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This paper proposes a concurrent implementation of a background subtraction algorithm suitable for monochromatic video sequences. In the training period, a set of frames is used to determine an estimate of the scene background, as well as background noise. In the test period, each new frame of the video sequence is compared to the background model, and foreground objects are obtained taking into account the background noise estimate. Shadows and highlights are also detected and removed. Concurrent programming tools and techniques, such as multithreading, are used to implement a concurrent solution for this problem, and combined with other optimization tools (such as destination word accumulation for morphological operators and the use of lookup tables for shadow detection/removal) to achieve the highest frame rate possible. Experimental results comprise a comparison of the performance including progressively each optimization tool, and they show that the fully optimized algorithm is much faster than a naive sequential implementation. Also, experiments performed on two-way processor and on dual-core processor computers indicate that the proposed algorithm is suited for the recent multi-core technology. [ABSTRACT FROM AUTHOR]

A temporal flexible planning problem that involves contingent and requirement events can be formulated as a simple temporal network with uncertainty (STNU). An STNU is controllable when there is a strategy for executing the requirement events (or actions) in such a way that all the conditions involving contingent events can be satisfied in all situations. The most interesting and useful controllability property is dynamic controllability in which the remaining actions in an STNU can always be scheduled under all possible feasible durations of future contingent events when all the past contingent events are known. In this paper, we propose and study a novel problem of assigning bounds on the duration of each requirement link in order for the resulting STNU to be dynamically controllable and to minimize the total cost over the allowed durations of all requirement links. We first prove the NP hardness of the problem with a linear cost function. We then formulate the dynamic controllability of an STNU as the constraints in a nonlinear optimization problem. Finally, we present methods for reducing the number of constraints in order to make the problem tractable and to demonstrate the computational performance of our methods. [ABSTRACT FROM AUTHOR]

For solving complex optimization problems in some engineering applications, intelligent optimization algorithms based on biological mechanisms have better performance than traditional optimization algorithms. Most of these intelligent algorithms, however, have disadvantages in population diversity and preservation of elitist antibody genes, which lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. Drawing inspiration from the features of major histocompatibility complex (MHC) in the biological immune system, we propose a novel MHC-inspired antibody clone algorithm (ACAMHC) for solving the above problems. ACAMHC preserves elitist antibody genes through the MHC strings that emulate the MHC haplotype in order to improve its local search capability; it improves the antibody population diversity by gene mutation that mimick the MHC polymorphism to enhance its global search capability. To expand the antibody search space, ACAMHC will add some new random immigrant antibodies with a certain ratio. The convergence of ACAMHC is theoretically proven. The experiments of ACAMHC compared with the canonical clone selection algorithm (CLONALG) on 20 benchmark functions are carried out. The experimental results indicate that the performance of ACAMHC is better than that of CLONALG. The ACAMHC algorithm provides new opportunities for solving previously intractable optimization problems. [ABSTRACT FROM AUTHOR]

Multi-objective differential evolution (MODE) is proposed to handle economic environmental dispatch problem, which is a multi-objective optimization problem (MOOPs) with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true Pareto-optimal set. Numerical results for two sample test systems have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of economic environmental dispatch problem in one single run. Simulation results with the proposed approach have been validated with Non-dominated Sorting Genetic Algorithm-II. [ABSTRACT FROM AUTHOR]