Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this stud...Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects （NTDs）. A hybrid particle swarm optimization/ant colony optimization （PSO/ACO） algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. Results The algorithm was easy to apply, with the accuracy of the results being 69.5%＋7.02% at the 95% confidence level. Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.展开更多
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid...The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.展开更多
为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规...为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规划效果。实验结果表明,与单独的改进PSO和ACO算法相比,本研究的融合改进种群算法和多因素地形适应度的路径规划算法具有更佳的路径规划效果,在3 km、6 km、9 km、12 km的路径规模下的转折点个数仅为33,105,239,543,路径长度仅为3.25 km, 6.33 km, 10.26 km, 13.44 km,明显低于改进PSO和ACO算法;与其他路径规划算法相比,本研究算法在复杂场景下能够得到最佳的路径,同时还能够保持路径的平滑性。以上结果表明,本研究设计的路径规划算法具有优秀的路径规划效果,能够应用于实际场景中的路径规划,具有较高的可行性和使用价值。展开更多
基金supported by National Natural Science Foundation of China(No.41101431)the fourth installment special funding of China Postdoctoral Science Foundation(No.201104003)+1 种基金China Postdoctoral Science Foundation(No.20100470004)the State Key Funds of Social Science Project(Research on Disability Prevention Measurement in China,No.09&ZD072)
文摘Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects （NTDs）. A hybrid particle swarm optimization/ant colony optimization （PSO/ACO） algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. Results The algorithm was easy to apply, with the accuracy of the results being 69.5%＋7.02% at the 95% confidence level. Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.
文摘The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.
文摘为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规划效果。实验结果表明,与单独的改进PSO和ACO算法相比,本研究的融合改进种群算法和多因素地形适应度的路径规划算法具有更佳的路径规划效果,在3 km、6 km、9 km、12 km的路径规模下的转折点个数仅为33,105,239,543,路径长度仅为3.25 km, 6.33 km, 10.26 km, 13.44 km,明显低于改进PSO和ACO算法;与其他路径规划算法相比,本研究算法在复杂场景下能够得到最佳的路径,同时还能够保持路径的平滑性。以上结果表明,本研究设计的路径规划算法具有优秀的路径规划效果,能够应用于实际场景中的路径规划,具有较高的可行性和使用价值。
