For some disabled people can’t use hand to control the mouse, this paper designs a novel head-controlled mouse. Using accelerometers and gyroscopes as the sensitive element of the system measure the head rotation angle. The mouse is controlled as for the relationship between the head rotation angle and the mouse move distance. For mouse click operation, this article designed a software to achieve. Considering the MEMS sensor noise affect the mouse control accuracy, using the unscented Kalman filter (UKF) to fusion the data of accelerometers and gyroscopes to obtain the optimal angle estimation. Compared with only using an accelerometer or gyroscope sensor, the method proposed in this paper for angle measurement can have high accuracy and stability, thereby improving the mouse control precision and maneuverability.
In order to provide a general purpose method to search optimum solution for complex constrained engineering problems without explicit system model, a hybrid optimization strategy based on artificial neural networks (ANNs) and genetic algorithms (GAs) is proposed in this paper. This strategy combines the strong nonlinearity mapping abilities of ANNs and effective and robust evolutionary searching ability of GAs. Firstly, ANNs are utilized to model the un-known system using inputs and outputs of system. Then the direct comparison approach based improved GAs are employed to search optimal solution in the constrained design space, using the trained ANNs as the function generator of system outputs. This strategy is implemented in optimization of design variables for sheet metal flanging process. The verification results of numerical simulation and the experiments demonstrate the feasibility and effectiveness of the strategy.
Portable Unmanned Aerial Vehicles (PUAVs) present an enormous application potential, and the real time accurate position and attitude information is the basis of autonomous flight of PUAVs. In order to obtain comprehensive and accurate position and attitude data of PUAVs in flight, focusing on the common sensors configuration of PUAVs, each type of sensor’s characteristic is analyzed, and the data fusion problem of SINS/GPS/Compass combination is presented and studied in this paper. Firstly, the error expressions of MEMS inertia sensors, attitude, velocity and position are researched and derived, and the state equation and observation equation are built, and the discrete equations are derived for computer implementation, so the data fusion model for Kalman Filter fusion algorithms is presented. Then, the data fusion system and algorithms are implemented in software, and the flight data obtained in flight experiment is fed to the software for data fusion. The comparison between original data and fusional data shows that SINS/GPS/Compass data fusion system can promote accuracy of position and attitude markedly.
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