Int. J. Online Eng.最新文献

Nondestructive testing plays an important role in on-line equipment inspection. Traditional nondestructive testing methods have been widely utilized for this. Ultrasonic excitation-fiber Bragg grating sensing technique is based on well-developed optical fiber grating technology, which has a good prospect of damage detection for mechanical equipments. However the corresponding research is still at the starting stage and further research work is necessary. The main contribution pursued in this investigation is to establish a detection system based on fiber Bragg grating sensing under ultrasonic excitation and predict the damage position through time delay on the plate structure. Differencing from the conventional approaches, a new way of damage detection utilizes fiber Bragg grating sensors and a position algorithm by two-dimensional method is derived. Moreover, wavelet transform is adopted in the subsequent signal processing. Finally, a platform for experiment is built to verify the theoretical analysis of the sensing characterization of the fiber Bragg grating sensor in ultrasonic excitation. Experiment results show that the wavelet transform is effective for signal denoising and our localization algorithm is feasible.

The focus of this paper is to find a robust power control strategy with uncertain noise plus interference (NI) in cognitive radio networks (CRNs)in an under orthogonal frequency-division multiplexing (OFDM) framework. The optimization problem is formulated to maximize the data rate of secondary users (SUs) under the constraints of transmission power of each SU, probabilistic the transmit rate of each SU at each subcarrier and robust interference constraint of primary user. In consideration of the feedback errors from the quantization due to uniform distribution, the probabilistic constraint is transformed into closed forms. By using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, we solve this dual problem. Numerical simulation results show that our proposed algorithm is superior to the robust power control scheme based on interference gain worst case approach and non-robust algorithm without quantization error in perfect channels in the improvement of data rate of each SU, convergence speed and computational complexity.

Aiming at solving the poor the classical synchronous algorithm stability in wireless sensor network and high overhead of clock phase offset and frequency offset, a synchronization algorithm (CSMS algorithm) was designed for wireless sensor networks based on frequency offset estimation. The CSMS algorithm used the low overhead phase bias and frequency offset estimation method to improve the synchronization accuracy and stability of the pair nodes. At the same time, a synchronization strategy was built based on layering and broadcast monitoring, which ensured the stability and synchronization accuracy of the algorithm, realized the synchronization with neighbor nodes and root nodes, and optimized the total synchronization cost. Among them, the CSMS algorithm was mainly divided into two stages: level discovery phase, which was used for generating a layered structure of network; synchronization phase, used to estimate clock offset and frequency offset between pairs of nodes. The experimental results showed that the CSMS algorithm can effectively balance the synchronization energy consumption, synchronization accuracy and synchronization stability. As a result, it is summed up that dynamic adjustment of the nodes clock deviation is realized, the long-term stability of synchronization is ensured, and the precision of synchronization is improved. 

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.