Based on the dynamic triaxial test system and using the fitted wave of the Wenchuan earthquake and 1 Hz constant amplitude sinusoid,the paper compares the results of tests on undisturbed loess samples under different ...Based on the dynamic triaxial test system and using the fitted wave of the Wenchuan earthquake and 1 Hz constant amplitude sinusoid,the paper compares the results of tests on undisturbed loess samples under different loads and vibration modes but under same saturated conditions.Results of the comparative experiment show:The stress-strain curves have a similar trend under random seismic loading and constant amplitude sinusoidal loading,but the random seismic loading is more sensitive to failure strength of the undisturbed loess samples under the same stress.展开更多
智能体路径规划算法旨在规划某个智能体的行为轨迹,使其在不碰到障碍物的情况下安全且高效地从起始点到达目标点.目前智能体路径规划算法已经被广泛应用到各种重要的物理信息系统中,因此在实际投入使用前对算法进行测试,以评估其性能是...智能体路径规划算法旨在规划某个智能体的行为轨迹,使其在不碰到障碍物的情况下安全且高效地从起始点到达目标点.目前智能体路径规划算法已经被广泛应用到各种重要的物理信息系统中,因此在实际投入使用前对算法进行测试,以评估其性能是否满足需求就非常重要.然而,作为路径规划算法的输入,任务空间中威胁障碍物的分布形式复杂且多样.此外,路径规划算法在为每个测试用例规划路径时,通常需要较高的运行代价.为了提升路径规划算法的测试效率,将动态随机测试思想引入到路径规划算法中,提出了面向智能体路径规划算法的动态随机测试方法(dynamic random testing approach for intelligent agent path planning algorithms,DRT-PP).具体来说,DRT-PP对路径规划任务空间进行离散划分,并在每个子区域内引入威胁生成概率,进而构建测试剖面,该测试剖面可以作为测试策略在测试用例生成过程中使用.此外,DRT-PP在测试过程中通过动态调整测试剖面,使其逐渐优化,从而提升测试效率.实验结果显示,与随机测试及自适应随机测试相比,DRT-PP方法能够在保证测试用例多样性的同时,生成更多能够暴露被测算法性能缺陷的测试用例.展开更多
基金sponsored by the Earthquake Professional Special Program of China Earthquake Administration (2008419031)
文摘Based on the dynamic triaxial test system and using the fitted wave of the Wenchuan earthquake and 1 Hz constant amplitude sinusoid,the paper compares the results of tests on undisturbed loess samples under different loads and vibration modes but under same saturated conditions.Results of the comparative experiment show:The stress-strain curves have a similar trend under random seismic loading and constant amplitude sinusoidal loading,but the random seismic loading is more sensitive to failure strength of the undisturbed loess samples under the same stress.
文摘智能体路径规划算法旨在规划某个智能体的行为轨迹,使其在不碰到障碍物的情况下安全且高效地从起始点到达目标点.目前智能体路径规划算法已经被广泛应用到各种重要的物理信息系统中,因此在实际投入使用前对算法进行测试,以评估其性能是否满足需求就非常重要.然而,作为路径规划算法的输入,任务空间中威胁障碍物的分布形式复杂且多样.此外,路径规划算法在为每个测试用例规划路径时,通常需要较高的运行代价.为了提升路径规划算法的测试效率,将动态随机测试思想引入到路径规划算法中,提出了面向智能体路径规划算法的动态随机测试方法(dynamic random testing approach for intelligent agent path planning algorithms,DRT-PP).具体来说,DRT-PP对路径规划任务空间进行离散划分,并在每个子区域内引入威胁生成概率,进而构建测试剖面,该测试剖面可以作为测试策略在测试用例生成过程中使用.此外,DRT-PP在测试过程中通过动态调整测试剖面,使其逐渐优化,从而提升测试效率.实验结果显示,与随机测试及自适应随机测试相比,DRT-PP方法能够在保证测试用例多样性的同时,生成更多能够暴露被测算法性能缺陷的测试用例.
