本文从人与人的相互作用和社会学、心理学的角度,对人群疏散中“非适应性”行为”的理论、计算机模型、模拟原理等进行了较为深入的研究,并以具体实例说明了Bu ild ing Exodus软件在人群疏散模拟分析中的具体应用,为完善该研究方向的计...本文从人与人的相互作用和社会学、心理学的角度,对人群疏散中“非适应性”行为”的理论、计算机模型、模拟原理等进行了较为深入的研究,并以具体实例说明了Bu ild ing Exodus软件在人群疏散模拟分析中的具体应用,为完善该研究方向的计算机模型和促进软件的开发提供理论和方法的指导。文章得出的结论是:“非适应”人群行为的研究需要从心理学和社会学的角度建立人群中个人行为与社会行为的理论框架。“非适应”人群行为的研究方法必须与紧急情况相结合,而紧急情况是一个由多个人员组成的复杂系统,大量人群环境的模拟需要涉及到人与人之间,人与环境之间的相互作用。展开更多
Traditional biomechanical analyses of human movement are generally derived from linear mathematics.While these methods can be useful in many situations,they do not describe behaviors in human systems that are predomin...Traditional biomechanical analyses of human movement are generally derived from linear mathematics.While these methods can be useful in many situations,they do not describe behaviors in human systems that are predominately nonlinear.For this reason,nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature.These analysis techniques have provided new insights into how systems(1) maintain pattern stability,(2) transition into new states,and(3) are governed by short-and long-term(fractal) correlational processes at different spatio-temporal scales.These different aspects of system dynamics are typically investigated using concepts related to variability,stability,complexity,and adaptability.The purpose of this paper is to compare and contrast these different concepts and demonstrate that,although related,these terms represent fundamentally different aspects of system dynamics.In particular,we argue that variability should not uniformly be equated with stability or complexity of movement.In addition,current dynamic stability measures based on nonlinear analysis methods(such as the finite maximal Lyapunov exponent) can reveal local instabilities in movement dynamics,but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored.Finally,systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.展开更多
文摘本文从人与人的相互作用和社会学、心理学的角度,对人群疏散中“非适应性”行为”的理论、计算机模型、模拟原理等进行了较为深入的研究,并以具体实例说明了Bu ild ing Exodus软件在人群疏散模拟分析中的具体应用,为完善该研究方向的计算机模型和促进软件的开发提供理论和方法的指导。文章得出的结论是:“非适应”人群行为的研究需要从心理学和社会学的角度建立人群中个人行为与社会行为的理论框架。“非适应”人群行为的研究方法必须与紧急情况相结合,而紧急情况是一个由多个人员组成的复杂系统,大量人群环境的模拟需要涉及到人与人之间,人与环境之间的相互作用。
文摘Traditional biomechanical analyses of human movement are generally derived from linear mathematics.While these methods can be useful in many situations,they do not describe behaviors in human systems that are predominately nonlinear.For this reason,nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature.These analysis techniques have provided new insights into how systems(1) maintain pattern stability,(2) transition into new states,and(3) are governed by short-and long-term(fractal) correlational processes at different spatio-temporal scales.These different aspects of system dynamics are typically investigated using concepts related to variability,stability,complexity,and adaptability.The purpose of this paper is to compare and contrast these different concepts and demonstrate that,although related,these terms represent fundamentally different aspects of system dynamics.In particular,we argue that variability should not uniformly be equated with stability or complexity of movement.In addition,current dynamic stability measures based on nonlinear analysis methods(such as the finite maximal Lyapunov exponent) can reveal local instabilities in movement dynamics,but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored.Finally,systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.
