To overcome the randomness of RRT-Connect and slow convergence of B-RRT ^*(asymptotically-optimal Bidirectional Rapidly-exploring Random Tree) in path generation, an efficient path planning algorithm based on B-RRT ^*, abbreviated as EB-RRT ^*, was proposed. Firstly, an intelligent sampling function was intriduced to achieve more directional expansion of random tree, which could improve the smoothness of path and reduce the seek time. A rapidly-exploring strategy was also added in EB-RRT ^* by which RRT-Connect exploration mode was adopted to ensure rapidly expanding in the free space and improved asymptotically-optimal Rapidly-exploring Random Tree (RRT ^*) algorithm was adopted to prevent trapped in local optimum in the obstacle space. Finally, EB-RRT ^* algorithm was compared with Rapidly-exploring Random Tree (RRT), RRT-Connect, RRT ^* and B-RRT ^* algorithms. The simulation results show that the improved algorithm is superior to other algorithms in the efficiency and smoothness of path planning. It reduced the path planning time by 68.3% and the number of iterations by 48.6% compared with B-RRT ^* algorithm.

In the existing genetic algorithms for permutation flow shop scheduling problem, the crossover and mutation operator is complex because of the processing sequence, the offspring is not similar to parent, and the algorithm easily falls into local optimum. To solve these problems, an improved genetic algorithm with priority-based value coding method and optimum limited operator was proposed. The coding method based on the priority values of the workpieces could avoid illegal coding, and the optimum limited operator could limit the propagation of the best individual to prevent falling into local optimum. The experiments show that this coding method is feasible and it can solve the practical problem when urgent workpieces must be processed firstly. The simulation results on benchmarks demonstrate that the proposed algorithm has superiority of smaller relative error and higher stable solution quality.

This paper proposed a new method for detecting and locating the singularities in the signal. By analyzing the characteristics of singular signal, the detection of the pulse singularities was first modeled as a classification task of two classes: one class consisted of the pulse singularities and the other contained the other points in the signal. Then, based on the Bayes decision rule and Neyman-Pearson criterion, a decision surface was derived by constraining the probability of missed detection for the class containing the pulse singularities to be fixed. As a result, a Bayes Decision Based Pulse Singularity Detection (BDPSD) method was directly developed. The experimental results on a number of artificial and real signals show that the BDPSD method can greatly improve the detection quality and locating accuracy of the pulse singularity, compared with the singularity detection method based on the wavelet transform local modulus maximum theory. This also shows that BDPSD is indeed an effective and practical singularity detection method.