气体动压轴承动态参数测定原理

将气体动压轴压简化为转化气膜系统后，针对其动力特点，给出气膜在微幅振动下动压轴承的实用数学模型和测定动态参数的方法，并进行了测量误差的修正。     

石化行业自动化控制仪表常见故障分析及其处理

介绍化工生产过程中仪表故障及其处理措施。     

基于Ad Hoc网络的SMR优化算法的研究

SMR是一种基于DSR算法的多路径路由算法,这种算法的本身具有明显的局限性。第一,它可能在存在多条不相关多路径路由的情况下选择两条相关多路径路由作为最后的主路由与备份路由;第二,它在目的节点只收到一个RREQ分组的情况下只生成一条主路由,而实际可能存在多条传输线路。针对SMR算法的局限性,本文提出一种改进的SMR算法—ISMR,该算法保证了两条路由的形成,保证了在存在多条不相关多路径路由情况下,一定会生成两条不相关多路径路由。     

500kV变压器油色谱在线监测装置故障处理

在我国的电力发展过程中,变压器对输电系统具有举足轻重的作用,因此及时发现变压器潜伏性故障对于变压器的正常运行显得尤为重要。变压器油色谱在线监测装置就可以及时发现变压器本体内部存在的潜在故障,对变压器的安全运行起到很好的监视作用。但前提是油色谱在线监测装置运行及数据正常,才能正确分析变压器存在的故障。对油色谱在线监测装置运行过程中出现的故障现象进行描述,针对出现的现象进行深入分析,有效避免主变事故的发生,最终保证变压器长期安全稳定运行。     

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.