提高综合滤波器鉴别率的一种有效方法

提出一种提高综合滤波器鉴别率的有效方法，其做法是：对样本集在等相关峰条件下求解综合滤波器的同时，使样本集的互补样本满足零件相关峰条件，此方法把训练模式类同所有其它模式类在映射空间的距离拉大，有效地提高了综合滤波器的鉴别率，以取自同一手指纹为样本集，求解综合鉴别函数后，建立了指纹的实时模式识别系统，实验结果表明本文提出的改进方法对提高综合滤波器的鉴别率是非常有效的。     

鉴别玉米杂交种纯度的近红外光谱分析技术研究

以不同产地和年份的农华101(NH101)玉米杂交种和母本种子为对象,研究了鉴别玉米杂交种子纯度的近红外光谱分析方法。 光谱采集时间跨度达10个月,运用傅里叶变换(FT)近红外光谱仪器,在不同季节用23天(分五个时间段)采集了这些样品共920条玉米单子粒近红外漫反射光谱。 全部原始光谱用移动窗口平均、 一阶差分导数和矢量归一化进行预处理,使用主成分分析(PCA)方法和线性判别分析(LDA)方法降维,采用仿生模式识别(BPR)方法建立模型。 通过对光谱预处理校正光谱失真,使样品光谱集在特征空间分布的范围收缩,相对距离增大了近70倍,实现了母本和杂交种子的鉴别。 通过代表性样品的选择,提高了模型对光谱采集时间、 地点、 环境等条件变动的应变能力,也提高了模型对样品种子制种时间与地点变动的应变能力,增强了模型的稳健性,使测试集玉米单子粒杂交种和母本种子的平均正确识别率达到95%以上,而平均正确拒识率也达到85%以上。     

Review of recent UV–Vis and infrared spectroscopy researches on wine detection and discrimination

Wine has become a commodity of significant commercial value, and the demand for high quality wine by consumers has been increasing. Suitable analytical techniques are needed for its quality control. Ultraviolet, Visible, Near-infrared and infrared spectroscopy is by far one of the most important techniques for determining the wine quality, including its components and characterization. This review will overview the available most recent applications of spectroscopic techniques in the past decade for wine quality prediction and discrimination both quantitatively and qualitatively. The fundamental principles of these techniques will be introduced briefly, and some innovative setups/instrumentations will also be illustrated. At last the limitations and prospects of spectroscopic techniques for wine industry will be discussed.     

Tissue-specific δ13C in ancient and modern tropical seabirds and flying fish in the Xisha Islands,South China Sea

We analyzed ¹³C characteristics in samples of bird bones, feathers, eggshell carbonate and membrane from modern specimens of red-footed booby (Sula sula) as well as fish muscle, scales, and bones from its predominant food source, flying fish (Exocoetus volitans), and muscle from its secondary food source squid (Loligo chinensis), as well as in ancient sub-fossil samples of seabird and flying fish at the Xisha Islands, South China Sea. δ¹³C is tissue-specific in both seabirds and flying fish due to the variance in turn-over among the tissues and differences in the type and content of amino acids across a diverse range of tissues. The δ¹³C discrimination factors also differed significantly among the various tissues between tropical seabirds and their prey. A Suess effect, caused by fossil fuel combustion and the emission of carbon with fewer ¹³C isotopes, was observed in the bird and fish tissue from ancient to modern time. Our study provides a multiple variability index for δ¹³C in organisms along a food chain, and verifies that tissue-specific ¹³C analysis is essential to identify diet and species and thus is a valuable tool for research on tropical seabird ecology.     

硅探测器的数字化脉冲形状甄别

介绍了利用硅探测器的脉冲形状甄别进行粒子鉴别的原理。详细叙述了基于数字化方法的脉冲形状甄别的实现。采样频率和位数是数字化方法的两个重要参数。对于硅探测器信号,采用100 MS/s,12 bit的Digitizer可以满足脉冲形状甄别法对时间分辨的要求。同时对该方法粒子鉴别的特征和能量阈值做了简要的分析和对比。粒子背面入射硅探测器的所得的阈值低于正面入射的情况。例如对于氖周围的同位素,背面入射情况的阈值约为100 MeV,为正面入射情况下鉴别阈值的二分之一,相当与ΔE-E方法中ΔE探测器厚度约为60 μm情况下的阈值。最后定性讨论了硅探测器的电阻率不均匀性和沟道效应对粒子鉴别性能的影响。In this paper Pulse Shape Discrimination(PSD) for silicon detector has been briefly introduced. The emerging digital method successfully applied to detector signal processing makes digital PSD method one of the most promising particle identification methods. Sampling frequency and the number of bits are two key parameters of digital method. For silicon detector signal, adopting 100 Ms/s, 12 bit Digitizer can satisfy the time resolution requirement of PSD method. The identification characteristic and energy threshold of this method have been discussed and compared with both front injection and rear injection cases. Energy threshold with rear injection usually is much lower than that with front injection. For example, around for Neon isotope energy threshold with rear injection is about 100 MeV which is only half of the threshold with front injection, also equivalent to thickness of about 60 μm silicon detector threshold in ΔE-E method. At the end the impact of silicon detector's resistivity nonuniformity and channel effect on the identification capacity of PSD method has been discussed in detail.     

Maximum confidence measurements via probabilistic quantum cloning

Probabilistic quantum cloning(PQC) cannot copy a set of linearly dependent quantum states.In this paper,we show that if incorrect copies are allowed to be produced,linearly dependent quantum states may also be cloned by the PQC.By exploiting this kind of PQC to clone a special set of three linearly dependent quantum states,we derive the upper bound of the maximum confidence measure of a set.An explicit transformation of the maximum confidence measure is presented.     

CsI(Tl)晶体中反冲Cs和I核Quenching?Factor的测量

许多实验对用CsI(Tl)闪烁晶体作为探测器来寻找和探测暗物质的可行性进行了研究.本工作利用8MeV单能中子轰击CsI(Tl)晶体探测器来研究Cs核和I核的QuenchingFactor.在数据处理中,运用脉冲形状甄别(PSD)方法来分辨反冲核信号和本底信号.实验结果表明,在7keV到132keV的能区中,Quench ingFactor随着反冲核能量的减少而增加.在探测暗物质的实验中,这一性质对于CsI(Tl)晶体探测器获得较低的能量阈值是很有利的.     

光电倍增管在磷酸液声致发光实验中的应用

对不同厚度液体进行光强测量,深入探索空化气泡的运动,研究发光机理很有意义。用超声激励法在磷酸液体中实现多泡声致发光,研究不同共振频率下发光的特点。利用光电倍增管多次测量发光强度相互比较,结果是在液体厚度10 cm、驱动频率f=21.061kHz和f=20.316kHz时,周期性较好为50μs,液体通过漩涡集中气泡可以使更多气泡发光;在液体厚度3 mm、驱动频率f=17.91kHz和f=19kHz时,周期性很好为25μs;且光信号都较强。结论是磷酸中声致发光强度、周期与液体厚度、驱动频率密切相关。本文以磷酸液多泡声致发光实验研究为基础,从内部和外部原理来出发,详细介绍了光电倍增管在多泡声致发光光强测量中的实用,为今后研究者提供了一些经验。根据实验过程中遇到的一些实际问题提出了建议和改善意见。     

基于1–10GHz空地链路信号的雨强监测方法可行性研究

空地链路上的微波信号受降雨影响, 会产生功率衰减和去极化效应. 基于这些物理特性, 本文提出利用1–10 GHz空地链路信号的降雨干扰项获取雨强的方法, 并开展了相关理论研究. 根据空地链路信号与雨滴复杂的相互作用, 研究了空地链路信号频率为1–10 GHz时, 雨强 (rain rate, R) 对衰减 (attenuation, A) 和交叉极化分辨率 (cross-polarization discrimination, XPD)的影响, 分别建立了A-R和XPD-R关系模型. 通过数值模拟, 分别分析了利用上述两个关系模型估测雨强的可行性, 并系统研究了不同频率、极化方式和仰角条件下的适用性. 研究结果表明, 对于水平极化或圆极化, 且频率较高的空地链路信号, 利用A-R关系反演强降雨具有理论上的可行性; 对于不同频率和极化方式的信号, XPD-R关系模型都可以用于反演雨强, 并且对于1–50 mm·h^-1范围内的雨强, XPD较为敏感; 不同仰角条件下, A-R和XPD-R 模型都适用. 在4–10 GHz时, 本文的XPD-R模型和国际电信联盟ITU-R中XPD预测模型的结果非常接近. 所得出的结论对于下一步开展相关的验证实验, 拓展卫星系统的气象应用, 实时估测降雨强度, 实现全球降雨观测具有重要的参考价值. 关键词： 空地链路信号 雨强 衰减特性 交叉极化分辨率(XPD)     

基于最小二乘支持向量机的橄榄油掺杂拉曼快速鉴别方法

提出了一种基于最小二乘支持向量机(LS-SVM)的橄榄油掺杂拉曼快速鉴别方法。首先,收集若干己知类别的橄榄油样作为训练样本,获取其拉曼谱图,并对其谱图进行预处理和波段选择,进而构建LSSVM分类器;对于未知类别的油样,获取其拉曼谱图,并进行相应的预处理和波段选择,由LSSVM分类器获得鉴别结果。实验以7种已知的特级初榨橄榄油为基础,分别掺入4种其它植物油(大豆油、菜籽油、玉米油、葵花籽油),获得112个掺杂油样。将全部样本随机分成训练集和测试集,对测试集样本的预测实验结果表明,本文方法能有效鉴别橄榄油掺杂,且掺杂量最低检测限为5%。与其它分类方法相比,LSSVM分类法具有最佳的分类性能。该方法快速、简便,为橄榄油掺杂鉴别提供了一种全新的方法。