Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
In this paper, a real-time online data-driven adaptive method is developed to\ndeal with uncertainties such as high nonlinearity, strong coupling, parameter\nperturbation and external disturbances in attitude control of fixed-wing unmanned\naerial vehicles (UAVs). Firstly, a model-free adaptive control (MFAC)\nmethod requiring only input/output (I/O) data and no model information is\nadopted for control scheme design of angular velocity subsystem which contains\nall model information and up-mentioned uncertainties. Secondly, the\ninternal model control (IMC) method featured with less tuning parameters\nand convenient tuning process is adopted for control scheme design of the\ncertain Euler angle subsystem. Simulation results show that, the method developed\nis obviously superior to the cascade PID (CPID) method and the\nnonlinear dynamic inversion (NDI) method....
Driver fatigue and inattention accounts for up to 20% of all traffic accidents, therefore any\nsystem that can warn the driver whenever fatigue occurs proves to be useful. Several systems have\nbeen devised to detect driver fatigue symptoms, such as measuring physiological parameters, which\ncan be uncomfortable, or using a video or infrared camera pointed at the driverâ??s face, which in some\ncases, may cause privacy concerns for the driver. Usually these systems are expensive, therefore\na brief discussion on low-cost fatigue detection systems is presented, followed by a proposal for a\nnon-intrusive low-cost prototype, that aims to detect driver fatigue symptoms. The prototype consists\nof several sensors that monitor driver physical parameters and vehicle behaviour, with a total system\nprice close to 30 euros. The prototype is discussed and compared with similar systems, pointing out\nits strengths and weaknesses....
This paper proposes system architecture and protocols for the deployment of a toll-free\nelectric vehicle charging service. The architecture enables the party initiating the electric vehicle (EV)\ncharging to have their service request authorized by the system and paid for by a third party....
In light of the increasing demand for passenger transportation on high-speed railway (HSR), the pedestrian flow at HSR stations\nhas become quite crowded in many countries, which has attracted researchers to study the HSR boarding behavior. In this paper,\nwe propose three boarding strategies based on the features of the boarding behavior at an origin HSR station; we then use a cellular\nautomaton (CA)model to study the impacts of boarding strategies on each passengerâ??smotion during the boarding process at HSR\nstation. The simulation results indicate that some of the three strategies can optimize some passengersâ?? boarding time and relieve\nthe congestion degree, and the positive impacts on the boarding process are the most prominent when the three strategies are used\nsimultaneously.The results can help administrators to effectively organize the boarding process at the origin HSR station....
Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe\ncondition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method\nusing wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is\nemployed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine\n(SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the\nwheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with\nEEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the\ndistribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum\nfeatures, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration,\ncombining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally,\nnumerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the\nproposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less\nthan 0.25%, and the classification accuracy of rail corrugation depth is more than 99%....
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