The process of algal bloom evolution in rivers or lakes has characteristics of suddenness and uncertainty, which leads to low prediction accuracy of algal bloom. To solve this problem, chlorophyll a concentration was used as the surface index of cyanobacteria bloom evolution process, and a cyanobacterial bloom forecast model based on Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) was proposed. Firstly, the improved Genetic algorithm-First order lag filter (GF) optimization algorithm was taken as data smoothing filter. Secondly, a GF-LSTM network model was built to accurately predict the cyanobacterial bloom. Finally, the data sampled from Meiliang Lake in Taihu area were used to test the forecast model, and then the model was compared with the traditional RNN and LSTM network. The experimental results show that, the mean relative error of the proposed GF-LSTM network model is 16%-18%, lower than those of RNN model (28%-32%) and LSTM network model (19%-22%). The proposed model has good effect on data smoothing filtering, higher prediction accuracy and better adaptability to samples. It also avoids two widely known issues of gradient vanishing and gradient exploding when using traditional RNN model during long term training.

In order to automatically register the skulls that differ a lot in pose with the reference skull, or miss a large part of bones, an automatic nonrigid 3D skull registration method based on boundary correspondence was proposed. First, all the boundaries of target skull were calculated, and according to the edge length and the shortest distance between the edges, the edge type was identified automatically, and the correspondence between the registered skull and the reference skull was established. Based on that, the initial position and attitude of the skull were adjusted to realize the coarse registration. Finally, Coherent Point Drift (CPD) algorithm was used twice to realize the accurate registration of two skulls from the edge region to all regions. The experimental results show that, compared with the automatic registration method based on Iterative Closest Point (ICP) and Thin Plate Spline (TPS), the proposed method has stronger robustness in pose, position, resolution and defect, and has more availability.