ESIEE Amiens

Amiens, France

ESIEE Amiens

Amiens, France

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Gazdac A.M.,Technical University of Cluj Napoca | Mabwe A.M.,ESIEE Amiens | Martis C.S.,Technical University of Cluj Napoca | Betin F.,UPJV | Biro K.,Technical University of Cluj Napoca
Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012 | Year: 2012

This paper presents an analytical design algorithm and a finite element method (FEM) analysis of a novel type of machine, the dual-rotor Permanent Magnet Induction Machine. The algorithm is based on the simplified Magnetic Equivalent Circuit (MEC) of the machine. After presenting the sizing procedure, the main results obtained via FEM computation are analyzed. Two possible configurations of the PM rotor are studied and compared in terms of developed electromagnetic torque, Joule and iron losses, and electromagnetic field capabilities. © 2012 IEEE.


Pantea A.M.,ESIEE Amiens | Aroquiadassou G.,ESIEE Amiens | Mabwe A.M.,ESIEE Amiens | Martis C.S.,Technical University of Cluj Napoca
SPEEDAM 2012 - 21st International Symposium on Power Electronics, Electrical Drives, Automation and Motion | Year: 2012

The aim of the work described in this paper is to present a real-time Sensorless Vector Control method (SVC) of Induction Machines (IM) using an FPGA board. It's well known that the performance of sensorless control of induction motor drives is generally poor, especially at low speed due to measurement accuracy (offset, voltage distortions, current unbalances, machine parameter variation etc.). The approach used in this article refers to the real-time Extended Kalman Filter (EKF) to overcome the incertitude of the measurement by considering noise. From the measurement of stator voltages and currents, the rotor speed of the Induction Motor is estimated in real time. The results obtained from SVC are then compared to standard Vector Control (VC) which uses a speed sensor. Tests in different conditions demonstrate excellent steady-state operation and good dynamic performance. © 2012 IEEE.


Li X.,ESIEE Amiens | Seignez E.,CNRS Fundamental Electronics Institute | Lambert A.,LIVIC | Loonis P.,ESIEE Amiens
Transactions of the Institute of Measurement and Control | Year: 2014

Driver drowsiness greatly increases the driver's risk of a crash or near-crash. It is recognized as one of the major causes of severe traffic accidents. In this paper, a novel non-intrusive surveillance system is proposed to estimate driver drowsiness by fusion of visual information about lane and driver with Dempster-Shafer theory. Based on expert knowledge and data statistics, various visual features extracted from lane and eye tracking are analysed for their correlation with driver drowsiness in the framework of the subjective 'observer rating of drowsiness'. The system is validated in real road scenarios and the experiment results demonstrate that it is promising in improving the robustness and temporal response of driver surveillance in real time. © The Author(s) 2013.


Li X.,ESIEE Amiens | Seignez E.,CNRS Fundamental Electronics Institute | Lu W.,CNRS Fundamental Electronics Institute | Loonis P.,ESIEE Amiens
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2014

Vehicle safety is the study and practice for minimizing the occurrences and consequences of traffic accidents. It is found that driver behaviors such as drowsiness, impaired driving and distraction are contributing factors to traffic accidents. In complex road surroundings, comprehensive analysis is more robust than separate evaluations which are broadly proceeded with. In this paper, we propose a vision-based nonintrusive system involving lane and driver's eye features to analyze driver behaviors. In the framework of evidence theory, evaluations of driver drowsiness and distracted and impaired driving performance are integrated to evaluate vehicle safety in real time. The system was validated in real world scenarios, and experimental results demonstrate that it is promising to improve the robustness and temporal response of vehicle safety vigilance. © 2014 IEEE.


Li X.,ESIEE Amiens | Seignez E.,CNRS Fundamental Electronics Institute | Loonis P.,ESIEE Amiens
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2013

Driver drowsiness influences critically the driving safety and the lack of discerning the drowsy level precisely causes failure to take measures to prevent the accidents. In this paper, a novel intelligent surveillance system is proposed to estimate driver drowsiness based on the Observer Rating of Drowsiness (ORD) model integrated into evidence theory via fusion of lane and eye features. ORD is a subjective assessment of drowsiness that is reflected in people's physical appearance, behaviors and mannerisms. Its drowsiness model in five levels, which acts as the framework in evidence theory, is used to describe the driver's state. Based on expert knowledge and data statistics, various visual eye features are studied to enhance the robustness of this system. The system is validated in real world scenarios, and experiment results demonstrate that it is promising to improve the robustness and temporal response of driver surveillance in real-time. © 2013 IEEE.


Figueredo R.,ESIEE Amiens | Sansen P.,ESIEE Amiens
Mechanics Based Design of Structures and Machines | Year: 2014

This paper investigates the kinematics optimization of the mechanical scissor systems of tipping. Lagrangian formalism in mechanics and genetic algorithms (GAs) are used respectively as analyzer and optimizer. Thus, multiobjective functions are considered and based on the reaction force of the hydraulic cylinder and the size of the whole mechanical system to obtain a final optimum one, more resistant and lighter. Two kinds of kinematics are optimized: a classical one which is usually adopted in industry and a suggested one including modifications. All optimized results are compared to the original one to demonstrate the effectiveness of the proposed methodology. © 2014 Copyright Taylor & Francis Group, LLC.


De Rybel T.,University of British Columbia | Singh A.,University of British Columbia | Pak P.,ESIEE Amiens | Marti J.R.,University of British Columbia
IEEE Transactions on Power Delivery | Year: 2010

An online signal injection approach based on a special current transformer is proposed as part of a solution for improving the test setup of transfer function diagnostics of transformers. However, the method is general and applicable to many types of substation equipment, such as circuit breakers and transmission lines. The system allows a high-power, high-frequency test signal to be injected directly on a high-voltage bus of an energized system. Due to the self-contained nature of the injector, no external power supplies or signal generators are needed and the design does not need to be ground-referenced. The system can thus be at the bus potential. In conjunction with wireless communication for control, no isolator bushings are required and the device can be constructed as a sleeve to be mounted around the busbar. This allows for economical retrofitting to existing installations. In this paper, the operational need for such an injection device is discussed, followed by the theory behind the proposed concept. Finally, a low-voltage, optically controlled, self-powered prototype is designed and demonstrated online to show the practical validity of the concept. © 2010 IEEE.


Li X.,ESIEE Amiens | Seignez E.,ESIEE Amiens | Loonis P.,ESIEE Amiens | Lambert A.,LIVIC
Proceedings - 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2012 | Year: 2012

Driving inattention is one of the most serious traffic accidents cause whereas a monitoring system of the driver's vigilance could be used as a prevention. In this paper, we propose an intelligent system that estimates the driver fatigue using combination of lane tracking and face detection fused via Dempster-Shafer theory. The approach is aimed to estimate the fatigue based on the expert knowledge for driving vigilance. The method can compensate for the impact of poor detection and improve the accuracy of fatigue alerting. © 2012 IEEE.


Kia S.H.,ESIEE Amiens | Bartolini F.,ESIEE Amiens | Mpanda Mabwe A.,ESIEE Amiens | Ceschi R.,ESIEE Amiens
IECON Proceedings (Industrial Electronics Conference) | Year: 2011

The current collection enhancement is a key requirement for train speedup in railway industries. However, pantograph-catenary interaction is the present challenge of enhancing current collection in electrical railways. In the literature, the application of active pantograph control is considered as a means to improve the pantograph dynamics and to reduce the contact force vibration. Due to track test high cost, control strategies are validated in the initial phase through laboratory tests and as a result, a real-time catenary model can be developed. Based on model developed, control strategies can be examined on a pantograph-in-the-loop (PIL) configuration including a real-time platform by means of which the dynamic behavior of catenary is emulated in real-time. This paper investigates the implementation of pantograph-catenary interaction on a real-time platform based on Opal-RT technology equipped with a quad-core main processor. The European standard EN50318-2000 is used for the validation of the catenary model. © 2011 IEEE.


Li X.,ESIEE Amiens | Seignez E.,ESIEE Amiens | Loonis P.,ESIEE Amiens
2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012 | Year: 2012

In this paper, driving drowsiness detection based on visual features offers a noninvasive solution to detect the driver's state. Fusion with lane and driver features is addressed in order to complement each other once any visual signs failed. Given uncertainty exists greatly, Dempster-Shafer theory is used to improve the accuracy of detection while reliability is given to present the data's robustness. Experimental results demonstrate that the performance of driving drowsiness vigilance is enhanced in the proposed framework and efficiently tolerates the failure of feature collection. © 2012 IEEE.

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