基于PLC控制的包装码垛生产线的设计

我国的聚丙烯(PP)产量占世界产量的10%以上,而长期以来国内聚丙烯产品的包装却一直处于人工套袋的半自动包装状态。针对PP树脂的特点,专门设计了全自动包装码跺生产线工艺流程,介绍了以PLC为基础的全自动包装码跺生产线控制系统,重点说明了控制系统的硬件配置、输入输出分配和软件设计。全自动包装码垛线改变了国内PP产品包装的落后局面,值得在PP包装领域推广。     

基于集成修剪的丁苯橡胶聚合转化率软测量

针对目前丁苯橡胶聚合转化率难以在线精确测量,不利于指导生产的问题,本文提出一种基于集成修剪的软测量方法用于丁苯橡胶聚合转化率的预测.首先采用bagging方法建立多个LS-SVM弱学习器,然后利用AdaBoost.RT方法对弱学习器进行修剪,最后将修剪出的弱学习器加权输出.该方法克服了集成算法需要存储空间大和预测时间长的缺点,并且在一定程度上改善了最小二乘支持向量机的稀疏性和鲁棒性问题.仿真结果表明,聚合转化率预报绝对误差大于1.5样本的比例小于10%,能够满足实际生产要求,可以作为过程信息用于丁苯橡胶聚合过程的优化控制.     

全自动硫磺灌包机防堵转系统设计

本文针对全自动硫磺灌包机在灌包过程中,破碎机系统易发生堵转,造成全自动硫磺灌包机停机甚至破碎电机过热烧毁的情况,提出了一个防堵转的控制系统设计。该设计通过行程开关判断破碎系统是否堵转,简单、可靠,对生产设备设计和改造具有一定的意义。     

Global and Local (Glocal) Bagging Approach for Classifying Noisy Dataset

Learning from noisy data is a challenging task for data mining research. In this paper, we argue that for noisy data both global bagging strategy and local bagging strategy su er from their own inherent disadvantages and thus cannot form accurate prediction models. Consequently, we present a Global and Local Bagging (called Glocal Bagging:GB) approach to tackle this problem. GB assigns weight values to the base classi ers under the consideration that: (1) for each test instance I_x, GB prefers bags close to I_x, which is the nature of the local learning strategy; (2) for base classi ers, GB assigns larger weight values to the ones with higher accuracy on the out-of-bag, which is the nature of the global learning strategy. Combining (1) and (2), GB assign large weight values to the classi ers which are close to the current test instance I_x and have high out-of-bag accuracy. The diversity/accuracy analysis on synthetic datasets shows that GB improves the classi er ensemble's performance by increasing its base classi er's accuracy. Moreover, the bias/variance analysis also shows that GB's accuracy improvement mainly comes from the reduction of the bias error. Experiment results on 25 UCI benchmark datasets show that when the datasets are noisy, GB is superior to other former proposed bagging methods such as the classical bagging, bragging, nice bagging, trimmed bagging and lazy bagging.     

环状物新检测方法及在敏感图像识别中的应用

为了克服传统的Hough变换类环状物体检测的局限,提出了1种结合物体形状与外观特征的环状物体识别检测算法.识别算法使用cascade结构,分别使用灰度、纹理以及外观综合特征,按Bagging的方法训练产生一组弱分类器.这些弱分类器串接而成,并结合局部物体分割,逐个处理当前扫描窗口.相比于传统的Hough算法,新方法具有更快的检测速度.选择敏感图像作为实验对象,采集数据进行训练和检测,实验结果表明,新方法具有明显的性能优势.使用更全面的物体外观信息,按Bagging产生弱分类器的组合,能够在提高环状物体的检测性能的同时,获得理想的处理速度.     

15 kt/a聚丙烯包装系统设计总结

详细介绍南京炼油厂新建５ｋｔ／ａ聚丙烯装置包装系统的设计、施工、安装和试车毛病情况,对今后各三改人工包装为自动包装,可作为一个较好的借鉴。     

‘澳洲青苹’果实解袋后果皮花青苷合成的变化

以‘澳洲青苹’为材料,测定套袋果实(对照组)与解袋后(处理组)果实果皮色泽参数、花青苷含量、多酚类物质含量以及与花青苷合成相关的调控基因(Md MYB1、Mdb HLH3、Mdb HLH33、Md TTG1)和结构基因(Md CHS、Md DFR、Md ANS、Md UFGT)表达水平的变化。结果显示:光是影响果实着色的重要因素,套袋遮光抑制了果实着色,套袋果实解袋后果皮L*、b*值逐渐降低,a*值逐渐升高,果实红色加深;套袋抑制了果皮花青苷和黄酮醇合成,套袋果实解袋后果皮中矢车菊素-3-O-半乳糖苷、槲皮素-3-半乳糖苷和槲皮素-3-芦丁糖苷的含量逐渐升高,而套袋对其他的酚类物质影响不显著;套袋抑制了与花青苷合成相关基因的表达,套袋果实解袋曝光后,果皮中与花青苷相关调控基因和结构基因的表达水平均存在不同程度的升高,并且与果皮中花青苷含量变化的趋势基本一致,表明‘澳洲青苹’果实解袋后,果皮中与花青苷合成相关的基因迅速表达,从而促进了果皮中花青苷合成,而套袋抑制了果皮中与花青苷合成的相关基因的表达,从而抑制了花青苷合成。     

基于激光测量的机织服装起拱性客观评价

为研究客观、精确的服装穿着起拱性测试与评价方法,将20种机织物制作成裤筒进行穿着起拱实验。拍照保存织物的起拱形态,并运用激光扫描仪获取起拱部位的三维点云数据。对获取的数据进行预处理及特征提取,得到三维指标:起拱高度、起拱体积、经纬向最大起拱率;并提取起拱图像的二维灰度共生矩阵参数用于对比分析。经过对12块机织物的验证表明:起拱体积与主观评价等级的相关系数最高;本文提出的经纬向最大起拱率与起拱等级的相关性较好,其差值可用于判断起拱形状;利用激光扫描得到的三维指标预测起拱等级的准确率高于二维指标,且可克服机织物花纹图案、组织结构等对其的影响。     

Random Sampling for Subspace Face Recognition

Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.