An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error

2021 ◽  
pp. 108162
Author(s):  
Di Liu ◽  
Shaoping Wang ◽  
Xiaoyu Cui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Measurement Error ◽  
Wiener Process ◽  
Individual Difference ◽  
Estimation Method ◽  
Reliability Estimation ◽  
Artificial Neural

scholarly journals Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 583
Author(s):  
Weiting Liu ◽  
Binpeng Zhan ◽  
Chunxin Gu ◽  
Ping Yu ◽  
Guoshi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sensor Array ◽  
Estimation Method ◽  
Tactile Sensor ◽  
Radius Of Curvature ◽  
Curvature Radius ◽  
Average Curvature ◽  
Artificial Neural ◽  
Tactile Sensor Array

Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.

scholarly journals Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2570
Author(s):  
Malgorzata Binek ◽  
Andrzej Kanicki ◽  
Pawel Rozga
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Systems ◽  
Magnitude Estimation ◽  
Estimation Method ◽  
Basic Function ◽  
Filter Method ◽  
Zero Crossing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Dynamic phenomena in electric power systems require fast and accurate algorithms for processing signals. The processing results include synchrophasor parameters, e.g., varying amplitude, phase or frequency of sinusoidal voltage or current signals. This paper presents a novel estimation method of synchrophasor parameters that comply with the requirements of IEEE/IEC standards. The authors analyzed an algorithm for measuring the phasor magnitude by means of a selected artificial neural network (ANN), an algorithm for estimating the phasor phase and frequency that makes use of the zero-crossing method. The original components of the presented approach are: the method of the synchrophasor magnitude estimation by means of a suitably trained and applied radial basic function (RBF); the idea of using two algorithms operating simultaneously to estimate the synchrophasor magnitude, phase and frequency that apply identical calculation methods are different in that the first one filters the input signal using the FIR filter and the second one operates without any filter; and the algorithm calculating corrections of the phase shift between the input and output signal and the algorithm calculating corrections of the magnitude estimation. The error results obtained from the applied algorithms were compared with those of the quadrature filter method and the ones presented in literature, as well as with the permissible values of the errors. In all cases, these results were lower than the permissible values and at least equal to the values found in the literature.

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