Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high com...Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.展开更多
保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive princip...保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。展开更多
为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模...为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模态分解子模态数,从而将具有强非线性的原始功率序列分解为一组相对平稳的子模态。其次,利用灰色关联度分析计算高维气象特征与功率序列的关联度值并进行排序分层,利用主成分分析提取各分层特征变量的第一主成分,实现对高维气象特征的降维。最后,引入COOT算法对门控循环单元预测模型的超参数进行优化,加速模型收敛速度,提高模型预测精度。对贵州某风电场的实测数据进行仿真分析,结果表明:相较于传统GRU模型的预测结果,所提方法的均方根误差、平均绝对误差、平均绝对百分误差分别下降了67.41%、72.25%、45.69%,且预测精度高于其他4种组合预测模型,有效提高了超短期风电功率预测精度。展开更多
Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conve...Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conversed into the independent indexes of principal component based on the principal component analysis method. The results showed that the rice kernel types influenced the meanings of principal components indexes. For long and short rice, the first principal component was comprehensive index. But the second principal component was springiness for the short rice, while it was adhesiveness for long rice. Therefore, the first principal component can be used to express the quality of cooked rice with a few of indexes, and the rice type can be recognized according to the second principal component.展开更多
To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on princip...To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.展开更多
文摘Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.
文摘保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。
文摘为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模态分解子模态数,从而将具有强非线性的原始功率序列分解为一组相对平稳的子模态。其次,利用灰色关联度分析计算高维气象特征与功率序列的关联度值并进行排序分层,利用主成分分析提取各分层特征变量的第一主成分,实现对高维气象特征的降维。最后,引入COOT算法对门控循环单元预测模型的超参数进行优化,加速模型收敛速度,提高模型预测精度。对贵州某风电场的实测数据进行仿真分析,结果表明:相较于传统GRU模型的预测结果,所提方法的均方根误差、平均绝对误差、平均绝对百分误差分别下降了67.41%、72.25%、45.69%,且预测精度高于其他4种组合预测模型,有效提高了超短期风电功率预测精度。
基金Education Department of Heilongjiang Province in China for the Oversea Researcher Projects(1151HZ01,10531002)
文摘Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conversed into the independent indexes of principal component based on the principal component analysis method. The results showed that the rice kernel types influenced the meanings of principal components indexes. For long and short rice, the first principal component was comprehensive index. But the second principal component was springiness for the short rice, while it was adhesiveness for long rice. Therefore, the first principal component can be used to express the quality of cooked rice with a few of indexes, and the rice type can be recognized according to the second principal component.
基金supported by the National Natural Science Foundation of China(71401052)the Key Project of National Social Science Fund of China(12AZD108)+2 种基金the Doctoral Fund of Ministry of Education(20120094120024)the Philosophy and Social Science Fund of Jiangsu Province Universities(2013SJD630073)the Central University Basic Service Project Fee of Hohai University(2011B09914)
文摘To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.
