In the parameter fault detection during rocket flights, the traditional red line method has high missing alarm rate and false alarm rate, expert system method has high maintenance cost, and machine learning is constrained by dataset size so that it is hard to train the model. Therefore, two transfer learning strategies based on instance and model respectively were proposed. In order to realize the real-time detection of the key parameter oxygen pump speed in the new type engine YF-77, after analyzing the parameters and data characteristics of LOX/LH2 engines YF-75 and YF-77 that have the same construction principle, the domain differences were solved, the feature space was built, and the feature vectors were filtered. In the experiments about instance transfer and model transfer from YF-75 to YF-77, compared with the methods without transfer learning such as k-Nearest Neighbor ( kNN) and Support Vector Machine (SVM), the models after transfer learning can reduce the missing alarm rate from 58.33% (the highest) to 12.25% (the lowest), and reduce the false alarm rate from 60.83% (the highest) to 13.53% (the lowest), therefore verifying the information transferability between two kinds of engines, and the possibility of applying transfer learning in aerospace engineering practice.

Particle Swarm Optimization (PSO) algorithm is easy to be premature and drop into the local optimum and cannot jump out when solving complex multimodal functions. Related researches show that changing the topological structure among particles and adjusting the updating mechanism are helpful to improve the diversity of the population and the optimization ability of the algorithm. Therefore, a Hybrid PSO with Topological time-varying and Search disturbance (HPSO-TS) was proposed. In the algorithm, a K-medoids clustering algorithm was adapted to cluster the particle swarm dynamically for forming several heterogeneous subgroups, so as to facilitate the information flow among the particles in the subgroups. In the speed updating, by adding the guide of the optimal particle of the swarm and introducing the disturbance of nonlinear changing extreme, the particles were able to search more areas. Then, the transformation probability of the Flower Pollination Algorithm (FPA) was introduced into the position updating process, so the particles were able to transform their states between the global search and the local search. In the global search, a lioness foraging mechanism in the lion swarm optimization algorithm was introduced to update the positions of the particles; while in the local search, a sinusoidal disturbance factor was applied to help particles jump out of the local optimum. The experimental results show that the proposed algorithm is superior to FPA, PSO, Improved PSO (IPSO) algorithm and PSO algorithm with Topology (PSO-T) in the accuracy and robustness. With the increase of testing dimension and times, these advantages are more and more obvious. The topological time-varying strategy and search disturbance mechanism introduced by this algorithm can effectively improve the diversity of population and the activity of particles, so as to improve the optimization ability.

Aiming at the problem of object structure discontinuity and incompleteness occurred in image completion, an image completion algorithm based on depth information was proposed. Firstly, the plane parameter Markov random field model was established to speculate depth information of the pixels in the image where the scene situate, then the coplanar region in the image determined, and the target matching blocks were located. Secondly, according to the principle of perspective projection, the transformation matrix was derived, which guided the geometric transformation of the matching blocks. Finally, the target cost function which includes the depth term was designed. Experimental results show the proposed algorithm has superiority in both subjective details and Peak Signal-to-Noise Ratio (PNSR) statistics.

In order to further improve the efficiency of the population diversity in the implementation process of the Particle Swarm Optimization (PSO), an Adaptive PSO (APSO) algorithm based on diversity feedback was proposed. APSO adopted a new population diversity evaluation strategy which enabled the automatic control of the inertia weight with population diversity in the search process to balance exploration and the exploitation's process. In addition, an elite learning strategy was used in the globally best particle to jump out of local optimal solution. It not only ensured the convergence rate of the algorithm, but also adaptively adjusted the search direction to improve the accuracy of solutions. The simulation results on a set of typical test functions verify the validity of APSO.