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A three-dimensional FEM(finite element model)is established, including penstocks with initial defect (ovality), backfill concrete, drainage cushion and surrounding rock. The nonlinear static calculation of the model is carried out. The stability of penstocks with backfill concrete, drainage cushion and surrounding rock under external pressure is studied. The sensibility of the embedded penstocks to initial defect, initial gap and elastic modulus of drainage cushion is analyzed. The results of finite element method, Jacobsen method and strength formula in SL281 are compared and analyzed. The results indicate that the FEM of penstocks, backfill concrete and surrounding rock with initial defects is easy to converge by nonlinear calculation; the ovality and gap have little influence on the critical external pressure of the embedded penstocks with stiffener ring, while the drainage cushion has a certain influence on the critical external pressure; the critical external pressure calculated by SL281 is low and safe; compared with Jacobsen method, the critical external pressure of the finite element method is increased by about 14%; for the embedded penstocks with drainage cushion, the finite element method can be used to calculate the influence of the drainage cushion on the critical external pressure, and the appropriate reduction factor can be obtained, and then the Jacobsen result can be modified by the reduction factor.

Aiming at the problem of manual design, slow speed, and low efficiency in traditional face detection, this paper proposes a face detection algorithm based on Fast R-CNN. In this method, CNN algorithm, the Haar-Adaboost algorithm, and the candidate search algorithm are used to detect the candidate region of the face that may exist in the image. Then, the candidate region is input to the trained convolution neural network, and after a series of convolution and pooling operations, the final convolution feature (ROI) is based on the Fast R-CNN network structure, and finally, the feature is input to the two full connections (ROI), and then the characteristics of the input image are not specified. Branch, respectively, for feature classification and coordinate regression calculation. The experimental results of the proposed algorithm on the LFW test data set show that the proposed algorithm is compared with the traditional PCA + SVM algorithm, the traditional which avoids the manual design features and higher Identify the accuracy rate and speed up the detection time.

The authors demonstrate a photonic link for stable radio frequency (RF) dissemination over fibre based on passive phase compensation with an active-assisted control method. A variable optical delay line is introduced in the transmitting link for active-assisted phase compensation. After active-assisted compensation, the residual jitter is

Spinning process is an effective and flexible method that has been widely employed to produce metal components having an axisymmetric rotational geometry, such as components for jet engines, turbines, radar reflectors, satellite nose cones, etc. Safe performance of mandrel and staff is critical to spinning process. In this paper, the safety aspects, i.e., temperature, leakage of hydraulic system, material deformation, roller path that related to spinning process operations, were in-depth analyzed. Moreover, a concept of mandrel protection curve was presented to avoid the potential dangers, and the relevance algorithm to generate mandrel protection curve was also developed. Then, based on the generation algorithm of mandrel protection curve, the redundant point problem was analyzed, and a new algorithm for rejecting redundant points was designed to enhance efficiency and safety. Finally, Multi-passes and constant linear velocity were adopted for higher product precision during complex spinning product manufacture. The common open-architecture CNC system and hydraulic servo system were used to realize the algorithm researches. The results show that the researched algorithms are effective to guarantee mandrel and staff’s safety and improve product precision.

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.