Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between le...Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.展开更多
Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and ...Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar characterisation of time-varying behaviours registered over an extended period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented. The application of the compound measures is shown to correlate with complexity analysis. The correlation is demonstrated using two healthy subjects compared against a control group. This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time.展开更多
由于嵌入式中央处理器(Central Processing Unit, CPU)负载需要同时考虑CPU利用率、内存利用率等相关因素,导致对其预测时难度较大且无法保证精准度。因此,提出一种新的自适应预测算法。构建嵌入式CPU负载预测框架,对其负载数据预处理,...由于嵌入式中央处理器(Central Processing Unit, CPU)负载需要同时考虑CPU利用率、内存利用率等相关因素,导致对其预测时难度较大且无法保证精准度。因此,提出一种新的自适应预测算法。构建嵌入式CPU负载预测框架,对其负载数据预处理,降低非平稳数据对预测结果精度的影响;在整合移动平均自回归模型中加入周期变动因素,构建季节性差分自回归滑动平均模型,分析CPU负载数据时间序列变化特征;并对其迭代计算,得到季节性差分自回归滑动平均模型的参数和CPU负载预测结果。实验结果表明,所提方法的MAPE值低于25%,表明该方法的预测精度高。展开更多
文摘Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.
文摘Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar characterisation of time-varying behaviours registered over an extended period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented. The application of the compound measures is shown to correlate with complexity analysis. The correlation is demonstrated using two healthy subjects compared against a control group. This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time.
文摘由于嵌入式中央处理器(Central Processing Unit, CPU)负载需要同时考虑CPU利用率、内存利用率等相关因素,导致对其预测时难度较大且无法保证精准度。因此,提出一种新的自适应预测算法。构建嵌入式CPU负载预测框架,对其负载数据预处理,降低非平稳数据对预测结果精度的影响;在整合移动平均自回归模型中加入周期变动因素,构建季节性差分自回归滑动平均模型,分析CPU负载数据时间序列变化特征;并对其迭代计算,得到季节性差分自回归滑动平均模型的参数和CPU负载预测结果。实验结果表明,所提方法的MAPE值低于25%,表明该方法的预测精度高。
