基于B/S结构的协同装配的关键技术

分析了协同设计中协同装配的应用模式。在具备协同感知的前提下针对同步协同装配和异步协同装配两种应用模式分别提出对应的体系结构设计。提出基于B／S结构协同装配的具体实现方法，并实现了一套基于B／S结构的协同装配工具。     

Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter

In this paper, we present a real-time 3D pointing gesture recognition algorithm for mobile robots, based on a cascade hidden Markov model (HMM) and a particle filter. Among the various human gestures, the pointing gesture is very useful to human-robot interaction (HRI). In fact, it is highly intuitive, does not involve a-priori assumptions, and has no substitute in other modes of interaction. A major issue in pointing gesture recognition is the difficultly of accurate estimation of the pointing direction, caused by the difficulty of hand tracking and the unreliability of the direction estimation.The proposed method involves the use of a stereo camera and 3D particle filters for reliable hand tracking, and a cascade of two HMMs for a robust estimate of the pointing direction. When a subject enters the field of view of the camera, his or her face and two hands are located and tracked using particle filters. The first stage HMM takes the hand position estimate and maps it to a more accurate position by modeling the kinematic characteristics of finger pointing. The resulting 3D coordinates are used as input into the second stage HMM that discriminates pointing gestures from other types. Finally, the pointing direction is estimated for the pointing state.The proposed method can deal with both large and small pointing gestures. The experimental results show gesture recognition and target selection rates of better than 89% and 99% respectively, during human-robot interaction.     

基于路径签名的改进时空图卷积网络

针对时空图卷积网络ST-GCN中GCN的关节邻接图不易学习远端关节之间的语义信息和TCN在描述时间信息方面存在不足的问题,引入了数字签名预处理来增强数据,提出了基于路径签名的改进时空图卷积网络SSIT-GCN。首先将关节位置坐标的时间序列输入签名层进行数据预处理,在该层时间序列通过嵌入算法被转换为多维路径,将其划分为多条路径并计算每条路径的签名特征;其次重新设计GCN的关节邻接矩阵,并用反卷积来代替补零,以保持TCN的尺寸不变,还引入1×1的卷积核增加非线性来改进ST-GCN,得到改进时空图卷积网络SIT-GCN;最后用签名特征代替原始数据输入SIT-GCN,得到最终的输出结果。实验结果表明,基于路径签名的改进时空图卷积网络大大提高了训练精度,缩短了训练时间,对动态手势识别有较好的识别能力和识别速度。     

实时协同设计系统的设计与实现

分析了基于对象的协同设计系统中操作冲突的特点,给出了冲突与相容关系及其基于逻辑时间钟的冲突检测方法和一致性保证算法。在此基础上,讨论了一个实时协同设计系统的设计方面有关的关键技术,并用一个实例验证了系统的效能。     

Early detection of human actions—A hybrid approach

Early detection of human actions is essential in a wide spectrum of applications ranging from video surveillance to health-care. While human action recognition has been extensively studied, little attention is paid to the problem of detecting ongoing human action early, i.e. detecting an action as soon as it begins, but before it finishes. This study aims at training a detector to be capable of recognizing a human action when only partial action sample is seen. To do so, a hybrid technique is proposed in this work which combines the benefits of computer vision as well as fuzzy set theory based on the fuzzy Bandler and Kohout's sub-triangle product (BK subproduct). The novelty lies in the construction of a frame-by-frame membership function for each kind of possible movement. Detection is triggered when a pre-defined threshold is reached in a suitable way. Experimental results on a publicly available dataset demonstrate the benefits and effectiveness of the proposed method.     

Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.     

基于人脸运动信息和改进保局投影的疲劳识别

提出了一种基于人脸运动信息和改进保局投影的疲劳识别方法。利用光流技术计算人脸皮层的运动速度,并以此作为疲劳特征;为了有效地进行疲劳特征降维,提出了改进的保局投影方法,该方法很好地保留了数据的局部流形结构和全局结构;采用加权k近部的方法进行疲劳识别。实验结果表明该方法具有很好的识别效果。     

Adaptation and learning in control of voluntary movement by the central nervous system

In order to control voluntary movements, the central nervous system must solve the following three computational problems at different levels: (1) the determination of a desired trajectory in visual coordinates; (2) the transformation of its coordinates into body coordinates; and (3) the generation of motor command. Concerning these problems, relevant experimental observations obtained in the field of neuroscience are briefly reviewed. On the basis of physiological information and previous models, we propose computational theories and a neural network model which account for these three problems. (1) A minimum torque-change model which predicts a wide range of trajectories in human multi-joint arm movements is proposed as a computational model for trajectory formation. (2) An iterative learning scheme is presented as an algorithm which solves the coordinate transformation and the control problem simultaneously. This algorithm can be regarded as a Newton-like method in function spaces. (3) A neural network model for generation of motor command is proposed. This model contains internal neural models of the motor system and its inverse system. The inverse-dynamics model is acquired by heterosynaptic plasticity using a feedback motor command (torque) as an error signal. The hierarchical arrangement of these neural networks and their global control are discussed. Their applications to robotics are also discussed.     

对话意图及语音识别错误对交互体验的影响

在自然人机对话中,由于环境噪声、方言口音等因素带来的语音识别错误以及语义分析的不充分等原因,计算机在理解用户交互意图时出现偏差,使得计算机对要反馈的话题出现错误,造成人机对话进程的断裂.以面向咖啡为主题的漫谈式人机对话为例,将对话中断分为3种情况：话题反馈不当引起中断、话题正确情况下的模糊反馈不当和精确反馈不当引起中断.根据用户与计算机对话的记录分析比较上述3种情况下人机对话进程断裂情况.统计数据结果表明,话题反馈不当带来的对话中断最为明显,在对话进程断裂情况中达到了60.1%的比例;在话题反馈正确情况下,模糊回答不当和精确回答不当带来的话题中断比例分别为22.2%和21.6%;在语音识别错误情况下,语义分析会带来数量更大的反馈错误.实验数据分析结果表明,在语音识别错误情况下,根据上下文信息提高计算机对用户话题反馈的准确率,能够有效降低人机对话的中断,提高人机对话的自然度.该工作为自然人机对话的意图分类重要性提供了数据分析和实验论证.     

基于多模态特征学习的人体行为识别方法

由于从单一行为模态中获取的特征难以准确地表达复杂的人体动作,本文提出基于多模态特征学习的人体行为识别算法.首先采用两条通道分别提取行为视频的RGB特征和3D骨骼特征,第1条通道C3DP-LA网络由两部分组成:(1)包含时空金字塔池化(Spatial Temporal Pyramid Pooling,STPP)的改进3D...     

基于融合几何特征时空图卷积网络的动作识别

最近,基于骨架的动作识别研究受到了广泛关注.因为图卷积网络可以更好地建模非规则数据的内部依赖,ST-GCN (spatial temporal graph convolutional network)已经成为该领域的首选网络框架.针对目前大多数基于ST-GCN的改进方法忽视了骨架序列所蕴含的几何特征.本文利用骨架关节几何特征,作为ST-GCN框架的特征补充,其具有视觉不变性和无需添加额外参数学习即可获取的优势,进一步地,利用时空图卷积网络建模骨架关节几何特征和早期特征融合方法,构成了融合几何特征的时空图卷积网络框架.最后,实验结果表明,与ST-GCN、2s-AGCN和SGN等动作识别模型相比,我们提出的框架在NTU-RGB+D数据集和NTU-RGB+D 120数据集上都取得了更高准确率的效果.     

基于关联分区和ST-GCN的人体行为识别

基于骨骼的动作识别因不受人体物理特征的影响,简单清晰地传达了人体行为识别的重要信息而受到广泛关注.传统的应用程序骨架建模通常依赖遍历规则的人为设置而导致表达能力有限和推广困难.因此,在近年来热门的时空图卷积网络(ST-GCN)模型基础上提出了一种新的划分骨架关节点的分区策略.该策略相比于原始分区方法加强了身体相对位置之...     

面向对手建模的意图识别方法综述

首先介绍了对手建模的几种不同的类型,引出行为建模中的意图识别问题;随后针对意图识别的过程、分类、主要研究方法、研究展望以及实际应用进行了归纳分析,总结并讨论了相关领域取得的最新研究成果;最后指出意图识别目前存在的不足以及未来的发展方向.     

区分交互意图的图卷积协同过滤算法

近几年提出了一些基于图卷积网络的协同过滤推荐模型,然而大部分模型将邻域权重视为常量且不区分用户和物品间的交互关系,无法获取令用户满意的推荐列表。因此,为了得到用户和物品更准确的嵌入表示,提出一种区分交互意图的图卷积协同过滤推荐算法MiGCCF(multi-intention graph convolutional collaborative filtering)。该算法将交互关系进行分解,细粒度分析用户与物品间的交互意图,并引入注意力机制,在消息传播过程中赋予邻域可学习的注意力权重,挖掘用户对于不同交互物品的喜爱度。在Gowalla与Amazon-book上的实验表明,该算法相比于基准算法,在两个数据集上的HR@50和NDCG@50指标分别提高了12.5%和8.5%,具有更好的性能表现。     

具有实时反馈的草图交互识别方法

针对同步在线草图识别算法中的效率和应用范围问题,提出基于增量式意图提取的识别算法．算法通过定义滞后窗口,采用增量式意图提取的方式理解用户的勾画意图,进而根据当前信息修正以前生成的意图段落,使得识别结果和用户的勾画意图保持一致．实验证明,该算法能够准确、实时地识别用户输入的多种图形．