model not only in the pitch direction but also in the yaw direction, resulting in inaccurate data and even damage of the supporting structure. In this paper, aiming at suppressing the vibration in pitch and yaw plane, a multidimensional system identification and active vibration control system on the basis of piezoelectric actuators is established. A vibration monitoring method based on the strain-displacement transformation (SDT) matrix is proposed, which can transform strain signals into vibration displacements. The system identification based on chirp-Z transform (CZT) is applied to improve the adaptability and precision of the building process for the system model. After that, the hardware platform as well as the software control system based on the classical proportional-derivative (PD) algorithm is built. A series of experiments are carried out, and the results show the exactness of the vibration monitoring method. The system identification process is completed, and the controller is designed. Vibration control experiments verify the effectiveness of the controller, and the results indicate that vibrations in pitch and yaw directions are attenuated apparently. The spectrum power is reduced over 14.8 dB/Hz, which prove that the multidimensional identification and active vibration control system has the capability to decline vibration from different directions.

scholarly journals Comparing a distributed parameter model-based system identification technique with more conventional methods for inverse problems

2019 ◽  
Vol 27 (5) ◽  
pp. 703-717 ◽  
Author(s):  
Jian Li ◽  
Susan E. Luczak ◽  
I. G. Rosen
Keyword(s):  
System Identification ◽  
Inverse Problems ◽  
Blind Deconvolution ◽  
Alcohol Concentration ◽  
Distributed Parameter ◽  
Statistical Measures ◽  
Distributed Parameter Model ◽  
Frequency Domain Methods ◽  
Identification Technique ◽  
Time Series Approach

Abstract Three methods for the estimation of blood or breath alcohol concentration (BAC/BrAC) from biosensor measured transdermal alcohol concentration (TAC) are evaluated and compared. Specifically, we consider a system identification/quasi-blind deconvolution scheme based on a distributed parameter model with unbounded input and output for ethanol transport in the skin and compare it to two more conventional system identification and filtering/deconvolution techniques for ill-posed inverse problems, one based on frequency domain methods and the other on a time series approach using an ARMA input/output model. Our basis for comparison are five statistical measures of interest to alcohol researchers and clinicians: peak BAC/BrAC, time of peak BAC/BrAC, the ascending and descending slopes of the BAC/BrAC curve, and the area underneath the BAC/BrAC curve.

A Community Based High Risk Register for Hearing Loss

1982 ◽  
Vol 47 (4) ◽  
pp. 373-375 ◽  
Author(s):  
James L. Fitch ◽  
Thomas F. Williams ◽  
Josephine E. Etienne
Keyword(s):  
Hearing Loss ◽  
System Identification ◽  
High Risk ◽  
Language Learning ◽  
Education Program ◽  
Communication System ◽  
Auditory Signal ◽  
Early Age ◽  
Community Based ◽  
Children With Hearing Loss

The critical need to identify children with hearing loss and provide treatment at the earliest possible age has become increasingly apparent in recent years (Northern & Downs, 1978). Reduction of the auditory signal during the critical language-learning period can severely limit the child's potential for developing a complete, effective communication system. Identification and treatment of children having handicapping conditions at an early age has gained impetus through the Handicapped Children's Early Education Program (HCEEP) projects funded by the Bureau of Education for the Handicapped (BEH).

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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