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A self-adaptive parameter optimization algorithm in a real-time parallel image processing system.

Li G, Zhang X, Zhao J, Zhang H, Ye J, Zhang W - ScientificWorldJournal (2013)

Bottom Line: Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle.We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC.Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 2, Yikuang Street, Harbin 150001, China.

ABSTRACT
Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle. We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC. Compared with current load balance algorithm, the algorithm proposed in this paper is proceeded under an unknown informed status about the maximum load and the current load of the processors, which means it has great extensibility. Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.

No MeSH data available.


The optimization results for different γf and α.
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Related In: Results  -  Collection


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fig2: The optimization results for different γf and α.

Mentions: Assume that Ωmax⁡⁡ = 400, λ1 = 0.25, λ2 = 0.5, Λ = 5, kT1 = 480, kT2 = 240, γf = 1, α = 0.6, and servo cycle T = 1000. Figure 1 shows the effects of the parameter optimization results of the SALB algorithm. The horizontal coordinates represent the sampling number. The vertical coordinates represent different values in the following different figures. Figure 1(a) represents the value of the state in the capacity rate evaluation function. Figures 1(b), 1(c), and 1(d) represent the number of processing results, capacity rate, and values of adjustable parameters, respectively. Figure 1(e) shows the overall capacity rate of the processor. Figures 1(h), 1(g), and 1(f) represent the number of processing results, capacity rate, and values of adjustable parameters, respectively. The first flip in state (state = −1) when the sampling size was 214 indicates that the initial data of the next servo cycle has changed and is awaiting the receiving state in the already received state, as shown in Figure 2. The subsequent state-repeated shocks indicate that the values of adjustable parameters simply satisfy the values of the maximum computational amount achieved by the processor. Moreover, Figures 1(b) and 1(f) indicate that the adjustable parameters of the ALBPO algorithms monotonically increase first and then remain unchanged, and thus, the adjustable parameters can be adjusted using the ALBPO algorithm to achieve the optimal control point. Changes in the number of the processing results also confirm the adjustment effect of the ALBPO algorithm. In addition, as can be seen in Figures 1(e), 1(c), and 1(g), the overall capacity rate of the two algorithms both changed at a very low rate during processing, denoting that the system can still be regulated when the system is initializing, even if the parameters of the artificial settings present deviations.


A self-adaptive parameter optimization algorithm in a real-time parallel image processing system.

Li G, Zhang X, Zhao J, Zhang H, Ye J, Zhang W - ScientificWorldJournal (2013)

The optimization results for different γf and α.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794567&req=5

fig2: The optimization results for different γf and α.
Mentions: Assume that Ωmax⁡⁡ = 400, λ1 = 0.25, λ2 = 0.5, Λ = 5, kT1 = 480, kT2 = 240, γf = 1, α = 0.6, and servo cycle T = 1000. Figure 1 shows the effects of the parameter optimization results of the SALB algorithm. The horizontal coordinates represent the sampling number. The vertical coordinates represent different values in the following different figures. Figure 1(a) represents the value of the state in the capacity rate evaluation function. Figures 1(b), 1(c), and 1(d) represent the number of processing results, capacity rate, and values of adjustable parameters, respectively. Figure 1(e) shows the overall capacity rate of the processor. Figures 1(h), 1(g), and 1(f) represent the number of processing results, capacity rate, and values of adjustable parameters, respectively. The first flip in state (state = −1) when the sampling size was 214 indicates that the initial data of the next servo cycle has changed and is awaiting the receiving state in the already received state, as shown in Figure 2. The subsequent state-repeated shocks indicate that the values of adjustable parameters simply satisfy the values of the maximum computational amount achieved by the processor. Moreover, Figures 1(b) and 1(f) indicate that the adjustable parameters of the ALBPO algorithms monotonically increase first and then remain unchanged, and thus, the adjustable parameters can be adjusted using the ALBPO algorithm to achieve the optimal control point. Changes in the number of the processing results also confirm the adjustment effect of the ALBPO algorithm. In addition, as can be seen in Figures 1(e), 1(c), and 1(g), the overall capacity rate of the two algorithms both changed at a very low rate during processing, denoting that the system can still be regulated when the system is initializing, even if the parameters of the artificial settings present deviations.

Bottom Line: Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle.We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC.Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 2, Yikuang Street, Harbin 150001, China.

ABSTRACT
Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle. We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC. Compared with current load balance algorithm, the algorithm proposed in this paper is proceeded under an unknown informed status about the maximum load and the current load of the processors, which means it has great extensibility. Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.

No MeSH data available.