Acoustic injection is one of the most dangerous ways of causing micro-electro–mechanical systems (MEMS) failures. In this paper, the failure mechanism of acoustic injection on the microprocessor unit 6050 (MPU6050) accelerometer is investigated by both experiment and simulation. A testing system was built to analyze the performance of the MPU6050 accelerometer under acoustic injection. A MEMS disassembly method was adopted and a MATLAB program was developed to establish the geometric model of MPU6050. Subsequently, a finite element model of MPU6050 was established. Then, the acoustic impacts on the sensor layer of MPU6050 were studied by acoustic–solid coupling simulations. The effects of sound frequencies, pressures and directions were analyzed. Simulation results are well agreed with the experiments which indicate that MPU6050 is most likely to fail under the sounds of 11,566 Hz. The failure mechanism of MPU6050 under acoustic injection is the relative shift of the capacitor flats caused by acoustic–solid resonance that make the sensor detect false signal and output error data. The stress is focused on the center linkage. MPU6050 can be reliable when the sound pressure is lower than 100 dB.
Platform motions induced by waves pose a challenge to accurately predict the aerodynamic performance of floating offshore wind turbines (FOWTs). In view of this, the power performance and wake structure of FOWTs under platform pitch, surge, and their combined motions were investigated in this paper, using the computational fluid dynamics software, STAR-CCM+, with overset meshing and rigid body motion techniques. First, the simulation cases in single and same-phase combined motions with different amplitudes and frequencies were performed. Afterward, the approach of calculating the phase difference between pitch and surge motions was proposed to investigate the influence of the combined motion with phase difference on the aerodynamic performance. Results show that the increment of amplitude and frequency augments the mean power output and aggravates the power fluctuation in single and same-phase combined motions. The intensity of power variation under combined motion with a phase difference is weakened at 0.1 Hz compared to the single motion, while enhanced at 0.2 Hz, showing a different influence law on the aerodynamic performance. In addition, this paper established the power fluctuation table based on real sea states of Shidao in China, providing a certain reference for the controller design in this sea area.
A crucial line of defense for the security of wireless sensor network (WSN) is intrusion detection. This research offers a new MC-GRU WSN intrusion detection model based on convolutional neural networks (CNN) and gated recurrent unit (GRU) to solve the issues of low detection accuracy and poor real-time detection in existing WSN intrusion detection algorithms. MC-GRU uses multiple convolutions to extract network data traffic features and uses the high-level features output after convolution operations as input parameters of the GRU network, which strengthens the learning of spatial and time series features of traffic data and improves the detection performance of the model. The experiment results based on the WSN-DS dataset show that the overall detection accuracy of the four types of attack of black hole, gray hole, flooding, and scheduling and normal behaviors reaches 99.57%, and it is also better than the existing WSN intrusion detection algorithms in real-time performance and classification ability.
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