As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and fake videos that seriously endanger the stability of the country and society. Examples include faking politicians to make inappropriate statements, using face-swapping technology to spread false information, and creating fake videos to obtain money. In view of this social problem, based on the original fake face detection system, this paper proposes using a new network of EfficientNet-V2 to distinguish the authenticity of pictures and videos. Moreover, our method was used to deal with two current mainstream large-scale fake face datasets, and EfficientNet-V2 highlighted the superior performance of the new network by comparing the existing detection network with the actual training and testing results. Finally, based on improving the accuracy of the detection system in distinguishing real and fake faces, the actual pictures and videos are detected, and an excellent visualization effect is achieved.

The detection of wood defects plays an important role in the processing and production of wood, the deep learning methods have achieved outstanding results in this field in recent years. There are still two unresolved problems, one is that the category of defects is unbalanced and difficult to obtain, and the other is the effective modeling of defects with different sizes and irregular shapes. Here an alternative detection method is proposed. For the problem of imbalance in defect categories, a generative data augmentation method, cycle generative adversarial network (GAN), is used to generate new defective images, while seven online data augmentation schemes are used to solve the problem of difficult access to defective data. The layered deformable mask region-based convolutional neural network (Mask R-CNN) is constructed to address the challenges of modeling irregular defects and contextual information extraction. By creating layered connections between residual modules, a larger receptive field is achieved at each layer of the network, while deformable convolution is used to better fit the shape of the defects. Finally, the upgraded model is trained and used to locate and segment defects simultaneously. Ablation experiments show that the enhanced model performs better than the original model in terms of localization and segmentation. Meanwhile, it is excellent relative to baseline object detection algorithms, instance segmentation algorithms, and other methods for detecting wood defects.

In the field of micromanipulation, accurate identification and localization of micro-targets are crucial. This article presents a robust high-precision micromanipulation targets localization and motion tracking algorithm to address the challenges posed by uneven illumination, shadows, object occlusion, and image noise in micromanipulation. The proposed method employs improved bilateral filtering and the Sobel operator for illumination correction and edge contour detection in the horizontal view, achieving identification and Z-directional localization of micro-targets. In the vertical view, the improved grayscale template and the feature point matching algorithms accurately identify micro-targets and locate manipulation points in the X/Y directions, even under occlusion and rotation. Experimental results demonstrate that the algorithm can identify and localize micro-targets with an average error less than 1 μm and an average time of 120 ms, significantly improving the success rate, accuracy, and efficiency compared to existing methods. This article provides a novel approach for the precise automation of micromanipulation tasks.

Piezoelectric actuators (PEAs), as a smart material with excellent characteristics, are increasingly used in high-precision and high-speed nano-positioning systems. Different from the usual positioning control or fixed frequency tracking control, the more accurate rate-dependent PEA nonlinear model is needed in random signal dynamic tracking control systems such as active vibration control. In response to this problem, this paper proposes a Hammerstein model based on fractional order rate correlation. The improved Bouc-Wen model is used to describe the asymmetric hysteresis characteristics of PEA, and the fractional order model is used to describe the dynamic characteristics of PEA. The nonlinear rate-dependent hysteresis model can be used to accurately describe the dynamic characteristics of PEA. Compared with the integer order model or linear autoregressive model to describe the dynamic characteristics of the PEA Hammerstein model, the modeling accuracy is higher. Moreover, an artificial bee colony algorithm (DE-ABC) based on differential evolution was proposed to identify model parameters. By adding the mutation strategy and chaos search of the genetic algorithm into the previous ABC, the convergence speed of the algorithm is faster and the identification accuracy is higher, and the simultaneous identification of order and coefficient of the fractional model is realized. Finally, by comparing the simulation and experimental data of multiple sets of sinusoidal excitation with different frequencies, the effectiveness of the proposed modeling method and the accuracy and rapidity of the identification algorithm are verified. The results show that, in the wide frequency range of 1–100 Hz, the proposed method can obtain more accurate rate-correlation models than the Bouc-Wen model, the Hammerstein model based on integer order or the linear autoregressive model to describe dynamic characteristics. The maximum error (Max error) is 0.0915 μm, and the maximum mean square error (RMSE) is 0.0244.