针对含物料自动运输环节(automated material handling unit,AMHU)的个性化定制生产系统性能分析问题,文章着重考虑其随机批量运输的特点,提出了该系统的开排队网建模方法。对含AMHU的生产系统进行了描述,并建立其排队网模型;采用改良...针对含物料自动运输环节(automated material handling unit,AMHU)的个性化定制生产系统性能分析问题,文章着重考虑其随机批量运输的特点,提出了该系统的开排队网建模方法。对含AMHU的生产系统进行了描述,并建立其排队网模型;采用改良状态空间分解法,建立了节点状态空间模型,求解并计算出生产系统的性能指标值;与生产系统仿真模型的实验结果进行对比,验证了所提方法的精确性和有效性,为进一步优化配置该类生产系统的资源奠定了基础。展开更多
针对现有的共空域子空间(common special subspace decomposition,CSSD)算法在脑电信号(EEG)特征提取时,类内和类间的信号特征变化导致脑电信号特征值稳定性低、特征向量区分度差的问题,提出一种改进的CSSD特征提取方法,即基于KullbackL...针对现有的共空域子空间(common special subspace decomposition,CSSD)算法在脑电信号(EEG)特征提取时,类内和类间的信号特征变化导致脑电信号特征值稳定性低、特征向量区分度差的问题,提出一种改进的CSSD特征提取方法,即基于KullbackLeibler距离的共空域子空间分解法(KL-CSSD)。在传统CSSD算法的基础上利用Kullback-Leibler距离,最大化类间距离而最小化类内差异,提取鲁棒性较强的EEG信号特征。实验结果表明:该算法相对于传统CSSD有较好的特征向量区分度,有效提高了脑电信号的正确识别率。展开更多
Based on the fact that the variation of tile direction of arrival (DOA) isslower than that of the channel fading, the steering vector of the desired signal is estimatedfirstly using a subspace decomposition method and...Based on the fact that the variation of tile direction of arrival (DOA) isslower than that of the channel fading, the steering vector of the desired signal is estimatedfirstly using a subspace decomposition method and then a constrained condition is configured.Traffic signals are further employed to estimate the channel vector based on the constrained leastsquares criterion. We use the iterative least squares with projection (ILSP) algorithm initializedby the pilot to get the estimation. The accuracy of channel estimation and symbol detection can beprogressively increased through the iteration procedure of the ILSP algorithm. Simulation resultsdemonstrate that the proposed algorithm improves the system performance effectively compared withthe conventional 2-D RAKE receiver.展开更多
文摘针对含物料自动运输环节(automated material handling unit,AMHU)的个性化定制生产系统性能分析问题,文章着重考虑其随机批量运输的特点,提出了该系统的开排队网建模方法。对含AMHU的生产系统进行了描述,并建立其排队网模型;采用改良状态空间分解法,建立了节点状态空间模型,求解并计算出生产系统的性能指标值;与生产系统仿真模型的实验结果进行对比,验证了所提方法的精确性和有效性,为进一步优化配置该类生产系统的资源奠定了基础。
文摘针对现有的共空域子空间(common special subspace decomposition,CSSD)算法在脑电信号(EEG)特征提取时,类内和类间的信号特征变化导致脑电信号特征值稳定性低、特征向量区分度差的问题,提出一种改进的CSSD特征提取方法,即基于KullbackLeibler距离的共空域子空间分解法(KL-CSSD)。在传统CSSD算法的基础上利用Kullback-Leibler距离,最大化类间距离而最小化类内差异,提取鲁棒性较强的EEG信号特征。实验结果表明:该算法相对于传统CSSD有较好的特征向量区分度,有效提高了脑电信号的正确识别率。
基金The National Hi-Tech Development Plan (863-317-03-01-02-04-20).
文摘Based on the fact that the variation of tile direction of arrival (DOA) isslower than that of the channel fading, the steering vector of the desired signal is estimatedfirstly using a subspace decomposition method and then a constrained condition is configured.Traffic signals are further employed to estimate the channel vector based on the constrained leastsquares criterion. We use the iterative least squares with projection (ILSP) algorithm initializedby the pilot to get the estimation. The accuracy of channel estimation and symbol detection can beprogressively increased through the iteration procedure of the ILSP algorithm. Simulation resultsdemonstrate that the proposed algorithm improves the system performance effectively compared withthe conventional 2-D RAKE receiver.
