急性等容血液稀释自体血回输对老年骨科手术患者脑电双谱指数与肌松效应的影响

目的：观察急性等容血液稀释（ANH）自体血回输对老年骨科手术患者脑电双谱指数与肌松效应的影响,探究含有麻醉药成分的自体血回输对术后麻醉苏醒质量及安全性的影响。方法：择期骨科手术患者40例,年龄65～75岁,体质量55～80 kg,ASA Ⅰ～Ⅱ级,预计术中出血量≥600 mL。患者随机分为2组（n=20）：A组为进行ANH组,在麻醉诱导平稳后实施ANH,血细胞比容（Hct）目标值28%～30%;B组为对照组,即术中常规补液,不进行ANH。于术毕回输自体血时（T1）、回输10 min（T2）、20 min（T3）、30 min（T4）、40 min（T5）、50 min（T6）、60 min（T7）各时点观测两组患者脑电双频指数（BIS）、四个成串刺激（TOF值）及丙泊酚、顺式阿曲库铵的血浆药物浓度变化;观察并记录术前（T0）及T7时点血气变化,观察患者拔除气管导管的时间以及苏醒质量。结果：A组BIS值在术后T6、T7时点明显低于B组（P<0.05）,A组TOF值在T5、T6、T7时点明显低于B组（P<0.05）;A组与B组术后丙泊酚、顺式阿曲库铵的血药浓度组间比较差异无显著性（P>0.05）;A组在术后T7时点乳酸（Lac）值明显高于B组（P<0.05）;A组拔除气管导管的时间明显长于B组（P<0.05）;A组Aldrete评分明显低于B组（P<0.05）。结论：ANH后术毕自体血回输可增加血浆中麻醉药物浓度,加深患者的麻醉深度,增强肌松效应,使老年患者呼吸功能恢复和拔管时间延迟。     

Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the control amplitude as well as the integral of the control input over a given time interval are constrained, and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and an integral drug dosing constraint over a specified time period. Copyright © 2010 John Wiley & Sons, Ltd.     

基于MOMEDA与双谱分析的滚动轴承早期故障诊断

滚动轴承早期故障阶段,故障特征微弱且环境噪声干扰严重,采集数据包含大量噪声信息,传统的包络谱分析难以提取故障特征信息。双谱分析理论上可以抑制高斯噪声,但很难从强背景噪声下提取出微弱故障特征。而多点最优调整的最小熵解卷积（Multipoint Optimal Minimum Entropy Deconvolution Adjusted,MOMEDA）方法能增强信号中的冲击特征,但其效果和故障信号周期区间等参数有关。利用MOMEDA与双谱分析进行信号处理,将提取到的信号高阶谱特征作为滚动轴承早期故障分类依据。利用MOMEDA方法对采集信号进行滤波处理,提取出有冲击特征的时域信号;对特征增强的信号进行双谱分析,从高阶谱中提取故障特征。经过仿真信号分析和实际轴承故障信号验证,该方法能有效地提取出滚动轴承早期故障特征,实现故障诊断。     

A reduced MIMO Wiener model for recursive identification of the depth of anesthesia

This paper presents a modeling and identification strategy for the depth of anesthesia using the propofol and remifentanil rates as the system inputs, and the bispectral index and state entropy measurements as the systems outputs. The standard model used for this purpose has twenty two patient‐dependent parameters. This high number of parameters, the little input excitation, and the small amount of output data make classical system identification approaches unsuccessful. To overcome these issues, the new model presented in this paper has six parameters, thereby meeting the parsimony principle of system identification. An extended Kalman filter algorithm is also developed and applied to real data. The good fitting results, combined with noise suppression and a recursive update of the model parameters, are promising for the design of the depth of anesthesia controllers to be used in real time platforms. Copyright © 2013 John Wiley & Sons, Ltd.     

Bispectral analysis and reconstruction in the frequency domain of mono- and bidimensional deterministic sampled signals

Deterministic sampled signals bispectra are periodic and hold more information than analog signal bispectra. After showing this difference, the communication presents two algorithms for reconstructing a sampled signal Fourier transform from its bispectrum: the first is a least squares reconstruction method deducing the Fourier transform logarithm from the bispectrum logarithm through a simple average; the second is an algorithm for reconstructing the Fourier transform from a restricted number of values on the bispectrum diagonal slice by a simple resolution of linear equations. The resistance of both algorithms to the measurement noise is given.     

风电功率波动引发的强迫振荡扰动源定位方法

电力系统中的强迫功率振荡有可能引发大范围的功率波动。风电等新能源接入电网时,其输出功率的波动具有随机性,当该功率波动导致系统中发生强迫振荡时,振荡同样会呈现随机特征,难以对扰动源进行定位。文中提出一种对风电引发的强迫振荡扰动源定位的方法,该方法利用振荡的双谱获取中心频率,并通过小波变换得到振荡中心频率处的初始相位,据此可通过能量函数对扰动源进行定位。实际算例验证了所提方法的可行性及有效性。     

基于特征融合的通信辐射源个体识别

针对通信辐射源个体识别研究中单一特征不足以全面表示细微特征差异,从而限制识别率的问题,提出了一种基于特征融合的通信辐射源个体识别方法。该方法首先对原信号进行短时傅里叶变换和双谱变换,提取时频特征和双谱特征,结合小波融合技术进行特征融合,最后使用残差神经网络挖掘信号隐含的深层次特征,完成分类识别。实验结果表明,对于模拟信号源发射的短波通信信号,经过特征融合后的识别效果相较于使用单一特征方法有更高的识别准确率,并且在低信噪比的情况下仍有较好的识别效果。     

基于高阶统计量的小波变换去噪算法

图像在获取和传输的过程中经常要受到噪声的污染.传统的去噪方法不仅滤出了图像的噪声,同时使图像细节变得模糊.本文提出一种基于双谱和小波变换的去噪算法.该方法是根据高斯噪声及椒盐噪声在小波变换下的不同特征,并结合双谱滤波、中值滤波的特点,在小波城内对高频子带进行双谱滤波,去除图像中的高斯噪声,然后进行中值滤波,去除图像中的椒盐噪声.高斯噪声的双谱为零,能够彻底的去除高斯噪声.该算法的实验结果表明不仅能滤出图像中高斯噪声和椒盐噪声,而且能较好的保留图像的边缘细节.其滤波效果优于传统的图像去噪方法.