CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Neural Computing and Applications - Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects...     

一种提升泵车臂架位置检测精度的工程应用解决方案

针对在利用倾角传感器实现混凝土泵车之类的大型操作机的臂架姿态的间接测量方案中存在的因为臂架的柔性和操作机在不同姿态下自身水平状态的不同可能导致的臂架末端的理论计算值存在较大测量误差的问题,采用了一种在臂架的不同位置增加传感器来实现对臂架柔性变形大小和自身水平状态的实时检测的方法,并着重研究了针对具体的柔性变形及水平状态变化所导致的理论偏差进行实时补偿的切实可行的方法,实验检测的数据证明,相对补偿前臂架的定位精度提升了75％以上.     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

Predictive simulation of human walking transitions using an optimization formulation

A general optimization formulation for transition walking prediction using 3D skeletal model is presented. The formulation is based on a previously presented one-step walking formulation (Xiang et al., Int J Numer Methods Eng 79:667–695, 2009b). Two basic transitions are studied: walk-to-stand and slow-to-fast walk. The slow-to-fast transition is used to connect slow walk to fast walk by using a step-to-step transition formulation. In addition, the speed effects on the walk-to-stand motion are investigated. The joint torques and ground reaction forces (GRF) are recovered and analyzed from the simulation. For slow-to-fast walk transition, the predicted ground reaction forces in step transition is even larger than that of the fast walk. The model shows good correlation with the experimental data for the lower extremities except for the standing ankle profile. The optimal solution of transition simulation is obtained in a few minutes by using predictive dynamics method.     

聚合物泥浆抑制剂的发展动向

聚合物抑制剂是聚合物泥浆的重要组成部分。本文报导了聚合物抑制剂的发展动向,介绍了作为聚合物泥浆抑制剂的阳离子型聚合物、复合离子型聚合物、非离子型聚合物及改性的部分水解聚丙烯酰胺。     

基于互联网实现多种信息传输技术

随着信息技术的迅速发展,使用互联网实现楼宇内电话、电视、电脑、安防和环境监控、计量数据等信息的合路传输,是实现智能家居和数字楼宇的一种主要发展趋势。最终目的,实现对物和过程的智能化感知。文章首先介绍信息合路编码、中央控管电路,然后介绍网络接口电路原理和系统体系构架。重点介绍多种多样的信息传输技术。它对现代楼宇信息化、智能化建设有很好的参考作用。     

电子警务中三维全景技术应用研究

随着信息技术的飞速发展,三维全景技术已成为电子警务中重要的组成部分。本文通过对三维全景技术特点和发展状况的介绍,提出了一套三维全景地理信息系统的应用框架,探讨了三维全景技术应用中的关键技术,并结合电子警务建设实际,详细介绍了三维全景技术在电子警务中的具体应用。可以预测,随着三维全景技术和物联网技术、云计算的集成,未来三维全景技术将成为电子警务的主流技术。     

基于HCPN的发电企业项目管理工作流建模

如何对工程项目的成本、进度、质量情况进行控制和跟踪是发电企业的重要问题,应用工作流可通过对业务流程的整合及资源的合理分配解决这一问题。本文基于分层着色Petri网(HCPN),将工作流过程模型的逻辑结构和语义与HCPN中的网结构进行映射,进而利用CPN-TOOLS定义基于HCPN的项目管理工作流过程模型,并利用化简原则对工作流模型进行畅通性验证。     

一种利用高精度时钟测量中断延迟的方法

中断延迟是实时操作系统最重要的性能指标之一,其通常指外部设备发出中断请求至操作系统中断服务例程执行第一条指令经过的时间,而中断延迟的精确测量对于定量分析实时操作系统实的时性能具有重要意义。利用MPC8641D双核PowerPC片上系统可编程中断控制器的高精度时钟,实现了核间中断及定时器中断在不同负载条件下的中断延迟精确测量。实验结果表明,不同类型中断的延迟特性具有显著差异,核间中断和定时器中断的最坏情况延迟分别为1060纳秒及2540纳秒,而核间中断延迟和定时器中断延迟受不同类型负载的影响程度也有显著不同。