基于灰度曲面匹配的虹膜识别方法

目前的虹膜识别都是采用在图像上提取特征点,并将特征点编码为固定长度的特征数据用于匹配的方式。这种方式使虹膜识别系统易受攻击。为了进一步提高虹膜识别系统的安全性和识别速度,提出了一种基于灰度曲面匹配的虹膜识别方法。该方法抛弃了特征提取和编码等传统操作,在特征分析的基础上直接利用了灰度曲面匹配的思想,首先对两幅图像中的像素灰度做差,得到灰度差曲面,然后求出该灰度差曲面的方差。将此方差作为衡量两个虹膜特征曲面之间距离的依据,并据此判定两个虹膜是否来自同一只眼睛。在给定阈值为40的前提下,正确识别率为96.89%,识别时间为53.2 ms。实验结果证明,该方法识别准确率高,识别速度快。     

基于改进云推理算法的塔架结构损伤识别

为了解决塔架结构的损伤识别问题,提出了基于应变能和改进云推理算法的损伤识别方法。首先描述了云模型的基本理论和数字特征,并给出了模态应变能的基本公式;然后分析了X条件云发生器和Y条件云发生器的基本算法和运行步骤,借助灰云模型建立相应的前件云和后件云规则,考虑了测量噪声的影响,利用云发生器生成多组云滴,并利用多模式下云滴的确定度和生成值构建了基本云推理算法及其损伤识别指标。基本云推理算法中常会产生不均匀发散的云滴,从而使计算结果产生一定的偏差,为了降低云滴发散产生的偏差影响,提出了基于损伤模式数量加权的云推理改进策略。计算结果表明:云推理算法可以较好地应用于塔架结构的损伤识别,其识别结果明显优于传统的应变能耗散率指标方法;而改进云推理算法进一步提高了识别的精度,优于基本云推理算法。     

基于L1正则化的随机梁式结构静力损伤识别方法

提出了一种结合摄动法和L1正则化方法的随机梁式结构静力损伤识别方法。考虑初始模型误差和测量误差的影响,建立了关于随机损伤指数的控制方程,并将摄动法和L1正则化方法相结合,对随机损伤指数的控制方程进行求解,进而从概率的角度对结构的损伤进行识别。损伤试验结果表明,和传统的最小二乘求解法相比,本文方法能够更为准确地识别多处局部损伤的位置及大小,对实际结构损伤检测具有较好的参考价值。     

Experimental Investigation of the Effect of Purge Flow and Main Flow Interaction in a Low-Speed Turbine Cascade Passage

In order to protect the vulnerable turbine components from extreme high temperature, coolant flow is introduced from the compressor to the disk cavity, inevitably interacting with the main flow. This paper describes an experimental investigation of the interaction between the main flow and the purge flow in a low-speed turbine cascade with three purge flow rates, Cm = 0, Cm = 1%, and Cm = 2%. In order to study the effect of the interaction between the main flow and the purge flow on the secondary flows, a Rortex method developed by Liu Chaoquan is introduced to identify the vortex in the flow field. In the meantime, a method to calculate the mean entropy production rate based on the particle image velocimetry (PIV) result is adopted to investigate the flow loss. The PIV result indicates that the purge flow has a prominent impact on the flow field of the cascade passage, changing the velocity distribution that induces a local blockage area. The results of vortex identification show that the purge flow promotes the generation of the passage vortex near the suction side. In addition, the purge flow makes the passage vortex migrate to the tip wall direction, enlarging the region affected by the secondary flow. The mean entropy production (MEP) result shows that the flow loss is mainly caused by the passage vortex. The coincidence of the high-MEP region and the location of the passage vortex indicates that the purge flow increases the secondary flow loss by affecting the formation and the migration of the passage vortex.     

On Regularization of a Source Identification Problem in a Parabolic PDE and its Finite Dimensional Analysis

We consider the inverse problem of identifying a general source term, which is a function of both time variable and the spatial variable, in a parabolic PDE from the knowledge of boundary measurements of the solution on some portion of the lateral boundary. We transform this inverse problem into a problem of solving a compact linear operator equation. For the regularization of the operator equation with noisy data, we employ the standard Tikhonov regularization, and its finite dimensional realization is done using a discretization procedure involving the space $L^2(0,\tau;L^2(Ω))$. For illustrating the specification of an a priori source condition, we have explicitly obtained the range space of the adjoint of the operator involved in the operator equation.     

自动化质谱图解卷积和鉴定系统用于鉴定含痕量杂原子的石油馏分（英文）

Here we present a simple yet effective gas chromatography-mass spectrometry (GC-MS) identification approach for the detection of heteroatom-containing compounds (HACCs) in petroleum fractions. The MS/AMDIS (Automated Mass Spectral Deconvolution and Identification System) program was used to identify parts per million (ppm) HACC concentrations in petroleum fractions in place of traditional techniques (extraction and standard injection). Polycyclic aromatic sulfur heterocycles (S-PAHs) were used as model compounds to confirm the validity of the AMDIS identifiers, which were compared with extracted results using the off-line X-calibur software. AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate. There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate. AMDIS was also used to detect nitrogen-containing compounds (NCCs) and alkylphenols in oil condensate. Our results confirmed the presence of 2-methylbenzothiazole, carbazole, and 2,4-ditertbutyl phenol. In a crude oil sample, AMDIS identification of m/z=191 biomarkers was consistent with empirical results. Therefore, AMDIS can help to reduce the number of experimental steps in identification protocols.     

基于线上线下结合的无机定性分析实验教学

针对在实验室开设定性分析实验受到的制约,设计并开设了"无机化合物的性质与鉴定"虚拟实验,以作为常规无机定性分析实验的重要补充和扩展。虚拟实验和传统实验一并开设,构成线上线下结合的教学模式,完善知识体系,全面培养学生的无机定性分析能力。     

新型除草剂丙酯草醚A环14C均标记合成和鉴定

以U-¹⁴C-对氨基苯甲酸为前体, 通过酯化、缩合、还原和取代四步反应获得了A环¹⁴C均标记的丙酯草醚, 用PHPLC对其进行纯化. 采用HPLC-MS(ESI), MS(EI)和¹H NMR验证了其结构, 通过HPLC(外标法)确定其化学纯度大于98%; HPLC-LSC和TLC-IIA两种方法分析表明, 其放射化学纯度大于98%, 其比活度为1.089±0.011 mCi/mmol. 合成的化学收率和放化收率均为53%.     

原油饱和烃指纹的内标法分析

采用内标法建立了原油中正构烷烃、生物标志物(甾、萜烷类)的分析方法。确定了样品前处理方法和组分定性定量方法。讨论了柱层析分离能力。结果表明,所有饱和烃组分均流入第一部分流出液F1中,分离能力较为满意。对48种甾、萜烷类生物标志物组分以及姥鲛烷、植烷和正构烷烃等进行了定性确认,定性化合物数量多,信息量大,易于据此进行可靠的油指纹分析鉴别。32个正构烷烃组分相对标准差为1.2%～7.4%;45个生物标志物组分相对标准偏差为2.5%～9.2%。分析精密度较好,满足油指纹鉴别需要。正构烷烃回收率为73%～116%;生物标志物为84%～106%;回收率结果较为满意。正构烷烃方法检出限为7.0μg/g;生物标志物为0.65μg/g,满足原油样品分析要求。通过本方法对自不同和相同平台的原油样品进行饱和烃浓度分析,并采用浓度数据进行鉴别,结果与实际情况相符。     

钳蝎毒中抗癫痫肽、镇痛肽和抗肿瘤肽的快速同时分离和鉴定

采用Shim-Pack WCX-1型阳离子交换高压色谱柱对中国东亚钳蝎全蝎毒进行了分离，在鉴定了其中的抗癫痫肽、镇痛肽和抗肿瘤肽活性峰的基础上，应用Shim-Pack DIOL-300型凝胶排阻高压色谱柱对它们进行了进一步分离和鉴定，可以得到较纯的3种多肽。在高压色谱所提供的全蝎毒分离信息的基础上，应用与Shim-Pack WCX-1色谱柱具有相同交换基团的、具有较大吸附容量的CM Sepharose CL-6B软胶介质在低压色谱上对全蝎毒进行了分离，并分别对其中的抗癫痫肽、镇痛肽和抗肿瘤肽进行了鉴定。