Digital image processing for non-linear system identification

Chung, Hung-Chi; Liang, Jia; Kushiyama, Shigeru; Shinozuka, Masanobu

doi:10.1016/s0020-7462(03)00021-0

Cited by 45 publications

(27 citation statements)

References 3 publications

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“…Numerous investigations have been published on laboratory experiments in which digital cameras or webcams are used [3][4][5][6][7] , optical mice 8,9 and games consoles and their wireless devices which control the games [10][11][12][13][14] . Also the sensors of smartphones are beginning to be used to make precise measurements [15][16][17][18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

Measurement of the magnetic field of small magnets with a smartphone: a very economical laboratory practice for introductory physics courses

et al. 2015

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In this work we propose an inexpensive laboratory practice for the laboratory of an introductory course of Physics for any grade of Sciences and Engineering, which was very well received by our students, where a smartphone (iOS, Android or Windows Phone is irrelevant) is used together with some mini magnets, similar to those that can be found in the door of our refrigerators, a 20 cm long school rule, a paper and a free application (app) for measuring the magnetic field using the magnetic fields sensor or magnetometer of the smartphone, which needs to be downloaded and installed. The apps we have used are: Magnetometer (iOS), Magnetometer Metal Detector and Physics Toolbox Magnetometer (Android). Nothing else is needed. Cost of this practice: 0 coins. The main purpose of the practice is that students determine the dependence of the component x of the magnetic field produced by different magnets (from the typical magnets that are decorated in refrigerators even with a ring magnet and spherical magnet). We have obtained that the dependency of the magnetic field with the distance is of the form x -3 , in total agreement with the theoretical analysis. The secondary objective is to apply the technique of least squares fit to obtain this exponent and the magnetic moment of the magnets, with theirs corresponding absolute error.

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Section: Introductionmentioning

confidence: 99%

Measurement of the magnetic field of small magnets with a smartphone: a very economical laboratory practice for introductory physics courses

et al. 2015

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“…The cameras were calibrated using a calibration plate anchored on a linear guide system with accurate positioning capability. Chung et al 15 used digital image techniques for identifying nonlinear characteristics in systems.…”

Section: Image Analysis and Civil Engineeringmentioning

confidence: 99%

From photogrammetry, computer vision to structural response measurement

Chang

2007

SPIE Proceedings

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Measuring displacement response of large-scale structures is necessary to assess their performance and health condition. Current global position systems (GPS), with a significant cost, can provide point coordinate measurement with an accuracy of ±1cm horizontally and ±2cm vertically at a sampling frequency up to 20 Hz. Photogrammetry is a measurement technology in which the three-dimensional coordinates of points on an object are determined by measurements made with two or more photographic images taken from different positions. This technology is regaining its popularity in various engineering disciplines due to a recent remarkable evolution in the consumer digital cameras. The image resolution of these cameras has increased rapidly from below 1 million pixels a few years ago to over 10 million pixels today, with little increase or even decrease in cost. Image sequences recorded by these cameras contain both spatial and temporal information of the target object; hence can be used to extract the object's dynamic characteristics such as natural frequencies and mode shapes. Research indicates that accuracy in the order of as high as 1 part in 30,000 can be achieved when cameras are properly calibrated in the field and multiple high-resolution images are used. At this level of accuracy, point positions of a 30m object would be accurate to 1mm at 68% probability and responses of large-scale structures can be measured for further meaningful processing and interpretation. In this paper, several image-based sensing techniques that can be used for measuring response of large-scale structures are presented. These techniques are developed based on some methods from photogrammetry and computer vision such as the optical flow method and the egomotion estimation. Examples used to illustrate these techniques include response measurement of buildings and cable-stayed bridges. Results show that the image-based sensing techniques have a great potential for accurately measuring displacement responses of these large-scale structures.

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“…System identification is important in the disciplines of control systems [1], communication [2], signal processing [3], and image processing [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

Cuevas

Gálvez

Hinojosa

et al. 2014

Journal of Applied Mathematics

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System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

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Digital image processing for non-linear system identification

Cited by 45 publications

References 3 publications

Measurement of the magnetic field of small magnets with a smartphone: a very economical laboratory practice for introductory physics courses

Measurement of the magnetic field of small magnets with a smartphone: a very economical laboratory practice for introductory physics courses

From photogrammetry, computer vision to structural response measurement

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

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