Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy sapping.Therefore,this paper proposes a neural network-based method for predicting the flowering phase of pear tree.Firstly,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method.Then,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm optimization.The crossover operator and mutation operator in the adaptive genetic algorithm are improved.Finally,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper.The results demonstrate that,the model can solve the local optimization problem of the BP neural network model.The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy.Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method.This method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.

Research on Face Anti-Spoofing Algorithm Based on Image Fusion

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.

Speed Control of Motor Based on Improved Glowworm Swarm Optimization

To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.