The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

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It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies—relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical.

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