Acetabular reamer is one of the most important surgical tools in hip replacement surgery. The milling quality of acetabular reamer on acetabulum is affected by the dimension change of cutting edges. The wear of acetabular reamer can be examined by processing 3D point cloud of acetabular reamer， so a dimensional analysis algorithm for the cutting edges of acetabular reamer based on 3D point cloud processing was proposed. Frist， an algorithm with tangency plane and maximum angle criterion were introduced in the proposed algorithm to obtain the boundary point cloud of acetabular reamer based on boundary characteristics of the tooth holes. Second， the boundary point cloud was partitioned into individual tooth hole point clouds by K-means clustering algorithm， and then the point cloud of each tooth hole boundary was searched by radius nearest neighbor search algorithm to obtain the point cloud of cutting edges belonging to different tooth holes. Finally， RANSAC （RANdom SAmple Consensus） algorithm was used to fit the point cloud of acetabular reamer to a sphere， and Euclidean distance from the point cloud of cutting edges to the center of the fitted sphere was calculated to analyze cutting edge dimensions of acetabular reamer. PCL （Point Cloud Library） was used as a development framework to process the point cloud of acetabular reamer. The accuracy of hole segmentation of the point cloud of acetabular reamer is 100%， and the accuracy of spherical fitting radius of the point cloud of the acetabular reamer is 0.004 mm. Experimental results show that the proposed algorithm has a good effect on the point cloud processing of acetabular reamer， and can effectively realize the dimensional analysis of the cutting edges of acetabular reamer.

Knowledge tracing models mainly use three types of learning behaviors data， including learning process， learning end and learning interval， but the existing studies do not fuse the above types of learning behaviors and cannot accurately describe the interactions of multiple types of learning behaviors. To address these issues， a Multi-Learning Behavior collaborated Knowledge Tracing （MLB-KT） model was proposed. First， the multi-head attention mechanism was used to describe the homo-type constraint for each type of learning behavior， then the channel attention mechanism was used to model the multi-type collaboration in three types of learning behaviors. Comparison experiments of MLB-KT， Deep Knowledge Tracing （DKT） and Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） models were conducted on three datasets. Experimental results show that the MLB-KT model has a significant increase in Area Under the Curve （AUC） and performs best on ASSISTments2017 dataset， the AUC is improved by 12.26% and 2.77% compared to DKT and ATCKT respectively； the results of the representation quality comparison experiments also verify that the MLB-KT model has better performance. In summary， modeling the homo-type constraint and multi-type collaboration can better determine students' knowledge status and predict their future answers.

Aiming at the issue of accurate recognition of human motion gait in exoskeleton robot human computer interaction and medical rehabilitation， a dynamic gait recognition method based on human model constraints was proposed. Firstly， Anybody Modeling System （AMS） simulation software was used to establish different motion simulation models， the gait phases were devided according to the model constraints， and the corresponding relationship between the real data and the simulation data was established through regression mapping. Then， the plantar pressure data collected by the flexible pressure sensor and the foot displacement data collected by the inertial measurement unit were fused into the foot motion data， and the motion data was dynamically segmented according to its dynamic changes and the model constraints to determine the gait phase. Finally， Convolutional Neural Network （CNN） was built to identify the walking gait phase. Experimental results show that the proposed method has the average recognition accuracy of walking action gait of 94.58%， and the average gait recognition accuracy for going upstairs and downstairs actions is 93.21% and 94.64% respectively， which has the gait recognition accuracy of the three actions （walking， going upstairs and downstairs） increased by 11.34， 12.19 and 16.03 percentage points， respectively. It can be seen that CNN recognition based on dynamically segmented foot motion data has a high accuracy， and is suitable for gait recognition of different actions.

For the problems of limited bandwidth resources， the existence of external disturbance and parameter uncertainty， a non-fragile dissipative control scheme for event-triggered networked systems was proposed. Firstly， based on the Networked Control System （NCS） model， a non-periodic sampling event-triggered scheme was proposed， and a delay closed-loop system model was established. Then， a novel bilateral Lyapunov functional was constructed by using the structure characteristics of sawtooth wave. Finally， the sufficient conditions to ensure the stability of the system were derived by using methods such as Jensen inequality， free weight matrix and convex combination， and the gain of the feedback controller was calculated. The results of numerical simulation show that the proposed bilateral functional is less conservative than the unilateral functional， the event-triggered mechanism can save bandwidth compared with the common sampling mechanism， and the proposed controller is feasible.