A Review on Robotized Automated Lay-up Technology for Composite Material Manufacturing
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摘要: 碳纤维增强复合材料(Carbon fiber-reinforced composite, CFRC)因具有轻质高强、耐腐蚀、耐冲击等优越性能, 在生产生活中的应用已越来越广泛, 然而复材产品的生产制造仍是劳动密集性产业, 主要依靠人工. 机械臂自上世纪50年代进入工业生产中以来, 极大提高了生产效率和质量, 然而目前机械臂在复材产品制造中的应用是少见的, 主要集中在机械臂形式的自动铺丝(Automated fiber placement, AFP)中. 复材产品制造工艺繁琐, 将复合材料铺放在模具上是复材产品制造过程中的一个重要环节, 本文称之为“铺层”, 使用机械臂完成复合材料自动铺层将是未来复材产品制造自动化、智能化发展的一个关键方向. 本文将机械臂进行复合材料自动铺层操作分为两种主要形式: 铺片和铺带(丝), 通过案例调研和分析, 归纳总结现有的设计理念和技术方法, 提出未来发展趋势, 以期对机械臂的应用和研究、复材产品的智能化制造和工业4.0的发展形成参考.Abstract: Carbon fiber-reinforced composite (CFRC) has been widely used in production and life because of its superior properties such as light weight and high strength, corrosion resistance and impact resistance. However, the manufacturing of composite products is still a labor-intensive industry, mainly relying on manual labor. Since the robot arm entered the industrial production in the 1950s, it has greatly improved the production efficiency and quality, however, the current application of robot arm in the manufacturing of composite products is rare, mainly focusing on the robot arm form of automated fiber placement (AFP). The manufacturing process of composite products is tedious, and laying the composite material on the mold is an important part of the manufacturing process of composite products, which we call “lay-up”, and the use of robot arm to complete the automated lay-up operation will be a key direction for the future automation and intelligent development of the manufacturing of composite products. This paper offers a thorough examination of automated lay-up operation for robot arm, and categorizes them into two primary types: Lay-up sheets and lay-up tapes (fibers). Through case study and analysis of existing design concepts and technical methods, this paper identifies trend and suggests future development direction. The insights are of significant value for application and research related to robot arm, intelligent manufacturing of composite products, and the progression of Industry 4.0.
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随着计算机技术和互联网技术的飞速发展, 当今社会进入了大数据时代, 数据的重要性也变得越来越高. 而与此相关的人工智能领域, 例如目标检测[1]、图像识别[2]和语音识别[3]等, 对数据的需求也在不断提高. 但是, 这些图像和语音类数据需要标注的数据量巨大, 采用传统的专家标注方法已经不能满足需求.
近年来, 随着AMT (Amazon mechanical turk)①、CrowdFlower①和Clickworker①等众包平台的出现, 众包技术为获取大量数据标记提供了一种经济、高效的方式. 众包学习也因此成为了一个新兴的研究领域. 在众包学习中, 首先通过在线的平台雇佣多个众包工人, 对每个实例进行标注, 获得该实例的多噪声标记集. 然后通过标记集成算法, 从每个实例的多噪声标记集中推理出一个合理的集成标记. 目前, 已经有研究者在标记集成算法的研究上做了大量工作, 例如: MV (Majority voting)算法[4]、ZC (ZenCrowd)算法[5]、MNLDP (Multiple noisy label distribution propagation)算法[6]、M3V (Max-margin majority voting)算法[7]、QS-LFC (Quality-sensitive learning from crowds)算法[8].
然而, 众包工人在专业知识水平、打标积极性和对评价指标的理解等方面的差异性, 导致数据标注的质量普遍偏低. 因此标记集成算法得到的集成标记中仍然存在一定比例的噪声, 即实例的集成标记与真实标记不一致. 标记噪声的存在会损害数据集的标记质量, 而具有高标记质量的数据集在相关研究和应用技术中又是至关重要的. 所以, 对标记集成后的数据集通过众包标记噪声纠正算法进行纠正从而提高数据集的标记质量具有重要的研究意义.
为了降低标记噪声的影响, 提高标记集成后的数据集的标记质量, 目前已经有研究者在众包标记噪声纠正方向做了一些工作, 主要可分为3类:
1)基于监督学习的众包标记噪声纠正算法. Nicholson等[9]提出了适用于众包领域的标记噪声纠正算法PL (Polishing labels). PL算法通过将数据集分成10个子集, 并在每个子集上训练一个分类器, 由生成的10个分类器对每个实例进行分类投票, 最终将得票最高的标记赋予实例. 而Xu等[10]结合重抽样的思想, 提出了一种基于重抽样的众包标记噪声纠正算法(Resampling-based noise correction, RNC). RNC通过在干净集和噪声集上按照比例多次重抽样训练多个分类器, 再用得到的多个分类器对整个数据集进行纠正, 从而提高数据集的标记质量. 除了以上两种众包标记纠正算法, 部分研究者还将研究的重点聚焦于监督学习领域知识和众包数据集信息的不确定性方法, 其中的代表性算法有AVNC (Adaptive voting noise correction)算法[11]、BMNC (Between-class margin-based noise correction)算法[12]以及CENC (Cross-entropy-based noise correction)算法[13]. AVNC算法提出了众包领域的噪声纠正框架, 利用众包服务提供的标记信息, 估计了众包工人的质量, 并进一步评估了数据集中含有噪声的比例. 然后对实例的噪声等级进行了排序, 最终通过集成模型对被认定为噪声的实例进行纠正. BMNC算法则是利用了众包数据集中实例的多噪声标记集信息, 评估每个实例的集成标记的置信程度, 从而更加精准地过滤出噪声实例, 并通过最终得到的干净集训练分类器对噪声实例进行纠正. 而CENC算法同样利用了众包数据集中实例的多噪声标记集, 计算多噪声标记集的信息熵去评估集成标记的置信程度, 然后在得到的干净集上训练多个分类器对噪声集实例进行预测, 进一步利用交叉熵的思想去衡量噪声集中实例标记的真实分布和预测分布的相似度, 从而实现对噪声实例的纠正, 提高数据集的标记质量.
2)基于无监督学习的众包标记噪声纠正算法. Nicholson等[9]提出了一种基于聚类的众包标记噪声纠正算法CC (Cluster-based correction). 聚类是无监督学习技术, 在噪声处理领域的优势是不依赖实例的类标记. 这种优势的存在使得该方法在性能上往往优于基于监督学习的算法, 但是CC算法在时间效率方面则较差. CC的核心思想是进行多次聚类算法, 在每次聚类执行时, 对每个簇中的实例计算并赋予相同的权重, 权重反映了实例属于不同类别的可能性. 最终根据每个实例的最大权值对实例进行重新标注, 从而提高数据集的标记质量.
3)基于半监督学习的众包标记噪声纠正算法. Nicholson等[9]提出的STC (Self-training correction)算法则是受自训练过程的启发, 首先用过滤器将数据集分为干净集和噪声集, 然后在干净集上训练分类器用于对噪声集中的实例进行预测打标, 再选取每类中打标置信度最高的噪声实例, 将预测标记赋予实例并加入干净集中, 重复上述步骤直到纠正的噪声实例达到设定的阈值. 通过以上步骤, 该算法提高了数据集的标记质量和模型精度.
在上文提到的3类众包标记噪声纠正算法中, 基于监督学习的方法训练分类器估计实例的集成标记质量, 并对噪声实例进行纠正; 基于无监督学习的方法不依赖数据集的集成标记, 通过数据的特征估计实例所属某类的权重; 基于半监督学习的方法是将干净集实例作为已标记实例来训练分类器, 对噪声集实例进行重新标注的方法. 在基于半监督学习的方法中, STC算法因为简单有效, 不需要特定的假设条件而被广泛应用. 但通过对STC算法的分析, 本文发现该算法仍然存在3个方面的不足: 1)当用于训练初始分类器的干净集不能很好表示整个数据空间, 难以反映数据的分布时, STC算法得到分类器的效果较差. 从而较多错误纠正的实例加入干净集, 误差的影响在循环中不断扩大, 导致STC算法效果受损; 2)经过过滤得到的干净集仍然存在噪声实例, 而STC算法是在干净集上训练单个分类器用于纠正噪声集, 因此训练的分类器纠正效果不佳; 3) STC算法是基于半监督学习领域的思想, 未充分利用众包数据集所含的标记信息, 难以取得更好的众包标记噪声纠正效果.
针对STC算法存在的不足, 本文提出了一种基于自训练的众包标记噪声纠正算法(Self-training-based label noise correction, STLNC). 本文同时使用仿真标准数据集和真实众包数据集进行了实证研究, 结果表明在噪声比和模型质量两个度量指标上, STLNC算法比其他五种最先进的标记噪声纠正算法表现更好.
1. 一种基于自训练的众包标记噪声纠正算法
1.1 背景知识
在一个众包系统中, N个实例由J个工人打标后, 获得了众包数据集
$D = \left\{ {\left( {{{\boldsymbol{x}}_i},{L_i}} \right)} \right\}_{i = 1}^N $ , 其中$ {L_i} = \left\{ {{l_{ij}}} \right\}_{j = 1}^J$ 为每个实例的多噪声标记集,$l_{ij} $ 表示实例${\boldsymbol{x}}_i $ 由众包工人${u_j}\left( {j = 1,2, \cdots ,J} \right) $ 给出的标记. 对于二分类问题,$ l_{ij} $ 属于{+, 0, −}, 其中的值分别表示众包工人对实例打正标, 未给出标记和打负标. 众包数据集$ D$ 中的每个实例$ {\boldsymbol{x}}_i $ 在经过标记集成算法推理得到集成标记${\hat y_i} $ 后, 获得了带有集成标记的众包数据集$ D' = \left\{ {\left( {{{\boldsymbol{x}}_i},{{\hat y}_i}} \right)} \right\}_{i = 1}^N$ . 而数据集$ D'$ 正是众包标记噪声纠正工作的起点.1.2 STLNC算法
上文分析了STC算法的不足, 为了提高STC算法的性能, 本文提出的STLNC算法利用加权密度峰值聚类算法构建数据集中低密度实例指向高密度实例的空间结构关系, 以便更好地指导噪声集实例的选择. 同时, 将STC中的单个分类器替换为集成分类器, 在纠正阶段利用设计的纠正策略对选择的噪声实例进行纠正, 降低了纠正的误差, 提高了数据集的标记质量和模型精度, 整个算法的框架如图1所示.
从图1可以看出, STLNC算法工作的起点是带有集成标记的数据集
$D' $ , 该数据集的集成标记是通过标记集成算法推理获得的. 在获得数据集$ D'$ 后, STLNC算法可分为3个阶段: 1)噪声过滤; 2)构建空间结构关系; 3)实例选择与纠正. 在噪声过滤阶段, 采用过滤器对$D' $ 进行过滤, 得到干净集$D_c'$ 和噪声集$D_n'$ ; 在构建空间结构关系阶段, 利用加权密度峰值聚类构建数据集中低密度实例指向高密度实例的空间结构关系; 在实例选择与纠正阶段, 根据获得的干净集和噪声集以及空间结构关系设计实例选择策略, 在每次循环中选择最适合纠正的噪声实例. 再利用干净集$D_c'$ 训练集成分类器$M $ , 对选择的噪声实例通过设计的实例纠正策略进行纠正, 然后加入到$D_c'$ 中. 循环执行实例选择和纠正步骤, 直到所有噪声实例被选择并纠正, 获得最后一轮训练的集成分类器$M'$ . 最后采用集成分类器$M'$ 通过多数投票(Majority voting)的方式对$D'$ 所有实例进行纠正, 得到最终纠正后的数据集$D_{{\rm{correct}}}'$ .在整个STLNC算法流程中, 空间结构关系的构建以及实例选择和纠正策略的设计是本算法的关键, 本文将在后续内容中详细介绍.
1.2.1 构建空间结构关系
为了解决精确选择噪声实例的问题, 受到Wu等[14]的启发, STLNC算法利用了加权密度峰值聚类算法构建数据集中低密度实例指向高密度实例的空间结构关系, 从而指导噪声实例选择过程.
密度峰值聚类的主要思想基于以下两点: 1)聚类中心的密度大于其邻居的密度, 也就是被低密度实例围绕; 2)聚类中心与更高密度的实例之间的距离较远, 即簇间距离较大. 对于众包数据集
$D^{'} $ 中每一个实例${{\boldsymbol{x}}_i} $ , 它的局部密度$ {\rho _i}$ 定义为$$ {\rho _i} = \sum\limits_{j,j \ne i} {{I} }\left( {{d_{ij}} < {d_c}} \right) $$ (1) 其中,
${I}\left( \cdot \right)$ 为指示函数,$ {d_{ij}} < {d_c}$ 值取1, 否则为0.${d_{ij}} $ 为实例$ {{\boldsymbol{x}}_i}$ 与${{\boldsymbol{x}}_j} $ 之间的距离, 一般为欧氏距离.$ d_c$ 为截断距离, 具体定义为$$ {d_c} = {d_{C_N^2 \times s}} $$ (2) 其中,
${d_{C_N^2 \times s}} \in [ {{d_1},{d_2}, \cdots ,{d_{C_N^2}}}]$ , 该集合包含每对实例之间的距离, 并按照升序排序.${d_{C_N^2 \times s}}$ 代表该集合中第${C_N^2 \times s}$ 个距离, s为原始密度峰值聚类算法给出的经验参数, 本文取值为0.2.在获取到局部密度
${\rho _i} $ 之后, 密度峰值聚类算法需要进一步得到实例$ {{\boldsymbol{x}}_i}$ 的${\delta _i} $ 值,${\delta _i} $ 定义为: 1) 对于非最高密度实例${{\boldsymbol{x}}_i} $ , 计算实例${{\boldsymbol{x}}_i} $ 与较高密度最近邻的距离; 2) 对于最高密度实例${{\boldsymbol{x}}_i} $ , 计算实例$ {{\boldsymbol{x}}_i}$ 和最远实例之间的距离. 具体为$$ {\delta _i} = \left\{ {\begin{array}{*{20}{l}} {{{\max }_j}\left( {{d_{ij}}} \right),}&{\forall j,\;{\rho _i} \ge {\rho _j}}\\ {{{\min }_{j:{\rho _i} < {\rho _j}}}\left( {{d_{ij}}} \right),}&{其他} \end{array}} \right. $$ (3) 在计算出所有实例的局部密度
$\rho _i $ 和距离$ \delta _i$ 之后, 局部密度$\rho _i $ 和距离$ \delta _i$ 均相对较大的实例将作为簇的中心, 剩下的实例被归于密度较高的最近邻所属的簇, 从而得到最终的聚类结果.在本文中, 为了精确构建众包数据集实例的空间结构关系, 我们将众包数据集的信息引入密度峰值聚类算法的距离计算中. 对于每个众包数据集中的实例, 实例所带有的多噪声标记集以及众包数据集的工人质量是能够反映实例之间相关关系的重要属性. 因此本文在计算实例
$ {{\boldsymbol{x}}_i}$ 和$ {{\boldsymbol{x}}_j}$ 之间的距离${d_{ij}} = dist\left( {{{\boldsymbol{x}}_i},{{\boldsymbol{x}}_j}} \right) $ 时, STLNC算法通过使用实例的多噪声标记集衡量实例之间的相关关系, 具体定义如下:$$ \begin{split} {d_{ij}} = \;&{dist}\left( {{{\boldsymbol{x}}_i},{{\boldsymbol{x}}_j}} \right)=\\ & {w_{ij}}\sqrt {\sum\limits_{k = 1}^K {{{\left( {{x_{ik}} - {x_{jk}}} \right)}^2}} } \end{split} $$ (4) 其中,
$K $ 是特征维度,${w_{ij}} $ 表示工人对实例$ {\boldsymbol{x}}_i$ 和$ {\boldsymbol{x}}_j$ 的打标相似度. 为了利用实例的多噪声标记集计算实例${\boldsymbol{x}}_i $ 和$ {\boldsymbol{x}}_j$ 的打标相似度, 首先需要根据实例的噪声标记集${L_i} = \left\{ {{l_{ij}}} \right\}_{j = 1}^J $ , 构建每个实例的标记分布, 对二类数据集, 实例具体的标记分布为${{\boldsymbol{P}}_i} = \left\{ {p_i^ + ,p_i^ - } \right\}$ , 其中$ {p_i^ + }$ 代表的是所有工人对实例${{\boldsymbol{x}}_i} $ 打标为正类的概率,${p_i^ - } $ 代表的是所有工人对实例$ {{\boldsymbol{x}}_i}$ 打标为负类的概率. 具体为$$ \left\{\begin{aligned} &p_i^ + = \frac{{Nu{m_i}( + ) + 1}}{{Nu{m_i}( + ) + Nu{m_i}( - ) + 2}}\\ &p_i^ - = \frac{{Nu{m_i}( - ) + 1}}{{Nu{m_i}( + ) + Nu{m_i}( - ) + 2}} \end{aligned}\right. $$ (5) 其中,
${Nu{m_i}} $ (+)为实例${{\boldsymbol{x}}_i} $ 多噪声标记集中正类标记的数目,$ {Nu{m_i}( - )}$ 为实例${{\boldsymbol{x}}_i}$ 多噪声标记集中负类标记的数目. 为了满足后续标记分布之间关系计算的需要, 式(5)使用了拉普拉斯纠正[15]. 在本算法中, 使用标记分布的好处是能够使用数值显式地表示工人打标的分布, 以便精确估计两个实例之间打标的相似程度. 因此在获得每个实例的标记分布之后, 我们采用KL (Kullback-Leibler)散度[16]来计算两个实例标记分布的近似程度, KL散度越小则说明工人对两个实例打标的情况越接近, 因此$ w_{ij}$ 也越小, 具体为$$ \begin{split} {w_{ij}} =\;& \frac{1}{2}\left( {KL\left( {{{\boldsymbol{P}}_i}||{{\boldsymbol{P}}_j}} \right) + KL\left( {{{\boldsymbol{P}}_j}||{{\boldsymbol{P}}_i}} \right)} \right)=\\ &\frac{1}{2}\left( {\sum\limits_{m \in \{ + , - \} } {{p_i}} (m) \cdot \log \frac{{{p_i}(m)}}{{{p_j}(m)}}}+ \right.\\ &\left. { \sum\limits_{m \in \{ + , - \} } {{p_j}} (m) \cdot \log \frac{{{p_j}(m)}}{{{p_i}(m)}}} \right) \end{split} $$ (6) 其中,
${{p_i}(m)} $ 表示实例$ {{\boldsymbol{x}}_i}$ 标记属于$m $ 类的概率.本文在根据实例之间的打标相似度
$ w_{ij}$ 得到$d_{ij} $ 后, 继续进行密度峰值聚类过程. 直到计算实例的${\delta _i} $ 值时, 会为每个实例${{\boldsymbol{x}}_i} $ 找到具有更高密度的最近邻实例$ {{\boldsymbol{x}}_j}$ . 根据Wu等[14]的证明, 通过实例${{\boldsymbol{x}}_i} $ 指向实例${{\boldsymbol{x}}_j} $ 可以发现整个数据空间的真实结构. 这种由低密度实例${{\boldsymbol{x}}_i} $ 指向高密度实例${{\boldsymbol{x}}_j} $ 的空间结构关系不会因为实例的标记改变而发生变化, 是一种稳定的结构关系, 能够指导我们发现密度更高更具有代表性的实例. 因此, 本文执行加权密度峰值聚类步骤计算所有实例的${\delta _i} $ , 从而也获得了整个众包数据集的空间结构关系.1.2.2 实例选择与纠正策略
在通过加权密度峰值聚类算法构建数据集中低密度实例指向高密度实例的空间结构关系之后, 本文结合噪声过滤阶段得到的干净集和噪声集, 设计了两种噪声实例选择策略. 首先, 本文定义了两类实例点, 当实例
$ {{\boldsymbol{x}}_i}$ 指向实例${{\boldsymbol{x}}_j} $ , 那么定义$ {{\boldsymbol{x}}_i}$ 为${{\boldsymbol{x}}_j} $ 的“previous”点,$ {{\boldsymbol{x}}_j}$ 为${{\boldsymbol{x}}_i} $ 的“next”点.1) 实例选择策略A: 在整个数据空间中, 搜寻所有“previous”点为干净集实例, “next”点为噪声集实例的实例对, 将每个实例对中的噪声集实例作为纠正的对象;
2)实例选择策略B: 在整个数据空间中, 搜寻所有“previous”点为噪声集实例, “next”点为干净集实例的实例对, 将每个实例对中的噪声集实例作为纠正的对象.
在实例选择与纠正阶段, 首先根据实例选择策略A选择噪声实例, 当没有满足条件的噪声实例时, 再使用实例选择策略B选择实例, 直到噪声集实例全部纠正并加入干净集. 根据第1.2.1节的说明, 本文构建空间结构关系目的是为了发现实例之间的真实结构, 指导算法寻找更高密度更具代表性的实例, 从而训练出更加可靠的分类器. 因此本文先执行了实例选择策略A来寻找具有更高密度的噪声实例, 这些高密度噪声实例更具代表性. 将这些高密度噪声实例先进行选择并纠正能够使扩充后的干净集更好的代表整个数据空间, 使训练得到的分类器更加可靠, 提高标记纠正的效果. 在执行完实例选择策略A直到没有满足条件的噪声实例后, 算法将训练得到一个可靠的分类器. 为了进一步扩充干净集, 本文随后执行了实例选择策略B, 对剩余的低密度噪声实例进行选择, 从而完成了整个实例选择的过程.
解决噪声实例选择的问题后, 剩下的问题就是如何设计实例纠正策略, 提高纠正的准确性. 在STLNC算法中, 为了提高纠正的准确性, 本文将用于标记纠正的单个分类器替换为由3个异质的分类器构成的集成分类器. 同时根据第1.2.1节得到的每个实例的标记分布
${{\boldsymbol{P}}_j} $ , 计算每个实例多噪声标记集的信息熵值$Entropy\left( {{{\boldsymbol{P}}_i}} \right) $ , 从而估计该实例集成标记的置信度$e_i $ . 当$e_i $ 越小时, 说明工人打标的一致性越高, 则集成标记的置信度越高; 反之则集成标记的置信度越低.$ e_i$ 的具体计算为$$ {e_i} = { Entropy }\left( {{{\boldsymbol{P}}_i}} \right)= - \sum\limits_{m \in \{ + , - \} } {{p_i}} (m)\log {p_i}(m) $$ (7) 其中,
${p_i}(m) $ 表示实例$ {{\boldsymbol{x}}_i}$ 标记属于$ m$ 类的概率.本文结合上述集成分类器和众包数据集标记信息, 设计了STLNC的实例纠正策略:
1)当实例
${{\boldsymbol{x}}_i} $ 的集成标记置信度$e_i $ 大于设定的阈值$T $ , 则说明${{\boldsymbol{x}}_i} $ 集成标记的质量较低. 当集成分类器对${{\boldsymbol{x}}_i} $ 进行纠正时, 采用多数投票(Majority voting)的策略对$ {{\boldsymbol{x}}_i}$ 进行标注.2)当实例
$ {{\boldsymbol{x}}_i}$ 的集成标记置信度$ e_i$ 小于设定的阈值$T $ , 则说明${{\boldsymbol{x}}_i} $ 集成标记的质量较高. 当集成分类器对$ {{\boldsymbol{x}}_i}$ 进行纠正时, 采用共识投票(Consensus voting)的策略对$ {{\boldsymbol{x}}_i}$ 进行标注, 即当三个分类器打标结果相同, 则对${{\boldsymbol{x}}_i} $ 进行纠正, 否则不纠正${{\boldsymbol{x}}_i} $ 的标记.综上所述, 本文提出的STLNC算法的详细步骤如算法1所示.
算法1. STLNC算法
输入. 带集成标记的数据集
${D'} = \left\{ {\left( {{{\boldsymbol{x}}_i},{{\hat y}_i}} \right)} \right\}_{i = 1}^N$ , 实例集成标记置信度阈值$ T$ .输出. 纠正后的干净数据集
$D_{{\rm{correct}}}'$ .应用CF过滤器过滤数据集
${D'}$ , 将${D'}$ 分为干净集$D_c'$ 和噪声集$D_n'$ ;for
$i = 1\;{\rm{to}}\;{\rm{sizeof}}\left( {{D'}} \right)$ do根据实例
$i $ 的多噪声标记集$L_i $ , 获得$i $ 的标记分布$ {{\boldsymbol{P}}_i}$ ;end for
for
$i = 1\;{\rm{to}}\;{\rm{sizeof}}\left( {{D'}} \right)$ dofor
$j = 1\;{\rm{to}}\;{\rm{sizeof}}\left( {D'} \right)\;\& \& \;i! = j $ do计算实例
$ i$ 和$j $ 之间的$w_{ij} $ ;根据
$ w_{ij} $ , 计算实例$i $ 和$j $ 的距离$d_{ij} $ ;end for
根据
$d_{ij} $ 计算实例$i $ 的局部密度$ {\rho _i}$ ;根据
$d_{ij} $ 和$ {\rho _i}$ 计算实例$ i$ 的$ {\delta _i}$ 值;end for
获取带有指向关系的实例对集合
${D_{{\rm{couple}}}} =\{ \left( previou{s_i}, nex{t_i} \right) \}_{i = 1}^U$ ;count = 1;
while
$count > 0\;\& \& \;{\rm{sizeof}}\left( {D_n'} \right)> 0$ docount = 0;
在
$D_c'$ 上训练集成分类器$ M$ ;for
$i = 1\;{\rm{to}}\;{\rm{sizeof}}\left( {{D_{{\rm{couple}}}}} \right)$ doif
$previou{s_i} \in D_c'\;\& \& \;nex{t_i} \in D_n'$ 通过集成分类器
$ M$ 对实例$nex{t_i} $ 按照实例纠 正策略进行纠正, 并加入干净集$D_c'$ ;count++;
end if
end for
根据新的干净集和噪声集更新
$ {D_{{\rm{couple}}}}$ ;end while
count = 1;
while
$count > 0\;\& \& \;{\rm{sizeof}}\left( {D_n'} \right)> 0$ docount = 0;
在
$D_c'$ 上训练集成分类器$M $ ;for
$i = 1\;{\rm{to}}\;{\rm{sizeof}}\left( {{D_{couple}}} \right) $ doif
$previou{s_i} \in D_n'\;\& \& \;nex{t_i} \in D_c'$ 通过集成分类器
$ M$ 对实例$previou{s_i} $ 按照实例 纠正策略进行纠正, 并加入干净集$D_c'$ ;count++;
end if
end for
根据新的干净集和噪声集更新
${D_{{\rm{couple}}}} $ ;end while
获得最后一轮循环训练的集成分类器
$M'$ ;用
$M'$ 对$D'$ 中所有实例进行纠正, 得到$D_{{\rm{correct}}}'$ ;return
$D_{{\rm{correct}}}'$ .2. 实验设计与结果分析
在本节中
$, $ 为了验证提出的STLNC算法的有效性, 本文在仿真标准数据集和真实众包数据集上进行了实验. 将STLNC算法与目前5种最先进的众包标记噪声纠正算法PL[9]、STC[9]、CC[9]、AVNC[11]和CENC[13]在纠正后数据集噪声比以及训练模型的模型质量两个度量指标上进行了比较. 其中, 数据集噪声比(Noise ratio)定义为经过算法纠正后的众包数据集中集成标记与真实标记不同的实例所占整个数据集的百分比. 模型质量(Model quality)定义为在纠正后的众包数据集上训练的目标分类模型的测试精度.利用CEKA (Crowd environment and its knowledge analysis)平台[17], 本文实现了STLNC算法和CENC算法, 同时使用了该平台现有的MV、PL、STC、CC和AVNC算法和WEKA (Waikato environment for knowledge analysis)平台[18]上现有的K-means算法、C4.5算法、KNN (K-nearest neighbor)算法和LR (Logistic regression)算法. 6种众包标记噪声纠正算法在实验中设置如下:
1) PL: PL算法使用的基分类器为C4.5;
2) STC: STC算法使用的基分类器为C4.5, 使用的过滤器为CF (Classification filter)过滤器[19], 需要被纠正的噪声实例比例设置为0.8;
3) CC: CC算法使用的聚类方法为K-means聚类算法, 该算法执行次数设置为10次, 而簇中心的个数设置为2到数据集实例个数的一半;
4) AVNC: AVNC算法使用的基分类器为C4.5, 数据集分割为大小相同子集的个数设置为10个, 训练的分类器个数设置为5;
5) CENC: CENC算法使用的基分类器为C4.5, 训练的分类器个数设置为10个, 而实例的多噪声标记集的信息熵阈值
$ T$ 设置为0.1;6) STLNC: STLNC算法使用的集成分类器由C4.5、KNN
$(K = 3)$ 和LR构成, 使用的过滤器为CF过滤器, 截断距离$d_c $ 中s的值设置为0.2, 实例的多噪声标记集信息熵阈值$T $ 设置为0.1.其中, CF过滤器将整个数据集分割为10个大小相同的子集, 而且所使用的基分类器同样为C4.5.
2.1 仿真标准数据集上的实验
为了验证STLNC在不同工人质量和数据特征下的有效性, 本文使用了22个二分类标准数据集由仿真实验模拟不同专业水平的工人进行打标, 数据信息具体如表1所示, 其中“#Ins”表示数据集实例的数量, “#Att”表示数据集实例的属性维度, “#Pos”表示标记为正的实例数量, “#Neg”表示标记为负的实例数量. 为了模拟众包过程为每个实例获取多噪声标记集, 对使用的数据集隐藏了实例的真实标记, 同时模拟了9个工人对每个实例打标的过程, 其中每个工人的打标质量为
${p_j}\;( j = 1,2, \cdots , 9)$ . 这代表着每个工人有$ p_j$ 的概率给实例打正确标记,$1-p_j $ 的概率打错误标记. 值得注意的是, 数据集中的正例和负例均有$p_j $ 概率被正确标记,$ 1-p_j$ 概率被错误标记.表 1 22个仿真标准数据集详细描述Table 1 Description of 22 simulated benchmark datasets数据集 #Ins #Att #Pos #Neg biodeg 1055 41 356 699 breast-cancer 268 9 85 201 breast-w 699 10 241 458 credit-a 690 16 383 307 credit-g 1000 21 300 700 diabetes 768 8 268 500 heart-statlog 270 14 120 32 hepatitis 155 20 123 32 horse-colic 368 22 232 136 ionosphere 351 35 225 126 kr-vs-kp 3196 37 1527 1669 labor 57 16 37 20 mushroom 8124 23 3916 4208 sick 3772 30 231 3541 sonar 208 61 111 97 spambase 4601 57 813 2788 tic-tac-toe 958 10 332 626 vote 435 17 168 267 climate 540 20 494 46 colic 368 22 136 232 monks 432 6 228 204 steel-plates-faults 1941 33 673 1268 为了保证在不同工人质量下实验结果的可靠性, 我们设置了两种工人质量的方案:
1)在第1个系列的实验中, 所有工人的打标质量设置为0.6, 即
$ p_j=0.6$ ;2)在第2个系列的实验中, 所有工人的打标质量由均匀分布的区间
$\left[ {0.55,0.75} \right] $ 随机生成, 即${p_j}\in \left[ {0.55,0.75} \right] $ .在为每个实例获得9个带有噪声的众包标记之后, 本文使用最经典的标记集成算法MV获取每个实例的集成标记, 并将该算法处理后的数据集噪声比和训练模型质量作为基线. 然后, 使用6种不同的众包标记噪声纠正算法对带有集成标记的众包数据集中的噪声实例进行识别并纠正, 目标分类模型将在纠正后的数据集上进行训练. 最后, 本文将评估每个数据集上各种众包标记噪声纠正算法纠正后的噪声比以及训练模型质量. 值得注意的是, 与评估数据集的噪声比不同, 我们在评估训练模型质量的时候采用了10折交叉的验证方法, 特别是在同一个数据集上运行不同算法的时候用了相同的训练集以及测试集.
表2和表3详细展示了在工人质量
$p_j=0.6 $ 的情况下各个算法在不同数据集上的实验结果. 根据表2中的纠正后的数据集中的噪声比以及表3中的模型质量, 本文采用了威尔科克森(Wilcoxon)符号秩检验[20-21]来比较实验用到的每一对标记噪声纠正算法. 表4和表5分别展示了每组实验的威尔科克森符号秩检验的比较结果. 其中符号“•”代表该行中的算法明显优于对应列中的算法, 符号“$\circ$ ”代表该列中的算法明显优于对应行中的算法. 表中, 主对角线以下区域的显著性水平为$ \alpha = 0.05$ ; 而主对角线以上区域的显著性水平$ \alpha = 0.1$ .表 2 工人质量0.6时的噪声比对比结果 (%)Table 2 Noise ratio comparisons with pj = 0.6 (%)数据集 MV PL STC CC AVNC CENC STLNC biodeg 28.25 29.95 28.34 19.53 18.48 21.90 15.83 breast-cancer 27.62 26.92 25.87 31.12 26.57 29.37 24.84 breast-w 28.76 9.01 19.31 10.30 9.16 8.44 7.30 credit-a 26.67 20.00 15.94 18.84 13.04 13.33 12.90 credit-g 26.60 27.40 28.40 26.60 25.30 27.50 26.40 diabetes 26.69 32.29 26.56 26.95 23.70 23.96 22.79 heart-statlog 25.19 19.26 23.70 22.96 24.07 25.93 18.52 hepatitis 30.32 19.35 26.45 20.65 27.74 25.16 30.97 horse-colic 27.72 32.34 17.39 21.20 17.66 14.13 14.13 ionosphere 27.92 16.24 21.65 9.12 10.83 13.39 11.68 kr-vs-kp 27.38 21.96 10.45 19.34 2.19 2.85 2.28 labor 31.58 24.56 24.56 15.79 12.28 31.58 7.02 mushroom 26.71 12.65 6.43 4.30 0.04 0.10 0 sick 27.60 2.60 8.83 10.31 1.78 2.28 3.37 sonar 26.92 31.73 29.33 24.04 25.00 24.52 18.75 spambase 27.02 27.47 19.50 14.78 9.11 10.56 8.06 tic-tac-toe 26.20 34.13 23.07 24.43 22.44 22.23 14.61 vote 25.98 4.60 10.34 11.26 3.91 4.14 4.14 climate 27.41 8.52 27.41 14.07 8.52 8.52 8.52 colic 27.45 22.28 20.92 23.10 14.13 14.95 13.59 monks 26.39 25.00 11.34 21.76 5.32 6.71 2.78 steel-plates-faults 27.51 34.98 9.22 18.70 0 0.10 0.15 平均值 27.45 21.97 19.77 18.60 13.69 15.08 12.21 表 3 工人质量0.6时的模型质量对比结果(%)Table 3 Model quality comparisons with pj = 0.6 (%)数据集 MV PL STC CC AVNC CENC STLNC biodeg 71.37 75.91 72.13 78.77 74.34 74.21 78.29 breast-cancer 67.00 71.28 69.98 69.08 70.92 68.13 69.73 breast-w 92.85 92.47 90.68 93.38 94.00 92.85 95.54 credit-a 82.03 84.78 84.49 84.78 83.91 83.04 83.48 credit-g 62.20 68.30 67.40 69.70 67.30 63.90 70.40 diabetes 71.74 70.56 71.50 70.80 71.72 72.12 74.00 heart-statlog 65.56 74.81 67.78 70.00 74.07 69.26 78.15 hepatitis 69.00 77.67 71.33 77.50 72.00 70.17 74.17 horse-colic 77.42 78.11 80.00 80.15 81.62 81.36 82.35 ionosphere 83.48 85.45 86.90 85.19 85.77 84.04 85.76 kr-vs-kp 95.18 95.49 96.62 90.29 96.81 96.59 97.03 labor 70.67 61.83 73.50 68.33 68.33 72.33 76.33 mushroom 99.85 98.56 99.88 99.90 99.90 99.86 99.83 sick 96.74 94.62 96.98 94.48 97.77 97.75 97.11 sonar 58.93 55.57 50.07 58.29 55.00 59.14 58.29 spambase 85.92 88.87 87.44 84.20 89.68 88.98 90.39 tic-tac-toe 77.17 69.74 74.54 71.63 74.63 74.63 78.36 vote 89.89 95.37 94.21 90.59 94.21 93.98 94.21 climate 91.48 91.48 91.48 91.48 91.48 91.48 91.48 colic 79.97 81.09 81.09 82.47 81.09 82.47 81.09 monks 90.75 90.73 93.51 83.35 93.51 93.51 93.28 steel-plates-faults 100.00 89.64 100.00 92.01 100.00 100.00 100.00 平均值 80.87 81.47 81.89 81.20 82.64 82.26 84.06 表 4 工人质量0.6时的噪声比的威尔科克森测试结果Table 4 Noise ratio summary of Wilcoxon tests with pj = 0.6MV PL STC CC AVNC CENC STLNC MV — ◦ ◦ ◦ ◦ ◦ ◦ PL — ◦ ◦ ◦ ◦ STC • — ◦ ◦ ◦ CC • — ◦ ◦ ◦ AVNC • • • • — • ◦ CENC • • • • ◦ — ◦ STLNC • • • • • • — 表 5 工人质量0.6时的模型质量的威尔科克森测试结果Table 5 Model quality summary of Wilcoxon tests with pj = 0.6MV PL STC CC AVNC CENC TTLNC MV — ◦ ◦ ◦ ◦ PL — ◦ STC — ◦ ◦ CC — ◦ AVNC • • — • ◦ CENC • — ◦ STLNC • • • • • • — 表2 ~ 5给出了第1个系列实验的详细对比, 验证了STLNC算法在提高数据集标记质量和模型质量上的有效性. 具体结论如下:
1) STLNC算法纠正后的数据集的平均噪声比为12.21%, 低于MV (27.45%)、PL (21.97%)、STC (19.77%)、CC (18.60%)、AVNC (13.69%)和CENC (15.08%).
2) STLNC算法纠正后的数据集训练的目标模型的平均模型质量为84.06%, 高于MV (80.87%)、PL (81.47%)、STC (81.89%)、CC (81.20%)、AVNC (82.64%)和CENC (82.26%).
3) 根据威尔科克森符号秩检验的结果, STLNC算法在噪声比和模型质量两个指标上都要显著优于对比的众包标记噪声纠正算法.
在第2个系列实验中, 表6 ~ 9详细展示了在工人质量
${p_j} \in \left[ {0.55,0.75} \right] $ 的情况下各个众包标记噪声纠正算法在纠正后数据集噪声比和模型质量上的实验结果, 以及威尔科克森符号秩检验中的算法差异显著性的对比结果. 根据表6和表7中的实验结果, 在数据集的噪声比和模型质量两个度量指标上, STLNC算法的效果都是最好的. 同时, 根据表8和表9中的威尔科克森符号秩检验的结果, STLNC算法在纠正后数据集的噪声比上显著优于对比算法MV、PL、STC、CC, 与AVNC、CENC算法性能相当. 而在模型质量上显著优于对比算法MV、PL、STC、CC、CENC, 与AVNC算法性能相当.表 6 工人质量[0.55, 0.75]时的噪声比对比结果 (%)Table 6 Noise ratio comparisons with pj ∈ [0.55, 0.75] (%)数据集 MV PL STC CC AVNC CENC STLNC biodeg 14.22 21.14 16.02 13.84 13.46 13.08 12.89 breast-cancer 16.43 26.22 20.98 19.93 23.43 24.83 24.48 breast-w 20.46 3.72 10.01 4.15 4.15 4.43 3.72 credit-a 18.41 20.58 14.93 13.62 13.77 13.04 12.17 credit-g 17.70 29.60 22.70 22.90 21.60 22.30 24.60 diabetes 20.18 22.66 24.09 22.27 23.44 22.66 23.44 heart-statlog 16.30 20.37 15.19 20.00 16.67 16.67 18.52 hepatitis 12.26 20.65 14.19 14.84 16.77 12.90 12.26 horse-colic 17.66 15.49 13.86 18.75 14.67 14.13 15.22 ionosphere 17.38 18.80 13.68 9.69 11.11 10.83 13.96 kr-vs-kp 17.43 25.19 5.60 11.55 1.31 1.88 2.44 labor 17.54 29.82 17.54 12.28 21.05 21.05 14.04 mushroom 18.07 4.94 4.84 1.67 0.10 0.11 0 sick 13.94 1.78 4.98 3.76 1.46 1.54 2.04 sonar 15.38 37.50 21.63 25.96 19.23 22.60 20.67 spambase 19.32 37.54 15.11 9.04 7.00 7.04 6.67 tic-tac-toe 20.67 27.45 19.31 17.54 15.76 14.41 6.47 vote 22.07 6.90 10.57 8.97 4.37 4.83 4.60 climate 22.96 8.52 22.96 10.74 8.52 8.52 8.52 colic 16.58 19.57 15.49 17.93 15.22 14.40 15.49 monks 17.13 12.73 7.18 23.38 2.78 4.86 2.78 steel-plates-faults 22.46 34.83 7.32 15.92 0.26 0.26 0.21 平均值 17.93 20.27 14.46 14.49 11.64 11.65 11.15 表 7 工人质量[0.55, 0.75]时的模型质量对比结果 (%)Table 7 Model quality comparisons with pj ∈ [0.55, 0.75] (%)数据集 MV PL STC CC AVNC CENC STLNC biodeg 74.58 81.87 76.36 79.99 81.59 80.25 81.78 breast-cancer 69.43 71.81 69.50 70.27 71.28 71.64 71.64 breast-w 90.76 94.54 91.10 94.34 93.85 92.40 94.69 credit-a 82.17 85.36 84.78 85.65 85.65 84.64 84.93 credit-g 69.50 69.80 70.50 69.60 71.10 69.00 72.00 diabetes 71.67 74.44 71.15 74.21 73.13 72.87 74.56 heart-statlog 70.00 78.89 76.30 75.93 79.63 78.52 80.37 hepatitis 76.17 79.17 77.33 80.50 76.83 77.83 79.00 horse-colic 82.50 80.35 81.57 82.63 83.51 83.01 82.68 ionosphere 80.62 83.48 82.33 88.03 87.73 86.90 84.07 kr-vs-kp 97.94 95.34 97.66 92.86 98.28 98.00 98.06 labor 78.33 68.17 78.33 64.33 77.17 77.17 84.33 mushroom 99.99 98.52 99.95 99.96 100.00 100.00 99.95 sick 97.72 96.95 97.48 95.47 97.64 97.83 97.14 sonar 63.79 68.93 67.14 70.36 69.29 69.50 70.86 spambase 86.65 88.87 88.07 84.83 90.37 88.96 90.76 tic-tac-toe 77.83 73.00 77.58 76.30 77.89 76.85 80.08 vote 93.35 93.79 94.98 92.68 95.24 95.00 94.77 climate 91.48 91.48 91.48 91.48 91.48 91.48 91.48 colic 83.74 77.90 84.19 79.17 82.53 82.63 81.84 monks 98.37 85.21 98.60 88.16 100.00 100.00 100.00 steel-plates-faults 99.90 100.00 100.00 99.69 100.00 100.00 100.00 平均值 83.48 83.54 84.38 83.47 85.65 85.20 86.14 表 8 工人质量[0.55, 0.75]时的噪声比的威尔科克森测试结果Table 8 Noise ratio summary of Wilcoxon tests with pj ∈ [0.55, 0.75]MV PL STC CC AVNC CENC STLNC MV — ◦ ◦ ◦ ◦ ◦ PL — ◦ ◦ ◦ ◦ ◦ STC • — ◦ ◦ ◦ CC • — ◦ ◦ ◦ AVNC • • • • — CENC • • • • — STLNC • • • • — 表 9 工人质量[0.55, 0.75]时的模型质量的威尔科克森测试结果Table 9 Model quality summary of Wilcoxon tests with pj ∈ [0.55, 0.75]MV PL STC CC AVNC CENC STLNC MV — ◦ ◦ ◦ ◦ PL — ◦ ◦ ◦ STC • — ◦ ◦ ◦ CC — ◦ ◦ ◦ AVNC • • • • — • CENC • • ◦ — ◦ STLNC • • • • • — 根据上述两个系列的实验结果, 在不同的工人质量下的仿真数据集上都验证了本文提出的STLNC算法在提高数据集的标记质量和模型质量上的有效性, 并且STLNC算法在两个评估指标上都整体优于对比的5个众包标记噪声纠正算法.
为了分析实例的多噪声标记集信息熵阈值
$ T$ 的取值对STLNC算法的影响, 同时节省版面和避免重复, 本文随机选择了仿真数据集“ionosphere”在工人质量0.6的情况下进行实验, 同时使用了相同的过滤器CF保证每轮实验的干净集和噪声集相同. 最后评估了在不同$ T$ 值的情况下STLNC算法在纠正后数据集上噪声比的情况.在本文中设置
$T $ 值的目的是协助实例纠正策略, 防止集成标记质量高的实例被实例纠正策略给错误纠正. 从图2可以看出, 随着$ T$ 值的变化, 噪声比呈现梯度上升, 从而验证了本文设置$T $ 值的想法,$T $ 值的取值范围为0.1 ~ 0.5, 为了精确筛选高标记质量实例, 本文设置$T$ 为较小值0.1.
图 2 不同T值的STLNC在ionosphere数据集上的噪声比结果Fig. 2 Noise ratio of STLNC with different T values on ionosphere dataset2.2 真实众包数据集上的实验
为了进一步验证STLNC算法的有效性, 本文使用CEKA平台自带的真实众包数据集Leaves和文献[22]中使用的真实众包数据集LabelMe进行实验. 同时, 在真实众包数据集上的实验, 本文还针对STLNC算法过滤阶段过滤器的选取进行了讨论. 最后, 针对STLNC中构建空间结构关系阶段和实例选择与纠正阶段进行了消融实验.
Leaves数据集包含384个带有63维特征的实例. 每个实例都是一张树叶图片, 由众包工人根据树叶的特征进行标注, 因此Leaves数据集中每个实例都带有多个含噪声的标记. 而LabelMe数据集则是包含1000个带有200维特征的实例. 每个实例都是一张拍摄的图像, 同样发布在AMT平台上, 由不同众包工人进行标注.
本文从Leaves和LabelMe中提取了8个只包含二类的数据集, 这8个数据集分别记为Leaves1, Leaves2, Leaves3, Leaves4, LabelMe1, LabelMe2, LabelMe3和LabelMe4, 这些数据集的具体细节如表10所示. 例如: “LabelMe1”的目标是判断实例为高速公路还是街道的图片. 该数据集中包含199个实例, 其中89个为正例(高速公路), 110个为负例(街道). 为获得每个实例的多噪声标记集, 共有50个众包工人标注了395个标记. 需要注意的是, 在本文的实验中, 针对LabelMe数据集, 首先使用文献[23]中图像处理的方法将图像数据转换为文本词向量, 然后通过文本分类器对数据集进行训练和预测. 其中, 使用MNB (Multinomial naive Bayes)算法[24]作为PL、STC、CC、AVNC和CENC的基分类器, 而STLNC的基分类器则替换为MNB、CNB (Complement naive Bayes)[25]和OVA (One-versus-all-but-one)[25].
表 10 8个真实众包数据的详细描述Table 10 Description of eight real-world crowdsourced datasets数据集 分类任务 #Instances #Positives #Negatives #Labelers #Labels Leaves1 maple/alder 142 96 46 70 1093 Leaves2 maple/tilia 140 96 44 74 1044 Leaves3 alder/eucalyptus 93 46 47 58 407 Leaves4 alder/poplar 89 46 43 54 400 LabelMe1 highway/street 199 89 110 50 395 LabelMe2 highway/forest 227 89 138 54 476 LabelMe3 highway/opencountry 240 89 151 54 375 LabelMe4 highway/insidecity 205 89 116 49 339 图3 ~ 6分别展示了各个算法在8个真实众包数据集上纠正后的噪声比和模型质量的详细对比结果. 从对比结果可以看出: 在Leaves数据集上, STLNC算法的效果整体优于MV、PL、STC、CC、AVNC以及CENC算法, 而在LabelMe数据集上, STLNC算法的优势更加显著. 因此, 通过上述实验的比较, STLNC同时提高了数据集的标记质量和模型质量, 验证了该算法在真实众包场景的有效性.
为了分析过滤器的选取对STLNC算法的影响, 本文选取了传统机器学习领域经典的噪声过滤器CF、IPF (Iterative partitioning filtering)[26]和MVF (Majority vote filter)[27], 三个过滤器具体描述如下:
1) CF: CF的思想是将整个数据集分为大小相等的
$n $ 个子集, 对于其中的每一个子集, 都将剩余的$n-1 $ 个子集合并作为基分类器的训练集并进行训练, 然后对该子集中的实例进行预测, 若预测标记和集成标记不同, 则该实例为噪声, 并从整个数据集中去除. 上述步骤重复$n $ 次, 直到整个数据集的实例被预测. 在本实验中CF的$n $ 值设置为10, 基分类器使用MNB.2) IPF: IPF的思想将整个数据集分为大小相等的
$n $ 个子集, 在每个子集上训练一个分类器来对数据集中每个实例进行预测, 再根据投票策略判断实例是否为噪声, 如果是则移出数据集. 执行上述的步骤直到筛选出的噪声实例的比例小于阈值时停止. 在本实验中IPF的$n $ 为5, 阈值为0.01, 投票策略为多数投票, 所使用的基分类器为MNB.3) MVF: MVF的思想是将整个数据集分为大小相等的
$n $ 个子集, 对于其中的每一个子集, 都将剩余的$n-1 $ 个子集合并作为$ m$ 个基分类器的训练集并进行训练, 然后对该子集中的实例进行预测, 再根据多数投票的策略判断该实例是否为噪声. 上述步骤重复$n $ 次, 直到整个数据集的实例被预测. 在本实验中MVF的$n $ 值设置为10,$m $ 值设置为3, 所使用的基分类器为MNB、CNB和OVA.根据以上噪声过滤器设置, 本文随机选取了真实众包数据集LabelMe4进行了实验, 并评估了使用不同过滤器的STLNC在纠正后的数据集上的噪声比和训练模型质量.
图7(a)和图7(b)分别展示了使用各个过滤器的STLNC算法在LabelMe4上的噪声比和模型质量的详细对比结果. 从对比结果可以看出: 使用CF、IPF和MVF过滤器的STLNC在噪声比和模型质量上结果相似. 由此分析可得, STLNC在选用不同过滤器时表现稳定, 对算法性能影响不大.
图 7 STLNC基于不同过滤器在LabelMe4数据集上的实验结果Fig. 7 Experimental results of STLNC with different filters on LabelMe4 dataset在第2节中, 本文介绍了在构建空间结构关系阶段和实例选择与纠正阶段的主要改进为: 1)使用众包数据信息对密度峰值聚类进行加权, 构建空间结构关系; 2)使用集成分类器, 同时设计实例选择和纠正策略. 为了详细说明STLNC算法的这两个核心阶段的重要性, 本文还在LabelMe4上进行了消融实验. 消融实验对比算法说明如下:
1) STLNC-(1): 表示该算法与STLNC相比, 未使用加权的密度峰值聚类构建空间结构关系, 只使用原始的密度峰值聚类算法.
2) STLNC-(2): 表示该算法与STLNC相比, 未使用集成分类器和实例选择与纠正策略. 只使用单个分类器MNB对噪声实例进行预测, 将预测后的标记就作为纠正的结果.
3) STLNC-(1)(2): 表示该算法与STLNC相比, 未使用加权的密度峰值聚类构建空间结构关系以及未使用集成分类器和实例选择与纠正策略. 只使用原始的密度峰值聚类算法构建空间结构关系, 使用了单个分类器MNB对噪声实例进行预测, 将预测后的标记就作为纠正的结果.
从图8(a)和图8(b)中可以看出, STLNC算法在纠正后数据集上的噪声比和训练的模型质量表现最好, STLNC-(1)和STLNC-(2)算法表现其次, STLNC-(1)(2)的算法效果最差. 从而可以得出结论: STLNC中两个核心阶段的改进均能提高STLNC算法的性能.
图 8 STLNC在LabelMe4数据集上的消融实验结果Fig. 8 Results of STLNC ablation experiment on LabelMe4 dataset3. 结束语
本文针对众包数据集经过标记集成后仍然存在标记噪声问题, 提出了一种基于自训练的众包标记噪声纠正算法STLNC. 主要创新包括: 1) 在构建空间结构关系阶段, 本文将实例的多噪声标记集转化为标记分布, 根据标记分布信息改进了密度峰值聚类算法, 从而精确地构建数据集中低密度实例指向高密度实例的空间结构关系; 2) 在实例选择与纠正阶段, 根据构建的空间结构关系, 设计了噪声实例选择策略, 同时引入集成学习的思想和标记置信度计算, 设计了新的实例纠正策略, 提高了纠正的准确性.
根据在22个仿真的标准数据集以及8个真实的众包数据集上的实验结果, STLNC与PL、STC、CC、AVNC、CENC五种目前最先进的噪声纠正算法相比, 其性能在数据集噪声比和模型质量两个度量指标上更好, 从而验证了所提出的众包标记噪声纠正算法的有效性和优越性. 但是, 本文方法在算法的过滤阶段并未进行深入的研究, 如何获取更加精确的干净集和噪声集仍然是未来将要面临的难题之一. 因此, 下一步工作将围绕设计更好的过滤策略, 提高过滤效果展开.
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图 1 复材产品制造工艺的生产流程图
Fig. 1 Production flow chart of the manufacturing process of composite products
图 6 面向工业生产的多机械臂协同铺层研究案例
Fig. 6 Multi-robot arms collaborative lay-up study cases for industrial production
表 1 机械臂在传统工业场景和复材产品制造场景应用特点对比
Table 1 Comparison of the application characteristics of robot arm in traditional industrial scenario and composite products manufacturing scenario
对比 特点 传统工业场景 复材产品制造场景 喷涂 点焊 搬运 装配 铺片 铺带(丝) 相同之处 重复定位精度 高 位置跟踪要求 高 不同之处 操作是否接触 否 是 是 是 是 是 操作材料特性 气体 高温 硬质 硬质 柔软粘性 柔软粘性 是否需要加热 否 是 否 否 是 是 是否有接触力 否 否 是 是 是 是 末端构造 喷嘴 焊钳 夹持 各类工具 夹持悬垂拾取 专有铺放头 
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表 2 不同拾取原理的优劣对比
Table 2 Comparison of the advantages and disadvantages of different pick-up principles
拾取原理 对材料的损坏程度 成本 实现难度 易操作性 针刺 高 低 低 高 低温 中 中 中 中 真空吸取 无 高 中 中
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表 3 单机械臂铺层研究案例对比
Table 3 Comparison of single robot arm lay-up study cases
研究机构 研究重点 路径规划 运动规划 工艺参数 系统软件 使用的机械臂 相关文献 德国宇航中心 全过程自动化 基于视觉生成 系统生成 未知 独立开发 KUKA [45−46, 71−72] 汉堡科技大学 工艺流程优化 未知 未知 未知 未知 ABB [73−76] 慕尼黑工业大学 全过程自动化 人类专家设计 控制器生成 人类专家设计 CFK-Tex.Office KUKA KR-500 [32−34, 77] 布里斯托大学 铺放自动化 未知 未知 人类专家设计 未知 ABB [62] 德国宇航中心 全过程自动化 系统生成 系统生成 未知 独立开发 KUKA [78−79] 南丹麦大学 铺放自动化 基于模拟方法 系统生成 未知 独立开发 KUKA KR-360 [60, 80−84]
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表 4 多机械臂协同铺层研究案例对比
Table 4 Comparison of multi-robot arms collaborative lay-up study cases
研究机构 机械臂数量 研究内容 路径规划 运动规划 系统软件 使用的机械臂 相关文献 南卡罗莱纳大学 3 路径规划 运动规划 算法生成 控制器生成 独立开发 KUKA-iiwa [85−87, 89−91] 斯图加特大学 3 系统搭建 路径规划 人类专家设计 系统生成 独立开发 ABB [64, 92] 德国宇航中心 2 系统搭建 路径规划 算法生成 系统生成 独立开发 KUKA-KR270 [93−101] 空客集团 2 系统搭建 末端开发 人类专家设计 系统生成 独立开发 KUKA [41−42, 106] 林雪平大学 2 技术验证 末端开发 未知 未知 未知 KUKA-KR10, ABB [107−108] 慕尼黑工业大学 2 系统搭建 路径规划 算法生成 系统生成 独立开发 Staubli, KUKA [24] 思克莱德大学 1 技术验证 人类专家设计 系统生成 独立开发 KUKA-KR6 [110] 维也纳技术大学 2 技术验证 人类专家设计 系统生成 未知 自制 [111−112]
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表 5 铺带(丝)头中采用的切割方式对比
Table 5 Comparison of cutting methods used in tape (fiber) lay-up heads
切割方式 成本 优点 缺点 机械道具切割 低 结构简单, 切割效率高, 适用于多种复杂环境,
维修更换比较方便难以控制切割深度且切口毛糙, 损伤预浸料,
无法保证切口质量激光切割 较高 切割效率高, 非接触式切割, 产品边缘光滑平整,
激光对位精准, 切割精度高温度较高, 使复合材料发生变质且
切割深度不易控制水喷射切割 低 设备结构简单, 操作容易, 工作机构具有喷头体积小、
后坐力小、移动方便、生产效率高等特点给整个铺带环境带来大量污染液体,
影响复合材料成型, 铺带工作不便超声波切割 较高 切割效率高, 切口平整; 合适的切割速度、
切割深度满足不同工况下的切割易受负载、温度等因素影响, 引起谐振频率、
等效阻抗等参数漂移变化
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表 6 铺带(丝)头中采用的加热方式对比
Table 6 Comparison of heating methods used in tape (fiber) lay-up heads
加热方式 成本 优点 缺点 电阻丝加热 低 加热均匀, 实现简单 热损失大, 功率密度低, 使用寿命短 激光加热 高 激光加热效率高, 响应快 温度难以控制, 容易产生局部过热 热风加热 低 温度场均匀, 调节范围广 加热升温时间长, 热效率较低 红外加热 高 热效率高, 加热均匀, 响应速度快 辐射面存在一定限制, 温度场不均匀
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表 7 路径规划方法对比
Table 7 Comparison of path planning methods
分类 方法 优点 缺点 参考路径生成 自然路径法 可以避免纤维起皱, 轨迹可铺放性良好 计算量大, 仅适用于低曲率表面 定角度路径法 原理及计算过程简单 仅适用于整体曲率波动较小的曲面 变角度路径法 能够自适应芯模曲面不规则情况 算法计算量大 路径密化 等距偏置算法 算法简单, 能够覆盖整个芯模表面 在复杂表面上可能存在间隙和重叠 等角度算法 算法实现简单, 适应各种复杂构件 易存在间隙和重叠
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关键技术 研究内容 研究目标 轨迹规划 根据构件3D表面设计相应路径规划算法, 自适应生成铺放轨迹 满足构件结构的方向性、铺放顺序和铺叠层数要求 铺放路径覆盖 根据曲面上相邻路径的间距, 对铺丝路径的覆盖性进行检验与优化 实现对模具的满覆盖、不重叠, 满足空隙容差 边界处理 根据构件的边界轮廓信息, 设计边界处理算法, 控制边缘和角部的铺放方式与形态 确保铺放边界质量和表面光洁度 后置处理 数控代码生成、代码优化与合成、加工仿真技术等 机器人能够识别执行的指令
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表 9 现有自动化缺陷检测技术优劣对比
Table 9 Comparison of the advantages and disadvantages of existing automated defect detection technologies
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