尿液中常见毒品微量检测的表面增强拉曼光谱识别

通过将自适应平滑滤波器和结合小波变换的支持向量机(Support vector machine,SVM)分类器有机组合,建立了低信噪比拉曼光谱的模式识别方法。首先,通过自适应平滑滤波器进行光谱去噪,滤波窗口宽度根据信噪比估计值进行调整,从而在保证特征峰信号强度的同时达到更好的噪声滤波效果;其次,由小波变换实现光谱数据降维,通过小波分解层数优化可以获得训练集的最佳分类准确率;最后,由SVM进行分类,通过交叉验证(Cross validation,CV)实现SVM参数寻优,并根据交叉验证与分类器之间的准确率关系,得出分类器可用参数需满足的条件。基于表面增强拉曼光谱技术,本方法实现了人体尿液中甲基苯丙胺(Methamphetamine,MAMP)和亚甲基二氧基甲基苯丙胺(3,4-Methylenedio-xymethamphetamine,MDMA)的定性微量分析。实验使用中国科学院合肥智能机械研究所研发的金纳米棒拉曼光谱增强基底,由Delta Nu公司的Inspector型便携拉曼光谱仪采集光谱,激发光波长785 nm,曝光时间为5 s,整体检测准确率高于95.0%。     

毛细管电泳-小波神经网络法辅佐诊断乳腺癌的研究

采用毛细管电泳法测定了46个健康人和26个乳腺癌病人尿样中的13种正常核苷和修饰核苷,以小波神经网络作为模式识别工具对健康人和乳腺癌病人的分类作了研究,随机选取的训练集的识别率达到100％,相应的预测集判别率正确性在96％以上,与经典的前向多层神经网络相比,小波神经网络具有更强的信息提取和逼近能力.研究结果还表明,小波神经网络的预测能力强于主成分分析和线性判别分析,毛细管电泳法与小波神经网络的结合有望成为乳腺癌的辅助诊断手段.     

人工神经网络在示波计时电位法中的应用

研究了误差反传神经网络和小波变换在示波计时电位测定中应用的可能性。对Ｐｂ２ ＋ 等无机离子测定的实验结果表明： 人工神经网络可较好地用于高次微分和经小波变换处理后的示波计时电位测定。 与经典的示波测定方法相比较, 该法基本上消除了人为误差的影响, 提高了分析速度和分析结果的可靠性。     

Simple and fast near-infrared spectroscopic analysis of hydroxyl number of polyol using a disposable glass vial

Hydroxyl (OH) number of polyol was measured using near-infrared (NIR) spectroscopy with the use of a disposable glass vial as a sample container. Polyols are viscous, so disposable vials are advantageous when spectroscopic methods are employed. Due to the curvature of the vial walls, a narrow aperture was used to minimize the spectroscopic deviations. The narrow aperture attenuated the NIR radiation and increased the spectral noise in the collected polyol spectra. Wavelet transformation (WT) was employed to reduce this noise and partial least squares (PLS) calibration model was developed. The overall prediction results compare well with those from conventional wet analysis that requires time (1–3 h) and large amounts of chemical reagents. NIR spectroscopy with the use of disposable vials can be utilized for a simple and fast quality assurance of polyol in actual industrial settings.     

微流控电泳芯片中化学发光信号的分段门限小波降噪

采用分段门限小波降噪(STWD)方法对化学信号中的异方差噪声进行降噪处理.用STWD法和统一门限小波降噪法同时处理两种模拟信号(其中之一包含异方差噪声).结果显示,优化参数的STWD法能够更有效地提高降噪效果.采用STWD法对微流控芯片化学发光检测信号中的异方差噪声进行处理,取得了满意的降噪效果.     

联用色谱数据的多尺度分析

引入基于正交小波基的多尺度分析，并将其用于色谱／光谱二维数据压缩和消除背景影响的研究，结果表明，该法不但成功地消除了复杂的色谱／光谱背景有同时大计压缩数据量，对模拟和实际体系的均获得满意的结果。     

共聚物红外光谱的小波变换

用傅里叶变换红外光谱研究了辐射合成的丙烯酰胺、丙烯酸钠均聚物水凝胶和几种丙烯酰胺／丙烯酸钠配比不同的共的水凝胶的组成变化，发现共聚物的特征吸收带重叠得比较严重，尤其当丙烯酸单元含量较代时，其特征吸收带大部分则不能分辨，将其红外光谱经波波变换后，这类共聚物的特征吸收带较好地得到分离，成功地提高了红外光谱图的分辨率，有助于更好地定性或定量表征共聚物的组成。     

Structural health monitoring and damage detection using an intelligent parameter varying (IPV) technique

Most structural health monitoring and damage detection strategies utilize dynamic response information to identify the existence, location, and magnitude of damage. Traditional model-based techniques seek to identify parametric changes in a linear dynamic model, while non-model-based techniques focus on changes in the temporal and frequency characteristics of the system response. Because restoring forces in base-excited structures can exhibit highly non-linear characteristics, non-linear model-based approaches may be better suited for reliable health monitoring and damage detection. This paper presents the application of a novel intelligent parameter varying (IPV) modeling and system identification technique, developed by the authors, to detect damage in base-excited structures. This IPV technique overcomes specific limitations of traditional model-based and non-model-based approaches, as demonstrated through comparative simulations with wavelet analysis methods. These simulations confirm the effectiveness of the IPV technique, and show that performance is not compromised by the introduction of realistic structural non-linearities and ground excitation characteristics.     

Wavelet packet based damage identification of beam structures

Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. The wavelet packet transform (WPT) is a mathematical tool that has a special advantage over the traditional Fourier transform in analyzing non-stationary signals. It adopts redundant basis functions and hence can provide an arbitrary time-frequency resolution. In this study, a damage detection index called wavelet packet energy rate index (WPERI), is proposed for the damage detection of beam structures. The measured dynamic signals are decomposed into the wavelet packet components and the wavelet energy rate index is computed to indicate the structural damage. The proposed damage identification method is firstly illustrated with a simulated simply supported beam and the identified damage is satisfactory with assumed damage. Afterward, the method is applied to the tested steel beams with three damage scenarios in the laboratory. Despite the noise is present for real measurement data, the identified damage pattern is comparable with the tests. Both simulated and experimental studies demonstrated that the WPT-based energy rate index is a good candidate index that is sensitive to structural local damage.