关键词： 细辛   HPLC   指纹图谱   聚类分析   主成分分析  

摘要： [目的]通过建立中药细辛水提物高效液相色谱(HPLC)指纹图谱,评价不同产地细辛药材水提物的质量差异。[方法]采用ZORBAX Eclipse Plus C18柱(4.6 mm×250 mm,5μm);以0.1%甲酸水溶液(A)-乙腈(B)为流动相进行梯度洗脱洗;流速为1 mL/min;柱温:30℃;进样量:5μL;检测波长为285 nm。建立10批细辛药材水提物的指纹图谱,通过相似度分析并结合聚类分析(HCA)、偏最小二乘判别分析(PLS-DA)、正交偏最小二乘法-判别分析(OPLS-DA)及主成分分析(PCA)对细辛药材水提物进行质量评价。[结果]建立的细辛药材水提物指纹图谱共标定了20个共有峰,通过与对照品比对,指认出6个化学成分,10批样品的相似度均在0.908以上。HCA、PLS-DA、OPLS-DA及PCA结果表明10批细辛药材水提物之间存在一定的质量差异,这些结果可用于对不同产地的细辛药材水提物进行质量评价。[结论]HPLC指纹图谱结合化学模式识别技术可系统、全面地评价细辛药材水提物的质量。对中药材细辛水提物进行研究可进一步完善对细辛药材的质量评价。

关键词： 八珍益母丸   超高效液相色谱   指纹图谱   化学模式识别  

摘要： 目的:建立八珍益母丸(水蜜丸)UPLC指纹图谱,并结合化学模式识别方法对其进行质量评价。方法:采用Agilent ZORBAX RRHD Eclipse Plus C_(18)(2.1 mm×50 mm,1.8μm)色谱柱,以0.5%磷酸(A)-乙腈:甲醇65∶35(B)为流动相进行梯度洗脱,流速为0.3 ml·min^(-1),检测波长为220 nm,柱温为35℃,进样量为5μl,建立八珍益母丸UPLC指纹图谱,并进行化学模式识别。结果:建立的八珍益母丸(水蜜丸)UPLC指纹图谱确定17个共有峰,指认了芍药苷、甘草苷、补骨脂素、甘草酸铵、欧前胡素5个成分。12批八珍益母丸样品与对照图谱的相似度均>0.9,聚类分析、主成分分析、正交偏最小二乘判别分析其结果大致相同。结论:该研究建立的UPLC指纹图谱方法简单、可靠、重复性好、专属性强,可为八珍益母丸的质量评价及进一步研究提供参考。

关键词： Computer vision   image representation   formal languages   syntacticmethods   pattern recognition  

摘要： Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

关键词： underwater acoustic communication   modulation mode recognition   feature extraction   random forest  

摘要： The modulation mode recognition of non-cooperative underwater acoustic (UWA) communication signal faces great challenges due to the influence of the UWA channel and the demand for efficient recognition. This work proposes a recognition method for UWA orthogonal frequency division multiplexing (OFDM), binary frequency shift keying (2FSK), four-frequency shift keying (4FSK), and eight-frequency shift keying (8FSK) by using spectral peak feature extraction combined with random forest (RF). First, a new spectral peak feature extraction method is proposed. In this method, pre-processing, waveform optimization, and feature extraction are used to ensure that the extracted feature maintains high robustness in the UWA channel. Then, we designed an RF classifier that can meet the demand for high-efficiency recognition and good performance. Finally, simulation and experimental results verified the feasibility of the recognition method.

关键词： LiDAR   looting   visualization techniques   pattern recognition   geomorphon   San Giovenale   Etruscan archaeology   Italy  

摘要： Illegal archaeological excavations, generally denoted as looting, is one of the most important damage factors to cultural heritage, as it upsets the human occupation stratigraphy of sites of archaeological interest. Looting identification and monitoring are not an easy task. A consolidated instrument used for the detection of archaeological features in general, and more specifically for the study of looting is remote sensing. Nevertheless, passive optical remote sensing is quite ineffective in dense vegetated areas. For these type of areas, in recent decades, LiDAR data and its derivatives have become an essential tool as they provide fundamental information that can be critical not only for the identification of unknown archaeological remains, but also for monitoring issues. Actually, LiDAR can suitably reveal grave robber devastation, even if, surprisingly, up today LiDAR has been generally unused for the identification of looting phenomenon. Consequently, this paper deals with an approach devised ad hoc for LiDAR data to detect looting. With this aim, some spatial visualization techniques and the geomorphon automatic landform extraction were exploited to enhance and extract features linked to the grave robber devastation. For this paper, the Etruscan site of San Giovenale (Northern Lazio, Italy) was selected as a test area as it is densely vegetated and was deeply plundered throughout the 20th century. Exploiting the LiDAR penetration capability, the prediction ability of the devised approach is highly satisfactory with a high rate of success, varying from 85-95%.

关键词： 模式识别  

ISBN: (纸本)9787302591856

摘要： 本书共分为11章, 每章阐述模式识别中的一个知识点, 内容包括贝叶斯决策、概率密度函数的估计, 线性判别分析、非线性判别分析、组合分类器、无监督模式识别、特征选择、特征提取、半监督学习以及人工神经网络, 除了经典算法以外, 本书增加了部分较新的理论和算法, 读者可以选择性地学习。本书还配以电子课件、MATLAB仿真程序和实验指导书, 便于教和学。

摘要： The morphological classification of radio sources is important to gain a full under standing of galaxy evolution processes and their relation with local environmentalproperties. Furthermore, the complex nature of the problem, its appeal for citi zen scientists and the large data rates generated by existing and upcoming radiotelescopes combine to make the morphological classification of radio sources anideal test case for the application of machine learning techniques. One approachthat has shown great promise recently is Convolutional Neural Networks (CNNs).Literature, however, lacks two major things when it comes to CNNs and radiogalaxy morphological classification. Firstly, a proper analysis to identify whetheroverfitting occurs when training CNNs to perform radio galaxy morphological clas sification is needed. Secondly, a comparative study regarding the practical appli cability of the CNN architectures in literature is required. Both of these short comings are addressed in this thesis. Multiple performance metrics are used forthe latter comparative study, such as inference time, model complexity, compu tational complexity and mean per class accuracy. A ranking system based uponrecognition and computational performance is proposed. MCRGNet, ATLAS andConvXpress (novel classifier) are the architectures that best balance computationalrequirements with recognition performance.

关键词： Structural damage detection   Discrete wavelet transform   Principal component analysis   Deep neural networks   Couple sparse coding   Ensemble classifiers  

摘要： Damage detection is of great importance in reducing maintenance cost and preventing collapse of structures. Despite existing damage detection methods, the current literature lacks a comprehensive method, which: (i) is applicable to complex structures with large degrees of freedom, (ii) captures even low-level damages, and (iii) gives reasonable accuracy in the presence of uncertainty conditions such as noise and temperature. Hence, this study proposes a damage detection algorithm based on discrete wavelet transform and an ensemble of pattern recognition models, in which: (1) vibration data is decomposed through discrete wavelet transforms, (2) the decomposed data is compressed using principal component analysis, (3) individual damage models of the structure are trained through pattern recognition models of deep neural network and couple sparse coding, where the compressed decomposed vibration data as well as damage data are inputted, and (4) ultimately, the individual damage models are merged into one by majority voting to predict damage location and severity of the structure. The proposed algorithm is tested on a numerical model of a one-bay three-story steel frame, and experimental data of a large-scale bridge structure. It is found that the algorithm can precisely detect low-level damages at multiple locations, even in beam-column connections and complex structures, in the presence of uncertainty conditions such as noise and temperature.

标准号: GB/T 30579-2022

关键词： 模式识别   图像处理   局部卷积网络   地基云图  

摘要： 为修复全天空成像仪拍摄的地基云图中的大面积遮挡区域,提出了一种基于局部卷积神经网络的地基云图修复方法。通过在传统卷积神经网络的基础上增加网络结果整体跨层传输结构,连接对需修复图像的编码和解码两部分,实现了以含遮挡图像的输入到修复完图像的输出全过程。采用美国国家新能源实验室网上公开数据,从定性和定量的角度分析不同天气情况下所提出修复方法的性能,实验结果表明,基于局部卷积网络的地基云图修复方法可以较为真实地还原天空情况,取得优于现有方法的修复效果,为光伏电站输出功率预测提供了重要的数据。