In order to solve the problem that Fully-Convolutional Siamese network (SiamFC) tracking algorithm is prone to model drift and results in tracking failure when the tracking target suffers from dramatic appearance changes, a new Dual Attention Siamese network (DASiam) was proposed to adapt the network model without online updating. Firstly, a modified Visual Geometry Group (VGG) network which was more expressive and suitable for the target tracking task was used as the backbone network. Then, a novel dual attention mechanism was added to the middle layer of the network to dynamically extract features. This mechanism was consisted of a channel attention mechanism and a spatial attention mechanism. The channel dimension and the spatial dimension of the feature maps were transformed to obtain the double attention feature maps. Finally, the feature representation of the model was further improved by fusing the feature maps of the two attention mechanisms. The experiments were conducted on three challenging tracking benchmarks:OTB2013, OTB100 and 2017 Visual-Object-Tracking challenge (VOT2017) real-time challenges. The experimental results show that, running at the speed of 40 frame/s, the proposed algorithm has higher success rates on OTB2013 and OTB100 than the baseline SiamFC by the margin of 3.5 percentage points and 3 percentage points respectively, and surpass the 2017 champion SiamFC in the VOT2017 real-time challenge, verifying the effectiveness of the proposed algorithm.

Focusing on the issue that conflict is hard to detect accurately and comprehensively in collaborative design, a conflict detection model based on constraint was proposed. Considering the hierarchical constraints and constraint satisfaction, the detection model divided constraints into two sets: one set is with known constraints and the other set is with unknown constraints. The constraints of two sets were detected respectively. The set with known constraints was detected by interval propagation algorithm. Meanwhile, Back Propagation (BP) neural network was used to detect the set with unknown constraints. Immune Algorithm (IA) was utilized to optimize the weights and thresholds of BP neural network, and the steps of optimization process were put forward. In the comparison experiments with BP neural network optimized by Genetic Algorithm (GA), the convergent speed was increased by 69.96%, which indicated that BP neural network optimized by IA has better performance in convergent speed and global searching ability. The constraints were described by eXtensible Markup Language (XML), so that computers could automatically recognize and establish the constraint network. The implementation of conflict detection system based on constraint satisfaction was designed. Taking co-design of wind planetary gear train as an example, a conflict detection system in collaborative design was developed on Matlab with C#. The conflict detection model is proved to be feasible and effective, and provides a solution of conflict detection for collaborative design.