It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or...It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is of utmost importance. Previous study mostly relied on cross-correlation, coherence functions or model based approaches such as Granger causality or dynamic causal modeling. In this work the information transfer index (ITI) was introduced to describe the information flows between motor cortex and muscle. Based on the information entropy the ITI can detect both linear and nonlinear interaction between two signals and thus represent a very comprehensive way to define the causality strength. The applicability of ITI is investigated based on simulations and electroencephalogram (EEG), surface electromyography (sEMG) recordings in a simple motor task.展开更多
为了提高无人机遥感对冬小麦叶面积指数(leaf area index,LAI)反演模型的精度与泛化能力,该研究利用无人机搭载多光谱相机获取不同氮素处理和不同复种方式的冬小麦生长实测数据,结合PROSAIL辐射传输模型生成包含机理信息的模拟数据,基...为了提高无人机遥感对冬小麦叶面积指数(leaf area index,LAI)反演模型的精度与泛化能力,该研究利用无人机搭载多光谱相机获取不同氮素处理和不同复种方式的冬小麦生长实测数据,结合PROSAIL辐射传输模型生成包含机理信息的模拟数据,基于不同组合方式建立了5种LAI反演混合数据集,结合多种机器学习方法,以期构建经验与机理相结合的LAI高精度反演模型。由于LAI反演受近红外波段(near infrared,NIR)反射率影响大,该研究筛选7种与NIR波段相关的植被指数提取冬小麦光谱特征,构建与混合数据集LAI的相关系数矩阵,进一步探究不同光谱特征对冬小麦LAI的影响程度。在此基础上,采用具有代表性和普适性的4种机器学习方法,即贝叶斯岭回归模型、线性回归模型、弹性网络模型和支持向量回归模型,构建不同冬小麦LAI反演模型,用以评估基于半经验半机理数据反演冬小麦LAI的可行性,进一步探索其对不同氮素水平和复种方式的冬小麦长势评估能力。结果表明:1)筛选的与NIR波段相关的植被指数与冬小麦LAI之间存在较强的相关性,其中归一化差异植被指数、增强植被指数、归一化差异红边指数、比值植被指数、红边叶绿素植被指数、土壤调节植被指数与LAI呈正相关,结构不敏感色素植被指数与LAI呈负相关;2)辐射传输模型中体现了冬小麦LAI影响太阳光线传播的机理,结果表明,与实测数据混合建立的模型,具有较强的鲁棒性和泛化能力。相比于其他3种模型,支持向量回归模型在各种数据组合下均取得了较好的LAI预测性能,在C1、C2、C3、C4这4种训练-测试组合的训练集中R^(2)依次为0.86、0.87、0.88、0.91,RMSE依次为0.47、0.45、0.45、0.41;在测试集的R^(2)依次为0.85、0.19、0.89、0.87,RMSE依次为0.45、1.31、0.49、0.50;3)使用支持向量机生成试验区LAI反演图,对4种氮素水平和2种复种方式的冬小麦长势评估,结果表明,适当的施加氮素处理能提高冬小麦LAI值,麦-豆复种方式下的冬小麦LAI值普遍高于麦-玉复种的LAI值。该研究为冬小麦LAI的反演提供了一种有效的方法,并为高效评估冬小麦长势研究提供了参考。展开更多
文摘It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is of utmost importance. Previous study mostly relied on cross-correlation, coherence functions or model based approaches such as Granger causality or dynamic causal modeling. In this work the information transfer index (ITI) was introduced to describe the information flows between motor cortex and muscle. Based on the information entropy the ITI can detect both linear and nonlinear interaction between two signals and thus represent a very comprehensive way to define the causality strength. The applicability of ITI is investigated based on simulations and electroencephalogram (EEG), surface electromyography (sEMG) recordings in a simple motor task.
文摘为了提高无人机遥感对冬小麦叶面积指数(leaf area index,LAI)反演模型的精度与泛化能力,该研究利用无人机搭载多光谱相机获取不同氮素处理和不同复种方式的冬小麦生长实测数据,结合PROSAIL辐射传输模型生成包含机理信息的模拟数据,基于不同组合方式建立了5种LAI反演混合数据集,结合多种机器学习方法,以期构建经验与机理相结合的LAI高精度反演模型。由于LAI反演受近红外波段(near infrared,NIR)反射率影响大,该研究筛选7种与NIR波段相关的植被指数提取冬小麦光谱特征,构建与混合数据集LAI的相关系数矩阵,进一步探究不同光谱特征对冬小麦LAI的影响程度。在此基础上,采用具有代表性和普适性的4种机器学习方法,即贝叶斯岭回归模型、线性回归模型、弹性网络模型和支持向量回归模型,构建不同冬小麦LAI反演模型,用以评估基于半经验半机理数据反演冬小麦LAI的可行性,进一步探索其对不同氮素水平和复种方式的冬小麦长势评估能力。结果表明:1)筛选的与NIR波段相关的植被指数与冬小麦LAI之间存在较强的相关性,其中归一化差异植被指数、增强植被指数、归一化差异红边指数、比值植被指数、红边叶绿素植被指数、土壤调节植被指数与LAI呈正相关,结构不敏感色素植被指数与LAI呈负相关;2)辐射传输模型中体现了冬小麦LAI影响太阳光线传播的机理,结果表明,与实测数据混合建立的模型,具有较强的鲁棒性和泛化能力。相比于其他3种模型,支持向量回归模型在各种数据组合下均取得了较好的LAI预测性能,在C1、C2、C3、C4这4种训练-测试组合的训练集中R^(2)依次为0.86、0.87、0.88、0.91,RMSE依次为0.47、0.45、0.45、0.41;在测试集的R^(2)依次为0.85、0.19、0.89、0.87,RMSE依次为0.45、1.31、0.49、0.50;3)使用支持向量机生成试验区LAI反演图,对4种氮素水平和2种复种方式的冬小麦长势评估,结果表明,适当的施加氮素处理能提高冬小麦LAI值,麦-豆复种方式下的冬小麦LAI值普遍高于麦-玉复种的LAI值。该研究为冬小麦LAI的反演提供了一种有效的方法,并为高效评估冬小麦长势研究提供了参考。
