关键词： 桑椹   指纹图谱   聚类分析   主成分分析  

摘要： 目的:建立桑椹HPLC指纹图谱。方法:采用Agilent 5TC-C18(250 mm×4.6 mm,5μm)色谱柱;以乙腈-0.1%磷酸溶液为流动相,梯度洗脱;流速为1.0 mL/min;柱温为25℃;检测波长为330 nm。建立桑椹指纹图谱,并进行相似度评价、聚类分析和主成分分析。结果:桑椹HPLC指纹图谱共确定11个共有峰,并指认其中绿原酸、芦丁、金丝桃苷、槲皮素4个成分。21批不同产地桑椹样品相似度均在0.910以上,质量均一性良好;聚类分析和主成分均将样品分为4类。结论:该方法重复性好,可操作性强,可为桑椹质量评价提供科学依据。

关键词： 人工智能   档案   深度学习   模式识别   自然语言处理  

摘要： 随着深度学习技术的迅速发展,人工智能逐渐进入行业应用阶段,也为档案工作的智能化发展提供了契机,尤其是文本处理、信息抽取、模式识别、专家系统等技术在档案工作中体现出广阔的应用前景。文章以北京市市场监督管理局为案例,分析了其引入人工智能技术进行档案智能挑选和智能鉴定划控等工作取得的成果,并研究探索了人工智能技术在档案审核和音视频档案管理中的应用思路,最后对人工智能在档案领域中的挑战和对策进行了思考。

关键词： Pattern recognition   Acoustic emission   Glass fiber reinforced polymer ( GFRP) laminate coupon   Failure mode identification   k-means clustering   Multilayer perceptron   Support vector machine  

摘要： Acoustic emission (AE) monitoring has been identified as one popular technique applicable for damage discrimination in composites. However, because the damage is not directly identified from the AE data, other nondestructive techniques are usually needed to further evaluate the damage. Moreover, there is no general guideline for AE monitoring in composites because of the unique AE signatures of each defect and the diverse properties due to varied material configurations. Therefore, this study aimed to develop a reliable method using advanced pattern-recognition techniques on AE data to identify damage modes exhibited in the glass fiber reinforced polymer (GFRP) coupons and composite systems with similar configurations. Three sets of GFRP laminate samples originated from a new fiber-reinforced polymer (FRP)-balsa wood composite bridge were tested to study critical failure modes such as fiber breakage, matrix cracking, delamination, and debonding. A methodology based on unsupervised pattern recognition was then developed. First, in order to eliminate uncorrelated AE features, the complete link hierarchical clustering algorithm and principal component analysis were used to preprocess the data and select a suitable subset of AE features for the clustering task. The unsupervised k-means clustering analysis was then applied to separate the date into several clusters and correlate each cluster to its corresponding failure mechanisms. After a reliable AE database was built for a typical sample of each test set, neural networks (NNs) based on multilayer perceptron (MLP) and support vector machine (SVM) algorithms were developed. Finally, the trained NNs were used for pattern recognition in samples with unknown damage modes. Most results conformed to the visual observation and thus led to the NN models with good performance. The developed network system was also applied on a full-scale GFRP bridge deck with a similar configuration, and the performance achieved further confirm

关键词： 企业裂变   人工智能   机器翻译   70年代   定理证明   专家系统   模式识别   智能控制  

摘要： 从最近五六年的发展来看,人工智能必定会从原来的形态走向一个新的形态,我们称之为人工智能2.0。5G+人工智能2.0会给我们带来什么?人工智能是63年前在美国诞生的,当时对人工智能的定义是“让机器能像人那样认知、思考和学习,即用计算机模拟人的智能”.上世纪70年代之后,人工智能固定为七个重要研究方向,机器定理证明、机器翻译、专家系统、博弈、模式识别、学习、机器人和智能控制,今天人工智能所取得的突破实际上是从63年以前的研究开始的。

关键词： 心音信号   深度学习   模式识别   信号分析   实验平台   进程择优算法  

摘要： 为了将深度学习和心音研究相结合以提高心音识别算法处理数据的能力,设计了一种深度学习网络的模块化实验平台。信号采集模块、数据处理模块和深度学习网络模块共同组成实验平台系统,使用实验室制作的传感器进行心音信号的采集和去噪声处理,随后由数据处理模块完成信号的特征提取并建立信号的特征数据库,心音信号的识别过程与数据处理在深度学习网络模块中实现。心音智能分析平台以心音深度学习网络为核心,采用进程择优算法,分类器选用BP神经网络,可对心音信号进行分类处理,实现了基于深度学习网络的心音信号识别与数据处理。与此同时,该平台的实现对心音与深度学习的结合、提高心音识别算法在自然环境下处理大数据的能力,具有积极的意义。

关键词： 建筑工程   测绘   激光扫描技术  

摘要： 建筑工程中的测绘工作较为复杂,技术难度较高属于专业性非常强的工作,传统的测绘方法在实际工作中人力成本和时间成本均较高,基于激光扫描技术的全新建筑测绘技术可以有效解决传统测量方法复杂、误差较大、时间及人力成本高等弊端。近年来,这种基于激光扫描技术的测绘方法已经在建筑测绘以及工程测绘领域得到了广泛的应用及认可。同时激光扫描测绘技术可以有效减少人工测绘对建筑的损耗与破坏,在古建筑改造工程中有不可替代的作用,文章对激光测绘技术的技术原理以及应用方法进行了研究及探讨。

关键词： 芪参益气滴丸   指纹图谱   化学模式识别   质量控制  

摘要： 目的采用超高效液相色谱法建立芪参益气滴丸的指纹图谱,并结合化学模式识别技术对其进行系统、全面和科学的质量评价。方法采用Waters超高效液相色谱仪,ACQUITY UPLC?HSS T3色谱柱,以乙腈-0.1%甲酸水为流动相进行梯度洗脱,检测波长为256nm,建立10批次芪参益气滴丸的指纹图谱。通过相似度分析并结合聚类分析(cluster analysis,CA)、主成分分析(principal component analysis,PCA)及正交偏最小二乘法-判别分析(orthogonal partial least squares discriminantanalysis,OPLS-DA)模式识别技术对芪参益气滴丸的总体质量进行分析评价。结果建立的指纹图谱共标定53个共有峰,经对照品进行化学指认并鉴定了其中的14个色谱峰。10批供试品的相似度均>0.94,表明该药物总体质量较为稳定;但通过CA及PCA均发现不同批次药物质量之间仍然存在有微小差异,且主要分为2大类,最后进一步采用OPLS-DA筛选出了导致批次药物质量差异的3个共有峰。结论本研究建立的分析方法科学、准确、可靠且简便易行,指纹图谱结合化学模式识别技术可更加系统、全面地评价芪参益气滴丸的药物质量,同时为今后中药及其制剂更进一步的质量控制研究奠定了坚实的理论基础。

关键词： 矩特征   PNN   局部放电   模式识别   三维谱图  

摘要： 为了提高局部放电的识别率与速度,本文将矩特征与概率神经网络相结合应用于局部放电模式识别,采集了开关柜四种缺陷放电类型.通过每种放电类型的三维谱图谱图获取矩特征.将得到的矩特征输入概率神经网络(PNN)进行分类识别.并分别与统计特征输入PNN、矩特征输入BP、HMM(隐马尔科夫模型)、贝叶斯分类器进行了比较.实验结果表明,基于矩特征与概率神经网路相结合对开关柜缺陷类型的分类效果令人满意.

关键词： ADMOW   优化加权   PSO   异常值   滚动轴承   模式识别  

摘要： 针对滚动轴承振动信号的状态特征及特征数据中存在'异常值'的现象,提出了基于优化加权代理判别模型(Agent discriminate model based optimization weighted,ADMOW)的模式识别方法。该方法首先通过计算样本特征值的类相似度对特征值进行评价,并依据评价结果对特征值赋予权值,以此弱化'异常值'导致模型出现偏差的问题;然后利用粒子群优化(Particle swarm optimization,PSO)算法对所建立的模型参数进行优化,得到更加准确可靠的分类模型;最后采用建立的优化加权代理预测模型对待测样本进行识别。滚动轴承实验结果表明,与以往的模式识别方法相比,该方法能有效地提高识别准确率。

关键词： Glass-fiber-reinforced plastics   compression after impact   acoustic emission   failure mechanism   pattern recognition   wavelet analysis  

摘要： In this article, to overcome the challenges encountered during the discrimination of various failure modes in post impacted/indented glass-fiber-reinforced plastic, techniques like pattern recognition method and advanced signal processing were employed. The significant acoustic emission parameters such as amplitude, rise time, counts, energy, duration, and peak frequency that are acquired during compression after impact test are considered as inputs to cluster validity index and for various clustering techniques such as k-means, fuzzy C-means, and Kohonen's self-organizing map. The acoustic emission count-frequency and amplitude-frequency have no overlapping, whereas other combinations of acoustic emission parameters result in overlapping with four clusters. The clustering techniques are validated by discrete wavelet transform of acoustic emission signals. The discrete wavelet transform was performed on the clustered acoustic emission signals to identify the percentage of energy and frequency content of each level which correlates the different failure modes. The results infer that k-means, fuzzy C-means clustering, and Kohonen's self-organizing map are 94.5%, 97.1%, and 98.6% reliability, respectively, clearly suggesting Kohonen's self-organizing map as the most appropriate technique for the classification of acoustic emission signature.