Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve pre...Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.展开更多
为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降...为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降低了重构矩阵的复杂度。考虑到多通道不一致性对重构矩阵的影响,引入0位校正算法,提高了重构方法的稳健性。最后应用重构后的协方差矩阵进行子空间类波达方向估计(direction of arrival,DOA)。实验仿真证明,该特殊重构矩阵在实数化下与原矩阵重构能力相同;当快拍数为100、信噪比为0 dB时,双信源分辨力较重构前由74%提高到95%以上;理论重构运算复杂度降低到原来的53.99%。展开更多
We prove that the inequality holds, when a m × n real matrix X = (xij) whose entries are not all equal to 0 satisfies Therefore we not only generalize the results of Horst Alzer [2] from non-negative matrix to re...We prove that the inequality holds, when a m × n real matrix X = (xij) whose entries are not all equal to 0 satisfies Therefore we not only generalize the results of Horst Alzer [2] from non-negative matrix to real matrix, but also complete a result of E R van Dam [1], which indicated that the best possible upper bound is equal to 1 for real matrix.展开更多
AIM:To assess expression of matrix metalloproteinases 2(MMP2)and MMP9 in gastric cancer,superficial gastritis and normal mucosa,and to measure metalloproteinase activity.METHODS:MMP2 and MMP9 mRNA expression was deter...AIM:To assess expression of matrix metalloproteinases 2(MMP2)and MMP9 in gastric cancer,superficial gastritis and normal mucosa,and to measure metalloproteinase activity.METHODS:MMP2 and MMP9 mRNA expression was determined by quantitative real-time polymerase chain reaction.Normalization was carried out using three different factors.Proteins were analyzed by quantitative gelatin zymography(qGZ).RESULTS:18S ribosomal RNA(18SRNA)was very highly expressed,while hypoxanthine ribosyltransferase-1(HPRT-1)was moderately expressed.MMP2 was highly expressed,while MMP9 was not detected or lowly expressed in normal tissues,moderately or highly expressed in gastritis and highly expressed in cancer.Relative expression of 18SRNA and HPRT-1 showed no significant differences.Significant differences in MMP2 and MMP9 were found between cancer and normal tissue,but not between gastritis and normal tissue.Absolute quantification of MMP9 echoed this pattern,but differential expression of MMP2 proved conflictive.Analysis by qGZ indicated significant differences between cancer and normal tissue in MMP-2,total MMP-9,250 and 110 kDa bands.CONCLUSION:MMP9 expression is enhanced in gastric cancer compared to normal mucosa;interpretation of differential expression of MMP2 is difficult to establish.展开更多
针对目标检测算法在交通标志检测中存在的不足,文中提出了一种融合感受野增强模块和注意力机制的交通标志检测算法。该算法在YOLOv5(You Only Look Once version 5)算法的基础上改进,选用感受野模块(Receptive Field Block,RFB)替换原...针对目标检测算法在交通标志检测中存在的不足,文中提出了一种融合感受野增强模块和注意力机制的交通标志检测算法。该算法在YOLOv5(You Only Look Once version 5)算法的基础上改进,选用感受野模块(Receptive Field Block,RFB)替换原骨干网络中的空间金字塔池化(Spatial Pyramid Pooling,SPP)模块,在特征融合网络中嵌入高效通道注意模块(Efficient Channel Attention Module,ECAM)和卷积块注意模块(Convolutional Block Attention Module,CBAM),选用矩阵非极大值抑制(Matrix Non-Maximum Suppression,Matrix NMS)筛选候选框以提升算法的检测精度和检测速度。实验结果表明,在模型参数量与原网络相比未变化的前提下,该算法的均值平均精度达到了82.31%,与原算法相比提升了8.59%,检测速度达到了51.89 frame·s^(-1),且该算法在各个测试场景中未出现错检漏检现象,证明其泛化能力优于原算法,可以实时检测交通标志。展开更多
For the lower bound about the determinant of Hadamard product of A and B, where A is a n × n real positive definite matrix and B is a n × n M-matrix, Jianzhou Liu [SLAM J. Matrix Anal. Appl., 18(2)(1997): 30...For the lower bound about the determinant of Hadamard product of A and B, where A is a n × n real positive definite matrix and B is a n × n M-matrix, Jianzhou Liu [SLAM J. Matrix Anal. Appl., 18(2)(1997): 305-311]obtained the estimated inequality as follows det(A o B)≥a11b11 nⅡk=2(bkk detAk/detAk-1+detBk/detBk-1(k-1Ei=1 aikaki/aii))=Ln(A,B),where Ak is kth order sequential principal sub-matrix of A. We establish an improved lower bound of the form Yn(A,B)=a11baa nⅡk=2(bkk detAk/detAk-1+akk detBk/detBk-1-detAdetBk/detak-1detBk-1)≥Ln(A,B).For more weaker and practical lower bound, Liu given thatdet(A o B)≥(nⅡi=1 bii)detA+(nⅡi=1 aii)detB(nⅡk=2 k-1Ei=1 aikaki/aiiakk)=(L)n(A,B).We further improve it as Yn(A,B)=(nⅡi=1 bii)detA+(nⅡi=1 aii)detB-(detA)(detB)+max1≤k≤n wn(A,B,k)≥(nⅡi=1 bii)detA+(nⅡi=1 aii)detB-(detA)(detB)≥(L)n(A,B).展开更多
基金National Natural Science Foundation of China(No.61701104)the “13th Five Year Plan” Research Foundation of Jilin Provincial Department of Education,China(No.JJKH2017018KJ)
文摘Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.
文摘为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降低了重构矩阵的复杂度。考虑到多通道不一致性对重构矩阵的影响,引入0位校正算法,提高了重构方法的稳健性。最后应用重构后的协方差矩阵进行子空间类波达方向估计(direction of arrival,DOA)。实验仿真证明,该特殊重构矩阵在实数化下与原矩阵重构能力相同;当快拍数为100、信噪比为0 dB时,双信源分辨力较重构前由74%提高到95%以上;理论重构运算复杂度降低到原来的53.99%。
基金Supported by the Science Foundation of Educational Commission of Fujian Province (JA03157)Supported by the Scientific Research Item of Putian University(20042002)
文摘We prove that the inequality holds, when a m × n real matrix X = (xij) whose entries are not all equal to 0 satisfies Therefore we not only generalize the results of Horst Alzer [2] from non-negative matrix to real matrix, but also complete a result of E R van Dam [1], which indicated that the best possible upper bound is equal to 1 for real matrix.
基金Supported by The National Council on Science and Technology (CONACYT:85675 and 79628)Institute of Public Health(POA: 2008-2010)Research Office of Veracruzana University and Public Education Secretariat(SEP-PROMEP-UV:PTC-319)
文摘AIM:To assess expression of matrix metalloproteinases 2(MMP2)and MMP9 in gastric cancer,superficial gastritis and normal mucosa,and to measure metalloproteinase activity.METHODS:MMP2 and MMP9 mRNA expression was determined by quantitative real-time polymerase chain reaction.Normalization was carried out using three different factors.Proteins were analyzed by quantitative gelatin zymography(qGZ).RESULTS:18S ribosomal RNA(18SRNA)was very highly expressed,while hypoxanthine ribosyltransferase-1(HPRT-1)was moderately expressed.MMP2 was highly expressed,while MMP9 was not detected or lowly expressed in normal tissues,moderately or highly expressed in gastritis and highly expressed in cancer.Relative expression of 18SRNA and HPRT-1 showed no significant differences.Significant differences in MMP2 and MMP9 were found between cancer and normal tissue,but not between gastritis and normal tissue.Absolute quantification of MMP9 echoed this pattern,but differential expression of MMP2 proved conflictive.Analysis by qGZ indicated significant differences between cancer and normal tissue in MMP-2,total MMP-9,250 and 110 kDa bands.CONCLUSION:MMP9 expression is enhanced in gastric cancer compared to normal mucosa;interpretation of differential expression of MMP2 is difficult to establish.
文摘针对目标检测算法在交通标志检测中存在的不足,文中提出了一种融合感受野增强模块和注意力机制的交通标志检测算法。该算法在YOLOv5(You Only Look Once version 5)算法的基础上改进,选用感受野模块(Receptive Field Block,RFB)替换原骨干网络中的空间金字塔池化(Spatial Pyramid Pooling,SPP)模块,在特征融合网络中嵌入高效通道注意模块(Efficient Channel Attention Module,ECAM)和卷积块注意模块(Convolutional Block Attention Module,CBAM),选用矩阵非极大值抑制(Matrix Non-Maximum Suppression,Matrix NMS)筛选候选框以提升算法的检测精度和检测速度。实验结果表明,在模型参数量与原网络相比未变化的前提下,该算法的均值平均精度达到了82.31%,与原算法相比提升了8.59%,检测速度达到了51.89 frame·s^(-1),且该算法在各个测试场景中未出现错检漏检现象,证明其泛化能力优于原算法,可以实时检测交通标志。
文摘For the lower bound about the determinant of Hadamard product of A and B, where A is a n × n real positive definite matrix and B is a n × n M-matrix, Jianzhou Liu [SLAM J. Matrix Anal. Appl., 18(2)(1997): 305-311]obtained the estimated inequality as follows det(A o B)≥a11b11 nⅡk=2(bkk detAk/detAk-1+detBk/detBk-1(k-1Ei=1 aikaki/aii))=Ln(A,B),where Ak is kth order sequential principal sub-matrix of A. We establish an improved lower bound of the form Yn(A,B)=a11baa nⅡk=2(bkk detAk/detAk-1+akk detBk/detBk-1-detAdetBk/detak-1detBk-1)≥Ln(A,B).For more weaker and practical lower bound, Liu given thatdet(A o B)≥(nⅡi=1 bii)detA+(nⅡi=1 aii)detB(nⅡk=2 k-1Ei=1 aikaki/aiiakk)=(L)n(A,B).We further improve it as Yn(A,B)=(nⅡi=1 bii)detA+(nⅡi=1 aii)detB-(detA)(detB)+max1≤k≤n wn(A,B,k)≥(nⅡi=1 bii)detA+(nⅡi=1 aii)detB-(detA)(detB)≥(L)n(A,B).
