Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
为了解决RRT^(*)(rapidly-exploring random tree star)算法在搜索过程中速度低下和冗余节点过多,路径代价等问题,在RRT^(*)算法的基础上提出一种A-RRT^(*)算法,A-RRT^(*)算法通过融合A^(*)算法中的代价函数和使用了动态步长策略有效缩...为了解决RRT^(*)(rapidly-exploring random tree star)算法在搜索过程中速度低下和冗余节点过多,路径代价等问题,在RRT^(*)算法的基础上提出一种A-RRT^(*)算法,A-RRT^(*)算法通过融合A^(*)算法中的代价函数和使用了动态步长策略有效缩短了路径长度提升路径质量,改进剪枝策略减少了树搜索的冗余节点。根据算法在简单、复杂和密集环境下的仿真结果显示,在密集环境下A-RRT^(*)算法的无效冗余节点剪除94.29%、内存缩减了94.29%、搜索时间提高了96.28%、迭代次数缩减了91.49%、路径距离缩短了10.18%。为了防止生成的路径不平整而使机械臂在运行中造成损伤,利用了三次B样条对路径进行了优化,通过三维机械臂仿真也可得出优化后的路径更加平滑,减少了机械臂在运行过程中的关节波动,更有利于机械臂的运行,进一步验证了算法在机械臂运行中的有效性。展开更多
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
文摘为了解决RRT^(*)(rapidly-exploring random tree star)算法在搜索过程中速度低下和冗余节点过多,路径代价等问题,在RRT^(*)算法的基础上提出一种A-RRT^(*)算法,A-RRT^(*)算法通过融合A^(*)算法中的代价函数和使用了动态步长策略有效缩短了路径长度提升路径质量,改进剪枝策略减少了树搜索的冗余节点。根据算法在简单、复杂和密集环境下的仿真结果显示,在密集环境下A-RRT^(*)算法的无效冗余节点剪除94.29%、内存缩减了94.29%、搜索时间提高了96.28%、迭代次数缩减了91.49%、路径距离缩短了10.18%。为了防止生成的路径不平整而使机械臂在运行中造成损伤,利用了三次B样条对路径进行了优化,通过三维机械臂仿真也可得出优化后的路径更加平滑,减少了机械臂在运行过程中的关节波动,更有利于机械臂的运行,进一步验证了算法在机械臂运行中的有效性。
