贴标检测自动化技术应用研究

为解决生产用工成本高、人工贴标一致性差和效率低下、贴标和激光雕刻质量检测效率低等问题,设计了一套集自动贴标和智能检测的控制系统,提高贴标的效率和准确性。使用PLC (Programmable Logic Controller)对分瓶、贴标、刷标、剔除等动作进行控制,加入OCR (Optical Character Recognition)文字识别、标签粘贴偏移识别、RFID读取和标签外观检测等功能。有效解决贴标速度慢、贴标不规范、质量检测效率低的问题,且结构合理紧凑,占地面积小,成本可控,可以实现高效、高质量全自动贴标及智能检测,节省人力物力的同时提高贴标、检测的准确率和效率。     

浅谈图书自动贴标机现状及发展趋势

贴标机包括图书输送系统、提供标签系统、贴标系统以及控制系统。其中，图书输送系统由步进电机驱动的传送带实现。提供标签系统可以利用小型步进电机对图书的底纸施加作用力。贴标系统由贴标头、齿轮齿条机构、小型电机组成，贴标头安装在齿条的一端，通过步进电机驱动齿轮，使贴标头做上下直线运动，实现对图书粘贴标签的目的。控制系统的核心硬件是西门子PLC，整个系统的每一个电机以及真空发生装置的启动时间、运行速度以及移动距离都由PLC控制，PLC的使用使整个贴标机的贴标过程更加精确。     

线缆贴标设备集合打印和标签贴放功能

Brady公司推出了Wraptor锐普线缆贴标打印机,使线缆的标识过程更高效.Wraptor锐普系统将标签打印和贴放功能集中在一台机器中,大大降低了线缆的标识成本.     

基于机器视觉的物体包装盒实时贴标系统设计

为了实现打印贴标机智能贴标的需求,提出了一种基于机器视觉的物体包装盒实时贴标系统设计方案,并完成了系统的软件设计和整套硬件测试。该系统的硬件部分主要用来获取图像数据以及进行打印贴标操作,软件部分采用基于轮廓提取和形状特征识别相结合的图像处理技术,能够完成对其输出图像进行目标识别和实时定位,并通过串口通信将数据转发给控制系统。实际应用表明,该系统具有操作简便、测试准确的特点,达到了设计要求。     

普贴趣 PT-1010——掌中办公伴侣

兄弟（Brother）公司再次推出“普贴趣”（P-Touch）标签打印机新产品PT-1010。“普贴趣”作为一种特种打印机，由于针对性强、功能独特，在行业的内部有非常高的知名度。与传统的手写标签相比，“普贴趣”打印出来的标签更为美观，并且速度更快，还可进行批量打印，缩短了标签的制作时间，使标签制作更加方便、快捷、高效。     

热固性酚醛树脂移印标识牢固性研究

镀金盖板广泛应用于军品集成电路的气密性封装中,表面标识通常采用油墨移印方式进行打标。移印打标后的电路在经历表贴后易出现标识脱落的问题,导致用户无法辨认。通过研究移印打标过程中采用的热固性酚醛树脂油墨固化机理,确定了电路经历异常高温、助焊剂污染、清洗剂清洗及外力摩擦与标识牢固性的关系,有效指导了表贴厂家在后续表贴时的注意事项。     

游戏情怀科健K618单款评测

由于和三星的特殊关系,科健出品的多款手机其实都是引进三星的机型,再由科健冠名推向市场。这次评测的科健K618也正如外界传言的那样,就是三星X400的简体中文版。送测的评测样机是由韩国三星制造的,在电池仓内所贴的标签上,赫然标着“三星SGH-X408”字样。     

融合标签关联关系与用户社交关系的微博推荐方法

通过分析微博特点及现有微博推荐算法的缺陷,提出一种融合了标签间关联关系与用户间社交关系的微博推荐方法.采用标签检索策略对未加标签和标签较少的用户进行加标,构建用户-标签矩阵,得到用户标签权重,为了解决该矩阵中稀疏的问题,通过挖掘标签间的关联关系,继而更新用户-标签矩阵.考虑到多用户之间社交关系对挖掘用户兴趣并进行微博推荐的重要性,构建用户-用户社交关系相似度矩阵,并与更新后的用户-标签矩阵进行迭代,得到最终的用户兴趣并进行相关推荐.实验证明了该算法针对微博信息推荐是有效的.     

可贴敷于人体的RFID 标签天线设计

设计了一款可贴敷于人体的超高频(UHF)射频识别(RFID)标签天线,该天线由矩形辐射单元、环形 馈电和匹配网络组成,其总体尺寸为54. 3 mm×40 mm×0. 8 mm。分析了人体不同部位、天线与人体间距以及天线自 身结构变化对天线辐射性能的影响。通过调节环形馈电和匹配网络尺寸可以实现天线与芯片之间的阻抗共轭匹 配。样品测试结果与仿真结果比较吻合,当标签天线贴敷在人体背部时,实际测量的最大读取距离可达4. 9 m。     

NPLS宽带网络技术讲座——第2讲 标签分发协议（LDP）

标签分发是指一个标签交换路由器（LSR）向另一个LSR通造其标签／转发等价类的绑定信息。标签分发的过程实际上就量标签交换路径建立的过程,标签分发协议是MPLS的信令协议,因而在MPLS的体系结构中占有非常重要的地位,本文介绍了标答分发协议中的一些基本概念,并结合兴例阐述了标签分发协议涉及到的标签操作和相应的消息处理过程。     

The VIDEOGRAPH®electrostatic printing process

An electrostatic printing process called VIDEOGRAPH makes use of a special cathode-ray tube for the generation of latent electrostatic images. Television-like signals produce these images on the surface of dielectrically coated paper by means of a special face plate. This face plate contains a large array of finely spaced electrical conductors which permit the transfer of the electrostatic image produced by the electron beam from the vacuum side of the face plate to the dielectric paper in contact with the atmospheric side of the face plate. These images may be developed and fixed in much the same manner as other electrostatic images. Equipment based on the VIDEOGRAPH process has been principally applied to the production of magazine address labels. The developed equipment produces address labels from digital input data at rates up to 14 000 lines per minute. This results in over 1 000 000 labels per 8 hour operational periods. Other equipments include facsimile, digital line printer, and high-speed point plotters. Printers using the process tend to be quiet in operation, have relatively few reciprocating parts, and provide relatively troublefree high-speed operations. The output is limited to a single copy and, to achieve high resolution, rather expensive precision circuits are required.     

广播电视节目标签标注与可视化研究

针对传统广播电视节目类型和节目标签并行存在,无法定量刻画节目与标签接近程度,节目与标签关联关系展示效果不佳的问题,本文提出了电视节目与标签“粘度”的概念,通过为电视节目标签赋予不同权重,并利用D3插件建立节目与标签的扁平化网络结构关系,实现了节目与标签关系的定量描述及可视化展示。通过抓取网络热播剧标签,对标签粘度进行计算及可视化展示,证明本文提出的方案能够直观、动态地将标签粘度关系进行有效展示,可以为后续的数据分析和数据挖掘提供技术基础。     

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.     

Label preference schemes in GMPLS controlled networks

The GMPLS assumption that all available labels are equal is reasonable in electronic networks but not always true in WDM optical networks where labels correspond to physical wavelengths. In this paper we present two schemes for collecting the preference for specific labels during GMPLS signaling. For this purpose a new use of the Suggested Label object is proposed, and a novel object called Suggested Vector is introduced. The approach is validated through simulations showing significant wavelength converter usage reduction in a WDM optical network     

双输入双任务注意力网络融入噪声标签纠正机制的人脸美丽预测

人脸美丽预测是研究让计算机具有与人相似的人脸美丽预测能力的前沿课题,目前存在监督信息不足、模型易受噪声标签影响等问题。多任务注意力网络（Multi-Task Attention Network,MTAN）利用单个数据库的多种标签类型数据进行监督训练,但忽略了多个仅有一种标签类型的数据库进行多任务训练时效果不佳的问题;同时,未考虑噪声标签对MTAN的影响。噪声标签纠正机制通过比较最大预测概率和标签对应预测概率,来纠正噪声标签。为此,本文结合MTAN,提出双输入双任务注意力网络（Dual-Input Dual-Task Attention Network,DIDTAN）,并融入噪声标签纠正机制。其中,DIDTAN能同时利用两个单标签类型人脸美丽数据库的监督信息,从而解决监督信息不足;而该网络融入噪声标签纠正机制,解决了噪声标签的影响,进而提高了人脸美丽预测准确率。DIDTAN将MTAN中任务共享的批量归一化层（Batch Normalization,BN）扩展为不同任务特定的BN层;引入神经辨别性降维（Neural Discriminative Dimensionality Reduction,NDDR）模块约束浅层特征的表达;同时,使用深度相关对齐（Deep CORrelation Alignment,Deep CORAL）损失函数约束全连接层特征表达;通过噪声标签纠正机制来纠正噪声标签。在大规模人脸美丽数据库（Large Scale Facial Beauty Database, LSFBD）、SCUFBP-5500数据库、CelebA数据库上实验,基于LSFBD、SCUFBP-5500数据库的双输入双任务人脸美丽预测取得65.4%的预测准确率,高于常规方法最高准确率。所提方法能实现双输入双任务训练并解决噪声标签影响,提高了人脸美丽预测准确率,可广泛应用于其他存在噪声标签的双输入双任务场景。      

DNA计算中的模板框优化方法研究

编码问题是目前DNA计算中的重点和难点之一,编码问题的难点就是当这些编码以某种方式线性连接起来表示一个特定的信息(如图的一个路径或一个最大团等),如何确保其中的每个编码能被唯一的识别.因此,如何有效使用编码是编码研究中要解决的另一个问题.本文在模板编码的基础上,提出了模板框的概念,并对其移位距离性质进行了研究.在此基础上,考察了词标长度、单词标及多词标等因素对模板框性能的影响.计算结果表明:多词标方法能够明显改善模板框的移位距离性质.最后,指出了模板框优化的进一步的研究方向.     

MTNet: Mutual tri-training network for unsupervised domain adaptation on person re-identification

The existing unsupervised domain adaptation (UDA) methods on person re-identification (re-ID) often employ clustering to assign pseudo labels for unlabeled target domain samples. However, it is difficult to give accurate pseudo labels to unlabeled samples in the clustering process. To solve this problem, we propose a novel mutual tri-training network, termed MTNet, for UDA person re-ID. The MTNet method can avoid noisy labels and enhance the complementarity of multiple branches by collaboratively training the three different branch networks. Specifically, the high-confidence pseudo labels are used to update each network branch according to the joint decisions of the other two branches. Moreover, inspired by self-paced learning, we employ a sample filtering scheme to feed unlabeled samples into the network from easy to hard, so as to avoid trapping in the local optimal solution. Extensive experiments show that the proposed method can achieve competitive performance compared with the state-of-the-art person re-ID methods.     

Label swapping versus label preserving in packet switched networks: Impact on the label space size

In recent years, switching and networking solutions exploiting all-optical nodes are gaining increasing interest to achieve the target of ultrawide-bandwidth and low-latency packet or burst processing. On the one hand, many prototypes, validated by experimental demonstration of all-optical label processing solutions, have been developed. On the other hand, the primitive available technology for performing label processing poses several constraints on the label structures; this in turn significantly impacts the traffic engineering aspects of such a network.In this paper, the label assignment problem is studied in a network that makes forwarding decision based on optical packet labels and formulated independently of any technology. Specifically, the problem of assigning labels to identify the label switched path (LSP) packets in a unique and disjoint way is defined, with the objective of optimally minimizing the label space size (i.e. number of labels, or bits, required to uniquely identify the LSPs). The network scenarios where (i) labels have local point-to-point significance (i.e. the label is swapped when traversing each node), and (ii) labels have end-to-end significance (i.e. the label is preserved along the LSP traversing multiple nodes) are both investigated. For both scenarios, labels can be uniquely identified at each node or at each node-port. The label assignment strategies for all the possible scenarios are investigated.Both theoretical and practical methods, i.e. integer linear programming formulations and heuristics, respectively, are used to assess the efficiency of the proposed label preserving solution. Numerical results show that, for a significant set of network topologies, the label space size increase experienced by networks with label preserving capabilities is limited or negligible in both per-node and per-port label identification.     

一种适应于非完备标签数据和标签关联性的多标签分类方法

多标签分类已在很多领域得到了实际应用,所用标签大多具有很强的关联性,甚至存在非完备标签或部分标签遗失。然而,现有的多标签分类算法难以同时处理这两种情况。基于此,提出一种新的概率模型处理方法,实现同时对具有标签关联性和遗失标签情况进行多标签分类。该方法可以自动获知和掌握多标签的关联性。此外,通过整合遗失的标签信息,该方法能够提供一个自适应策略来处理遗失的标签。在完备标签和非完备标签的数据上进行实验,结果表明,与现有的多标签分类算法相比,提出的方法得到了较好的分类预测评价值。     

基于结构标签学习的显著性目标检测

提出了一种基于结构标签学习的显著性目标检测算法,将结构化学习方法应用到显著性目标检测中。首先从含有标记的图像中随机采集固定大小的矩形区域,并记录其结构标签;然后使用含结构标签的区域特征构建决策树集合;最后采用监督学习的方法对图像进行优化预测,得到最终的显著图。实验结果表明,本文方法能较准确地检测出图像库中图像的显著性区域,在数据库MSRA5000和BSD300的AUC值分别为0.891 8、0.705 2,说明本文方法具有较好的显著性检测效果。