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After the completion of the three steps of "circling back", the lunar exploration has changed from the short-term single point exploration to the long-term exploration of lunar scientific research stations with the con-struction of lunar surface infrastructure, the higher requirements for the planning and demonstration, overall de-sign, system development and in-orbit exploration of lunar exploration missions are put forward. The thought of model based system engineering (MBSE) is adopted, a model-based forward process for the system analysis of the lunar scientific research station proposal is proposed, and the system model is taken as a carrier to analyze the mis-sion overall, mission requirements and task application scenario. By carrying out the model-based mission analysis of the lunar scientific research station, the process of the mission of the lunar scientific research station has been pre-liminarily realized.

In this paper a new search strategy for multi-objective optimization (MOO) with constraints is proposed based on a hybrid search mode (HSM). The search processes for feasible solutions and optimal solutions are executed in a mixed way for the existing methods. With regard to HSM, a hybrid search mode is proposed, which consists of two processes: Feasibility search mode (FSM) and optimal search mode (OSM). The executions of these two search modes are independent relatively and also adjusted according to the population distribution. In the early stage, FSM plays the leading role for exploring the feasible space since most of the individuals are infeasible. With the increase of the feasible individuals, OSM is the primary operation for the search of optimal individuals. The proposed method is simple to implement and need few extra parameter tuning. The handing method of constraints is tested on several multi-objective optimization problems with constraints. The remarkable results demonstrate its effectiveness and good performance.

Leak detection of an oil pipeline can prevent environmental and financial losses. A method for the cyber-physical system of pipeline leak detection is proposed based on the empirical mode decomposition (EMD) and deep belief network (DBN). Experiment data are acquired from an oil pipeline company. The EMD is suitable for noise removal and signal reconstruction from raw pressure signals, and the reconstructed signals are used to establish a DBN model of pipeline leakage. Our proposed method obtains higher-recognition-accuracy results (98% accuracy) and can more effectively identify leak detection than the twin support vector machine (TWSVM), support vector machine (SVM), and back-propagation neural network (BPNN). [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary strategy for multiobjective optimization in which a population’s evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence. [ABSTRACT FROM AUTHOR]