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In order to reduce the number and total distance of changing parking spaces in a parking lot or a given parking area through autonomous valet parking (AVP), in doing so to lower the related cost and the potential accident risk, this paper formula ted a vehicle-slot matching optimization model which satisfied the shared parking requirement. In view of the feature of autonomous vehicle freely translocating during parking, this paper defined the match, match slice and match map corresponding to decision variable and feasible solution by subdividing the shared parking demand and supply in time. By conception replacement, the paper changed the searching effective neighbor of match map into the classic assignment problem and solved it with Hungarian algorithm. In view of the NP-hard feature of the matching model, this paper proposed a designed tabu searching algorithm. Numerical analysis not only verified the rationality of this model and the effectiveness of the algorithm, also proved that the model and algorithm could deal with the shared parking of conventional vehicles. The results show that AVP can improve the utility of shared parking berths and increase the number of served vehicles. [ABSTRACT FROM AUTHOR]

This paper analyzed the security of an image encryption algorithm based on compound chaotic sequence, the proposed algorithm adopted three-level encryption structure of permutation-forward diffusion-reverse diffusion. Due to the forward diffusion and the reverse diffusion used the same one-dimensional diffusion sequence, therefore, this paper simplified the two level encryption structure of the forward diffusion and reverse diffusion to one-level diffusion encryption structure, further, simplified the three-level encryption structure of permutation-forward diffusion-reverse diffusion to two-level encryption structure of permutation and diffusion. Moreover, the chaotic sequences generated by the encryption algorithm were independent of the plaintext images or ciphertext images, consequently, it could crack the corresponding equivalent diffusion keys and equivalent permutation keys by chosen-plaintext attack. Theoretical analysis and numerical simulation experiment results verify the effectiveness of the analytical method. Finally, this paper proposed some improved suggestions for the original encryption algorithm to promote the security. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the analysis on mechanism modeling of GM(1,N) grey differential equations, based on the structure and characteristics of time series. By the numerical integration method a new algorithm of GM(1,N) forecasting model based on Simpson formula is proposed. Using the method of average relative error, we take empirical analysis on some time series models, and then find that the new algorithm has more accurate fitting precision than the original one. And the validity of the new algorithm is verified for some time series. The new algorithm is worth to be tried on establishing GM(1,N) forecasting model. And the reasonable application of GM(1,N) forecasting model has a practical significance. [ABSTRACT FROM AUTHOR]

Considering that the global optimal solutions often locate on or near the boundary of the feasible region for many constrained optimization problems, a novel genetic algorithm was proposed in this paper. The basic idea of the proposed algorithm was to put feasible solutions and infeasible solutions into two different containers respectively. Subsequently, a new designed crossover operator (named inward-outward crossover operator) was used to a feasible solution and a infeasible solution, then a line search along a potential decent direction was used to improve the offspring so as to find a good solution on or near to the boundary of feasible region. By this search procedure, the possibility for obtaining the globally optimal solution is obviously enhanced, and similarly, the convergent speed is also strengthened. The "particle swarm mutation" inherited the advantages of Particle Swarm Optimization (PSO) algorithm and searched for the potential solution along the direction of the best current particle and the direction of the best individual of the whole swarm in the past. Selection operator retained a constant rate of infeasible solutions. Numerical results indicate that the proposed algorithm can be efficient to get global optimal solutions or near to them in smaller population and less iteration times. [ABSTRACT FROM AUTHOR]