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Your search keyword '"SAMPLING errors"' showing total 2 results
2 results on '"SAMPLING errors"'

1. Robust Sparse Representation-Based Classification Using Online Sensor Data for Monitoring Manual Material Handling Tasks.

Author

Barazandeh, Babak, Bastani, Kaveh, Rafieisakhaei, Mohammadhussein, Kim, Sunwook, Kong, Zhenyu, and Nussbaum, Maury A.

Subjects
*MATERIALS handling, *NOISE, *GAUSSIAN function, *FAULT indicators (Electricity), *SAMPLING errors, *ERROR analysis in mathematics
Abstract

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Fractional Order Differentiation by Integration and Error Analysis in Noisy Environment.

Author

Liu, Da-Yan, Gibaru, Olivier, Perruquetti, Wilfrid, and Laleg-Kirati, Taous-Meriem

Subjects
*JACOBIAN matrices, *NOISE measurement, *ERROR analysis in mathematics, *SAMPLING errors, *ROBUST control, *TIME delay systems
Abstract

The integer order differentiation by integration method based on the Jacobi orthogonal polynomials for noisy signals was originally introduced by Mboup, Join and Fliess. We propose to extend this method from the integer order to the fractional order to estimate the fractional order derivatives of noisy signals. Firstly, two fractional order differentiators are deduced from the Jacobi orthogonal polynomial filter, using the Riemann–Liouville and the Caputo fractional order derivative definitions respectively. Exact and simple formulae for these differentiators are given by integral expressions. Hence, they can be used for both continuous-time and discrete-time models in on-line or off-line applications. Secondly, some error bounds are provided for the corresponding estimation errors. These bounds allow to study the design parameters' influence. The noise error contribution due to a large class of stochastic processes is studied in discrete case. The latter shows that the differentiator based on the Caputo fractional order derivative can cope with a class of noises, whose mean value and variance functions are polynomial time-varying. Thanks to the design parameters analysis, the proposed fractional order differentiators are significantly improved by admitting a time-delay. Thirdly, in order to reduce the calculation time for on-line applications, a recursive algorithm is proposed. Finally, the proposed differentiator based on the Riemann–Liouville fractional order derivative is used to estimate the state of a fractional order system and numerical simulations illustrate the accuracy and the robustness with respect to corrupting noises. [ABSTRACT FROM AUTHOR]

Published
2015
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