Aiming at the problem that the prediction method of protein function based on the current Protein-Protein Interaction (PPI) network has low precision and is susceptible to data noise, a new machine learning protein function prediction method named HPMM (HC, PCA and MLP based Method) was proposed, which combined Hierarchical Clustering (HC), Principal Component Analysis (PCA) and Multi-layer Perception (MLP). HPMM took comprehensive consideration from macro and micro perspectives. It combined the information of protein families, domains and important sites into the vertex attributes of PPI networks to alleviate the effect from the data noise of networks. Firstly, the features of function modules and attribute principal components were extracted by using HC and PCA. Secondly, a mapping relationship between multi-feature and multi-function, used to predict protein functions, was constructed by training the MLP model. Three homo sapiens PPI networks, which were annotated by Molecular Functions (MF), Biological Processes (BP), and Cellular Components (CC) respectively, were adopted in the experiments. Comparisons were performed among the HPMM algorithm, the Cosine Iterative Algorithm (CIA) and the Diffusing GO Terms in the Directed PPI Network (GoDIN) Algorithm. The experimental results indicate that HPMM can obtain higher precision and F-measure than algorithms CIA and GoDIN, which are purely PPI network based methods.

In order to solve the two-species Small Phylogeny Problem (SPP) in the duplication-loss model, a simulated annealing algorithm named SA2SP was devised for the duplication-loss alignment problem. An alignment algorithm was introduced to construct the initial solution; a labeling algorithm was used to construct the object function and obtain the evolution cost; and three intelligent neighborhood functions were introduced to generate neighborhood solutions by using the evolutionary characteristics of gene sequences. The ribosomal RiboNucleic Acid (rRNA) and transfer Ribonucleic Acid (tRNA) of four real bacterium were used to test the performance of SA2SP and Pseudo-Boolean Linear Programming (PBLP) algorithm. The experimental results show that the SA2SP algorithm has smaller evolution cost, and it is an effective method for solving the two-species SPP in the duplication-loss model.

Heavy land clutter with antenna scan is the main interference for Foreign Object Debris (FOD) detection. However, traditional Constant False Alarm Rate (CFAR) in space-domain is ineffective to detect targets on runways. To solve this problem, a cell-average clutter-map CFAR was proposed. First of all, an echo model based on the characteristics of FOD surveillance radar system was built. Then, the range-bearing two-dimensional CFAR detection could be realized by using clutter-map cells dividing, cell averaging and recursive filtering. Further analysis of the main factors that affected the detection performance of this method was studied in the end. The simulation results show that, the proposed algorithm can effectively detect the weak target and obtain high detection probability with a low signal-to-clutter ratio.

Web-courseware and learning object repository(LOR) are important in e-learning.They are dissociative and their structure is different.Knowledge representation was adopted in this paper to unify their structure.A model "LOR-concept map-web courseware" was put forward,doing research about how to integrate web-courseware and LOR.It is valuable to take advantage of web-courseware and LOR.It is also helpful to improve the efficiency of students’ learning by using resource.