Zhang J.,University of Auckland |
Bennouna O.,IRSEEM |
Swain A.K.,University of Auckland |
Nguang S.K.,University of Auckland
Proceedings of 2013 International Renewable and Sustainable Energy Conference, IRSEC 2013 | Year: 2013
This paper proposes a sliding mode observer (SMO)-based fault detection and isolation (FDI) scheme for wind turbines. The actuator faults in pitch systems of the wind turbine are transformed as sensor faults. A reduced order model of the drive train system is constructed to eliminate the effects of unknown aerodynamic rotor torque. Based on the new system representation, a bank of SMOs are designed such that the output signal can be accurately estimated in the presence of faults. The proposed method can accurately determine the location of the faults by comparing the estimated outputs with measurements. The effectiveness of the proposed FDI scheme is illustrated via simulations. © 2013 IEEE.
Mhiri R.,LITIS Laboratory |
Vasseur P.,LITIS Laboratory |
Mousset S.,LITIS Laboratory |
Boutteau R.,IRSEEM |
Bensrhair A.,LITIS Laboratory
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2014
This paper presents a visual odometry with metric scale estimation of a multi-camera system in challenging un-synchronized setup. The intended application is in the field of intelligent vehicles. We propose a new algorithm named 'triangle-based' method. The proposed algorithm employs the information from both extrinsic and intrinsic parameters of calibrated cameras. We assume that the trajectory between two consecutive frames of a camera is a linear segment (straight trajectory). The relative camera poses are estimated via classical Structure-from-Motion. Then, the scale factors are computed by imposing the known extrinsic parameters and the linearity assumption. We verify the validity of our method both in simulated and real conditions. For the real world, the motion trajectory estimated for image sequence of two cameras from KITTI dataset is compared against the GPS/INS ground truth. © 2014 IEEE.
Zhao L.,CNRS Material Physics Group |
Normand A.,CNRS Material Physics Group |
Delaroche F.,CNRS Material Physics Group |
Ravelo B.,IRSEEM |
Vurpillot F.,CNRS Material Physics Group
International Journal of Mass Spectrometry | Year: 2015
We propose in this paper novel approach for improving mass resolution in atom probe tomography. Using conventional counter-electrode or microelectrode design, improved mass resolution at half, tenth and 1% of the mass peak maximum is predicted when shaping properly the voltage evaporation pulse. Using a numerical approach, it is shown that a flat top voltage pulse used to trigger the field evaporation with sharp front and leading edges associated with a short tip to counter electrode distance(10 μm) strongly minimizes the energy deficits of evaporated ions from the sample, so that energy compensation devices are not necessary to obtain high mass resolution. © 2015 Elsevier B.V. All rights reserved.
Benkaci M.,Irseem Institute Of Recherche En Systemes Electronics Embarques |
Hoblos G.,IRSEEM |
2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013 | Year: 2013
Feature selection is an essential step for data classification used in fault detection and diagnosis process. In this work, a new approach is proposed which combines a feature selection algorithm and neural network tool for leaks detection and characterization tasks in diesel engine air path. The Chi2 is used as feature selection algorithm and the neural network based on Levenberg-Marquardt is used in system behavior modeling. The obtained neural network is used for leaks detection and characterization. The model is learned and validated using data generated by xMOD. This tool is used again for test. The effectiveness of proposed approach is illustrated in simulation when the system operates on a low speed/load and the considered leak affecting the air path is very small. © 2013 IEEE.
Dupuis Y.,IRSEEM |
Savatier X.,IRSEEM |
Vasseur P.,Avenue Of Luniversite
Image and Vision Computing | Year: 2013
In this paper, we tackle the problem of gait recognition based on the model-free approach. Numerous methods exist; they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space, we propose the use of the Random Forest algorithm to rank features' importance. In order to efficiently search throughout subspaces, we apply a backward feature elimination search strategy. Our first experiments are carried out on unknown covariate conditions. Our first results suggest that the selected features contribute to increase the CCR of different existing classification methods. Secondary experiments are performed on unknown covariate conditions and viewpoints. Inspired by the location of our first experiments' features, we proposed a simple mask. Experimental results demonstrate that the proposed mask gives satisfactory results for all angles of the probe and consequently is not view specific. We also show that our mask performs well when an uncooperative experimental setup is considered as compared to the state-of-the art methods. As a consequence, we propose a panoramic gait recognition framework on unknown covariate conditions. Our results suggest that panoramic gait recognition can be performed under unknown covariate conditions. Our approach can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions. © 2013 Elsevier B.V.