The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the origin...The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.展开更多
To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase spa...To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.展开更多
An adaptive blind support vector machine equalizer（ABSVME） is presented in this paper.The method is based upon least square support vector machine（LSSVM）,and stems from signal feature reconstruction idea.By oversa...An adaptive blind support vector machine equalizer（ABSVME） is presented in this paper.The method is based upon least square support vector machine（LSSVM）,and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.展开更多
为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取...为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取网络——掩蔽时空频融合网络(masking spatio-temporal frequency fusion network,MSTFFNet)从脑电信号中提取大脑活动信息,深入挖掘大脑活动变化背后的语义,同时提取时空频信息;其次,引入跨模态对比学习,对齐脑电、文本、图像特征,以便生成阶段使用;然后,提出级联视频扩散模型,具体来说,先利用稳定扩散模型以脑电特征为条件生成参考视频帧,接着以视频帧为参考,融入运动矢量,引入视频扩散模型捕捉视频时间特征;最终生成高质量视频。结果表明,该模型在重建视频的主体、动作、颜色、语义等方面表现较好。可见利用脑电信号可以捕获大脑活动的视觉与语义信息,从而重建高保真度和视觉真实性的视频。展开更多
针对常规虚拟空间矢量脉宽调制VSVPWM(virtual space vector pulse-width modulation)在高调制深度下,母线中点电位存在不可控区域问题,提出1种可在整个线性调制区实现中点电位快速均衡的优化型VSVPWM算法。基于VSVPWM母线中点电位均衡...针对常规虚拟空间矢量脉宽调制VSVPWM(virtual space vector pulse-width modulation)在高调制深度下,母线中点电位存在不可控区域问题,提出1种可在整个线性调制区实现中点电位快速均衡的优化型VSVPWM算法。基于VSVPWM母线中点电位均衡原理重构虚拟中矢量,为保证重构后虚拟中矢量的均压性能,同时提高算法对系统参数摄动的适应性,设计用于中点电压闭环控制的模糊推理确定重构因子对VSVPWM进行优化。即使在直流母线电容不对称情况下,优化型VSVPWM仍可实现线性调制区全范围的快速均压控制。进一步在60°坐标系内对算法简化,参考电压矢量子区域的判断条件、伏秒平衡方程组从30个减少至5个。实验结果表明,该控制策略具有良好性能,均压调节时间显著降低,直流母线中点电位稳态偏差可控制在1.14%内,相较于传统VSVPWM,并网电流总谐波含量更低。展开更多
基金the National Natural Science Foundation of China (60303029)
文摘The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.
基金Sponsored by the National Eleventh Five year Plan Key Project of Ministry of Science and Technology of China (Grant No. 2006BAJ03A05-05)
文摘To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.
基金Supported by the National Natural Science Foundation of China(60772056)the Postdoctoral Science Foundation of China(20070421094)
文摘An adaptive blind support vector machine equalizer（ABSVME） is presented in this paper.The method is based upon least square support vector machine（LSSVM）,and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.
文摘为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取网络——掩蔽时空频融合网络(masking spatio-temporal frequency fusion network,MSTFFNet)从脑电信号中提取大脑活动信息,深入挖掘大脑活动变化背后的语义,同时提取时空频信息;其次,引入跨模态对比学习,对齐脑电、文本、图像特征,以便生成阶段使用;然后,提出级联视频扩散模型,具体来说,先利用稳定扩散模型以脑电特征为条件生成参考视频帧,接着以视频帧为参考,融入运动矢量,引入视频扩散模型捕捉视频时间特征;最终生成高质量视频。结果表明,该模型在重建视频的主体、动作、颜色、语义等方面表现较好。可见利用脑电信号可以捕获大脑活动的视觉与语义信息,从而重建高保真度和视觉真实性的视频。
文摘针对常规虚拟空间矢量脉宽调制VSVPWM(virtual space vector pulse-width modulation)在高调制深度下,母线中点电位存在不可控区域问题,提出1种可在整个线性调制区实现中点电位快速均衡的优化型VSVPWM算法。基于VSVPWM母线中点电位均衡原理重构虚拟中矢量,为保证重构后虚拟中矢量的均压性能,同时提高算法对系统参数摄动的适应性,设计用于中点电压闭环控制的模糊推理确定重构因子对VSVPWM进行优化。即使在直流母线电容不对称情况下,优化型VSVPWM仍可实现线性调制区全范围的快速均压控制。进一步在60°坐标系内对算法简化,参考电压矢量子区域的判断条件、伏秒平衡方程组从30个减少至5个。实验结果表明,该控制策略具有良好性能,均压调节时间显著降低,直流母线中点电位稳态偏差可控制在1.14%内,相较于传统VSVPWM,并网电流总谐波含量更低。
