A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local o...A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local optima. Optimal Identification of unknown groundwater pollution sources poses similar challenges. Optimization based methodology is often applied to identify the unknown source characteristics such as location and flux release history over time, in a polluted aquifer. Optimization based models for identification of these characteristics of unknown ground-water pollution sources rely on comparing the simulated effects of candidate solutions to the observed effects in terms of pollutant concentration at specified sparse spatiotemporal locations. The optimization model minimizes the difference between the observed pollutant concentration measurements and simulated pollutant concentration measurements. This essentially constitutes the objective function of the optimization model. However, the mathematical formulation of the objective function can significantly affect the accuracy of the results by altering the response contour of the solution space. In this study, two separate mathematical formulations of the objective function are compared for accuracy, by incorporating different scenarios of unknown groundwater pollution source identification problem. Simulated Annealing (SA) is used as the solution algorithm for the optimization model. Different mathematical formulations of the objective function for minimizing the difference between the observed and simulated pollutant concentration measurements show different levels of accuracy in source identification results. These evaluation results demonstrate the impact of objective function formulation on the optimal identification, and provide a basis for choosing an appropriate mathematical formulation for unknown pollution source identification in contaminated aquifers.展开更多
利用空间目标雷达散射截面(Radar Cross section,RCS)序列开展空间目标结构识别是空间态势感知的重要组成部分,针对RCS序列受目标物理特性、姿态特性影响大,序列信号非平稳特征明显的问题,提出利用动态时间规整(dynamic time warping,D...利用空间目标雷达散射截面(Radar Cross section,RCS)序列开展空间目标结构识别是空间态势感知的重要组成部分,针对RCS序列受目标物理特性、姿态特性影响大,序列信号非平稳特征明显的问题,提出利用动态时间规整(dynamic time warping,DTW)算法解决空间目标结构特征识别的问题。首先介绍了DTW算法的原理与特点,对算法原理及适用范围进行了分析;然后针对空间结构目标识别问题的特点,提出一种生成仿真数据的方法并分析了利用DTW算法的具体步骤;最后,利用一组仿真测试数据对算法进行了仿真验证。分析结果表明,DTW算法在解决利用RCS序列进行目标结构识别这一问题中具有鲁棒性强,识别准确的特点。展开更多
文摘A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local optima. Optimal Identification of unknown groundwater pollution sources poses similar challenges. Optimization based methodology is often applied to identify the unknown source characteristics such as location and flux release history over time, in a polluted aquifer. Optimization based models for identification of these characteristics of unknown ground-water pollution sources rely on comparing the simulated effects of candidate solutions to the observed effects in terms of pollutant concentration at specified sparse spatiotemporal locations. The optimization model minimizes the difference between the observed pollutant concentration measurements and simulated pollutant concentration measurements. This essentially constitutes the objective function of the optimization model. However, the mathematical formulation of the objective function can significantly affect the accuracy of the results by altering the response contour of the solution space. In this study, two separate mathematical formulations of the objective function are compared for accuracy, by incorporating different scenarios of unknown groundwater pollution source identification problem. Simulated Annealing (SA) is used as the solution algorithm for the optimization model. Different mathematical formulations of the objective function for minimizing the difference between the observed and simulated pollutant concentration measurements show different levels of accuracy in source identification results. These evaluation results demonstrate the impact of objective function formulation on the optimal identification, and provide a basis for choosing an appropriate mathematical formulation for unknown pollution source identification in contaminated aquifers.
文摘利用空间目标雷达散射截面(Radar Cross section,RCS)序列开展空间目标结构识别是空间态势感知的重要组成部分,针对RCS序列受目标物理特性、姿态特性影响大,序列信号非平稳特征明显的问题,提出利用动态时间规整(dynamic time warping,DTW)算法解决空间目标结构特征识别的问题。首先介绍了DTW算法的原理与特点,对算法原理及适用范围进行了分析;然后针对空间结构目标识别问题的特点,提出一种生成仿真数据的方法并分析了利用DTW算法的具体步骤;最后,利用一组仿真测试数据对算法进行了仿真验证。分析结果表明,DTW算法在解决利用RCS序列进行目标结构识别这一问题中具有鲁棒性强,识别准确的特点。
