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Real time motion compensation technology based on least square support vector machine prediction

Song Zhehan, Feng Huajun, Xu Zhihai, Li Qi

Optica Applicata

2022

Vol. 52, nr 2

259--272

When cameras are used in aerial photography, satellite imaging or other scenes, the motion of the observational target causes image blur. The corresponding motion compensation systems often present some response delay. Thus, we introduce effective and fast motion prediction for the target to achieve steady real-time motion compensation. We first analyze the target motion states to propose a fast and robust prediction method based on the least square support vector machine algorithm. Then, we evaluate the performance between ours and other state-of-the-art methods through experiments. Experimental results confirm that the proposed method provides a fast and robust prediction for target motion. At last, we embed our method with dual-resolution camera system to perform high-quality motion compensation effect in real time.

Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach

Birame M’hamed, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

2021

Vol. 22, No. 1

57--63

Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

Birame M’hamed, Taibi Djamel, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

2020

Vol. 21, No. 4

35--41

Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.

A study of slope stability prediction using least square support vector machine

Samui P.

International Journal of Applied Mechanics and Engineering

2012

Vol. 17, no. 1

279-287

The determination of stability of slope is an important task in geological engineering practice. This paper proposes the use of the least square support vector machine (LSSVM) for the determination of stability of slope. The LSSVM is a statistical learning method which has a self-contained basis of statistical-learning theory and excellent learning performance. The five input variables used for the LSSVM model in this study are the unit weight (d), cohesion (c), angle of internal friction, slope angle, height (H) and pore water pressure coefficient (ru). The LSVM model also gives a probabilistic output. This study shows that the LSSVM model is a robust tool for slope stability analysis.