Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes and the functioning of astrocytes c...
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Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes and the functioning of astrocytes control the spikes passing along synapses. Looking for small universal computing devices is a classical researching topic in computer science, whose aim is to construct computationally universal computing devices using less resources. In this work, we investigate small universal SNPA systems as both devices computing functions and devices generating sets of numbers. Specifically, SNPA systems with 77 neurons and 21 astrocytes can compute any Turing computable function;and it is obtained that 67 neurons and 18 astrocytes can generate the family of Turing computable numbers.
Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to ch...
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Column Generation (CG) technique is popularly applied in solving the crew scheduling problem of large size, which is generally modeled as an Integer Linear Programming (ILP) problem. The traditional CG algorithms for ...
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In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
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There are many multi-objective planning problems in emergency decision-making domain. HTN planners were widely used in emergency decision-making, while they have limited ability to solve optimization, especially multi...
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There are many multi-objective planning problems in emergency decision-making domain. HTN planners were widely used in emergency decision-making, while they have limited ability to solve optimization, especially multi-objective optimization. Aimed to handling multi-objective in HTN planners, this paper proposes a novel method based on SHOP2, which is a domain-independent state-based forward HTN planner. The method uses a weighted vector to mirror the decision maker's performance for various objectives and an anytime search algorithm to improve the ability to find the best solution plan. Finally, a case of emergency evacuation is given to testify the effectiveness of the method.
This paper develops a routing method to control the picker congestion that challenges the traditional assumption regarding the narrow-aisle order picking system. We proposes a new routing algorithm based on Ant Colony...
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This paper develops a routing method to control the picker congestion that challenges the traditional assumption regarding the narrow-aisle order picking system. We proposes a new routing algorithm based on Ant Colony Optimization (ACO) for two order pickers (A-TOP) with congestion consideration. Using two extended dedicated heuristics with congestion consideration as reference group, a comprehensive simulation study is conducted to evaluate the effectiveness of A-TOP. The simulation proves that A-TOP achieves the shortest total picking time in most instances and performs well in dealing with the congestion. The impacts of warehouse layout, order size, and pick:walk-time ratio on A-TOP and system performance are analyzed as well. A-TOP can adapt to different warehouse configurations, meanwhile, it can be easily extended to the situation with more than two order pickers.
This paper addresses the bipartite flock control problem with or without a virtual leader. Using structurally balanced signed graph theory and LaSalle’s invariance principle, we prove that the algorithm guarantees a ...
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ISBN:
(纸本)9781479900305
This paper addresses the bipartite flock control problem with or without a virtual leader. Using structurally balanced signed graph theory and LaSalle’s invariance principle, we prove that the algorithm guarantees a bipartite flocking behavior. In such a collective motion, the whole group separates into two clusters, in each of which all individuals move with the same direction. Meanwhile, every pair of agents in different clusters moves with opposite directions. Moreover, all agents in the two separated clusters approach a common velocity magnitude, and collision avoidance among each cluster is ensured as well. Finally, the proposed bipartite flock control method are examined by numerical simulations. The bipartite flocking motion addressed by this paper has its references in both natural collective motions and human group behaviors such as predator-prey and panic escaping scenarios.
A new method to calculate the corridor of Reentry Launch Vehicles is proposed, based on their dynamic properties. The upper boundary of the corridor can be calculated by the skipping trajectories from the lower bounda...
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A new method to calculate the corridor of Reentry Launch Vehicles is proposed, based on their dynamic properties. The upper boundary of the corridor can be calculated by the skipping trajectories from the lower boundary of the corridor. By combination with a specific kinematic equations and a numerical simulation, the reliability and optimality of the method is analyzed. The results also show that the traditional reentry corridor estimated by quasi-equilibrium gliding conditions has its conservativeness, the reentry corridor that the new method calculated can better reflect the RLV's maneuvers abilities. Moreover, compared to the general methods, the direct and indirect methods to calculate the corridor, which are usually used to solve optimization problems, the proposed method has a smaller amount of calculation, shorter calculation time, stronger convergence, thus has stronger practicability.
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