基于血流图和Fisher线性鉴别分析的红外人脸识别方法

为了从生物特征和统计角度来提高识别的性能,提出了一种基于血流图和Fisher线性鉴别(FLD)相结合的人脸识别方法,该方法首先利用血流模型把红外温谱图转换成血流图,能够利用人体的生物特征增加样本之间的类间距,并减少样本之间类内距,然后从统计特性出发,对血流图进行能最大化类     

Color Discrimination Characteristics Depending on the Background Color in the (<Emphasis Type="Italic">L</Emphasis>, <Emphasis Type="Italic">M</Emphasis>) Plane of a Cone Space

We investigated color discriminability on the background color. The measurement was carried out at over 21 background colors in (L, M) plane of a cone space by four observers. We used low temporal stimulus frequency (1 Hz) so that threshold was determined by the red-green opponent mechanism. Results showed that color discriminability depends on the background colors. Threshold was higher with a more saturated background color. This suggests that the sensitivity of the red-green opponent mechanism is high when the mechanism’s output is small and increases with the output levels of the mechanism. To confirm this relationship between the red-green mechanism and color discriminability, color appearance depending the background color was also evaluated. There was a strong correlation between the sensitivity and the perceived whiteness. Both sensitivity and whiteness value were highest at around the equal energy white point and decreased with increase in the difference between background color and equal energy white. This suggests that the adaptation state of the red-green opponent color controls color discrimination.     

本舰机动左右舷分辨方法研究

针对传统单根线列阵的左右舷分辩问题，经过深入分析，给出了本舰机动左右舷分辨方法中的一些影响因素，并对机动角度和滞后时间得出了定量结论。文中研究的内容对传统拖曳线列阵声纳的左右舷分辨和战术使用具有参考价值。     

星载激光多普勒测风雷达鉴频系统仿真(I):基于Fizeau干涉仪的Mie通道大气风速反演研究

研究了星载激光多普勒测风雷达系统结构,构建了基于Fizeau干涉仪的鉴频仿真系统,仿真研究了Mie通道风速反演算法,并利用无线电探空数据集仿真结果统计分析了Mie通道大气水平视线(HLOS)风速反演误差.仿真和统计结果表明,基于Fizeau干涉仪的Mie通道可反演低对流层大气风速;低对流层HLOS风速误差和标准差分别小于1 m·s-1和2 m·s-1;气溶胶和云的分布影响星载激光多普勒雷达测风误差,可使风速最大偏差增大一倍.     

Directional discrimination for fiber-optic non-mechanical scanning laser Doppler velocimeter using single transmission path

We propose a fiber-optic non-mechanical scanning laser Doppler velocimeter (LDV) with directional discrimination of the velocity component using a single transmission path. The LDV uses a polarization maintaining fiber (PMF) as the transmission path between the main body and probe. Two beams with different frequencies induced by acousto-optic modulators (AOMs) are transmitted to the probe as two orthogonal polarization modes of the PMF. The measurement position is axially scanned using gratings in the probe and the change in wavelength. The functions of the scan and directional discrimination are demonstrated experimentally.     

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusions: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.     

基于堆栈压缩自编码的近红外光谱药品鉴别方法

由于近红外光谱在药品鉴别应用中具有分析速度快、样品无损、可现场检测等突出优点，目前已在众多领域中广泛应用。但近红外光谱存在信噪比低，吸收强度弱且谱峰重叠等缺点，无法从光谱中直接得到定性/定量的物质信息，因而近红外光谱分析技术常作为一种间接分析技术，并且光谱的化学计量学建模方法成为近红外光谱分析的核心内容。深度学习是机器学习的一个新的分支，并已经成功运用于多个领域。深度学习的网络结构和非线性的激活能力，使其模型特别适合高维、非线性的大规模数据建模。为进一步丰富近红外光谱建模方法，并提高近红外光谱分析技术的回归精度或分类准确率，将深度学习方法应用于近红外光谱分析，发展新的建模方法十分必要。面向近红外光谱定性分析技术，提出一种基于堆栈压缩自编码网络(SCAE)光谱定性分析方法，并应用于多类别药品的光谱分析，以区分或鉴别不同厂家生产的同种药品。压缩自编码网络(CAE)以自编码网络(AE)为基础，进一步加入雅克比矩阵作为约束项。自编码网络最初是用实现数据降维，以学习数据内部特征，而雅克比矩阵包含数据在各个方向上的信息，将其作为AE的约束项则可使提取到的特征对输入数据在一定程度下的扰动具有不变性，从而提高AE提取特征的能力。SCAE是一种由多层CAE构成的神经网络。前一层CAE的隐藏层作为后一层CAE的输入层，网络的全部参数是通过采用逐层贪婪的训练方式来获取的，训练结束后将所有网络视为一个整体，通过反向传播算法进行微调，最后使用Logistic/Softmax分类器进行定性分析。实验数据均为中国食品药品检定研究院采集，以头孢克肟胶囊作为二分类实验数据，硝酸异山梨酯片作为多分类实验数据。通过Bruker Matrix光谱仪测定每个样本在不同波长下的吸光度值得到其光谱曲线，再通过OPUS软件消除漂移等因素对光谱样本之间产生的偏差。接下来通过实验确定约束项雅克比矩阵的系数λ为0.003之后建立模型。建模过程分为五个阶段，分别为： 预处理阶段，预训练阶段，微调阶段，测试阶段和对比阶段。为了验证SCAE在分类准确性、算法稳定性和建模时间等方面的性能，与BP神经网络、SVM算法、稀疏自编码(SAE)和降噪自编码(DAE)开展对比实验研究。分类准确性方面，在不同的训练集与测试集的比例下，SCAE均有最佳的分类准确性与算法稳定性。建模时间方面，由于SVM算法不需要预训练和特征提取，所以运行时间方面比其他算法有大的优势，但是SCAE建模速度优于除SVM之外的其他对比算法。综合而言，使用SCAE进行药品鉴别有效可行。     

荧光光谱法结合分子对接研究人血清白蛋白 对齐墩果酸与熊果酸的异构体识别作用

五环三萜化合物齐墩果酸(OA)与熊果酸(UA)为同分异构体,具有相似的理化性质和稍有差异的药理活性。目前人们主要采用各种色谱、质谱类方法实现对OA与UA的异构体识别,未见使用荧光光谱法的报道。提出了一种使用荧光猝灭法实现对OA与UA异构体识别的方法。首先考察了两种常见的血清蛋白-牛血清白蛋白(BSA)与人血清白蛋白(HSA)同OA与UA的作用情况,结果表明OA与UA均可有效地猝灭BSA与HSA的荧光发射。对所得荧光猝灭数据计算可知OA,UA与BSA,HSA作用的双分子猝灭速率常数(K_q)均远大于生物大分子荧光猝灭所观察到的最大散射碰撞速率常数2.0×1010 L·(mol·s)-1,说明猝灭类型均为静态猝灭,即OA与UA均是通过与BSA及HSA形成稳定复合物方式实现荧光猝灭的。应用双对数方程对所得荧光猝灭数据计算可知OA,UA与BSA,HSA所形成的复合物中结合位点数在0.90～1.26之间,说明所形成的复合物为1∶1型。BSA与OA,UA所形成复合物的表观结合常数(K_A)为同一数量级,相差不大,但是HSA与OA,UA所形成复合物的K_A差别很大,HSA-UA复合物的KA比HSA-OA复合物高124.91倍,表明HSA-UA复合物的稳定性更强。同步荧光实验结果显示,OA与UA的加入对于HSA波长差(Δλ)为60 nm同步荧光光谱的影响大于Δλ为15 nm的同步荧光光谱,由此可以说明OA与UA在HSA上的结合位点可能位于Trp残基附近。分子对接模拟计算结果表明OA与UA均对接在HSA结构中一个疏水性空腔中,主客体之间存在强烈的氢键与疏水作用。OA同Arg218,His242,Pro447等残基间存在氢键作用,键长分别为2.95,2.97与3.17 Å,此外还与Lys195,Lys199,Trp214,Arg222,Leu238,Asp451和Tyr452等七个氨基酸残基间存在疏水作用。UA同Trp214,Arg218和Lys444等残基存在氢键作用,键长分别为3.01,2.88与2.65 Å,此外还与Leu198,Gln221,Arg222,Asn295,Val343,Pro447,Cys448,Asp451和Val455等9个氨基酸残基间存在疏水作用。由于UA同HSA作用位点数目多于OA,说明UA与HSA疏水性空腔的空间匹配程度更高。因此,认为HSA-UA与HSA-OA复合物间稳定性差异是HSA实现对OA与UA异构体识别的原因。     

Study on fast luminescence component induced by gamma-rays in Ce doped LiCaAlF6 scintillators

We discuss the origin of the fast luminescence component induced by fast electrons generated in gamma-ray interactions in Ce doped LiCaAlF₆ scintillators. Although the slow luminescence component induced by Ce³⁺ emissions depends on the Ce concentration in the LiCaAlF₆ scintillator, the fast component is independent. The fast component is suggested to be generated in the host matrix of the LiCaAlF₆ crystal. From quantitative considerations based on Frank–Tamm equation, which shows the light yield of the Cherenkov radiation, the Cherenkov radiation was determined as the origin of the fast component. We, additionally, found that the slow rise time of main Ce³⁺ emissions in the Ce:LiCaAlF₆ scintillator plays an important role to perform the pulse shape discrimination.     

Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Abstract

During harvest and transport, defects are most likely to affect the interior of jujubes and thus shorten their storage period. This study applied visible and near-infrared transmission spectroscopy to detect such internal defects. Spectra were acquired on the equator area at 0, 90, 180, and 270 degrees of each sample, and a model was constructed to obtain three-dimensional damage and defect detection model. The first derivative, multiplicative scatter correction, standard normal variate, and median filtering were used for preprocessing. Modeling by mean spectra achieved a better effect than using unidirectional spectra. Then, naive Bayes classifier and support vector machine were employed for the model establishment at 600–950?nm and 680–950?nm bands, respectively, using mean spectra. Median filtering effectively improved the signal to noise ratio and the discrimination accuracy of the support vector machine model at 600–950?nm reached 96.77%, which was the best value among all models. This result indicates that the support vector machine model was the optimum model and 600–950?nm was a suitable data range for the detection of internal defects. This research confirms the feasibility of implementing visible and near-infrared spectroscopy for the detection of internal defects in jujubes.