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.     

A conditional classification recurrent RBM for improved series mid-term forecasting

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

     

基于仿生嗅觉的电子鼻在煤矿火灾预报识别的应用研究

在煤矿井下存在多种复杂易燃混合气体,当外界温度或其他因素发生不稳定变化都可能引发火灾或其他事故;为了避免发生事故,提高预警机制,提前进行预报,根据生物嗅觉系统的模糊神经机理在嗅觉感知中所起的关键作用,构建了更接近生物嗅觉的模糊优化神经网络,使电子鼻系统具有仿生特性;最后通过MATLAB实验测试,根据不同温度下煤矿中的混合气味成份的不同进行成份模拟实验,实验结果表明,该电子鼻不仅具有辨识的快速性,还提高了自适应辨识的精度,从而能够准确做出预报。     

适用于复杂环境下的实时目标跟踪技术

针对复杂环境下目标跟踪过程中目标存在旋转、视角、尺度等变化以及噪声干扰的问题,提出了一种基于尺度不变特征与快速模板匹配相结合的目标跟踪技术;该技术通过分别提取预先存储模板和实时采集图像的尺度不变特征,建立初始模板;采用菱形搜索策略对模板的低分辨率子图和待跟踪图像的低分辨率子图进行快速互相关检测,根据检测结果在该帧高分辨率图像中建立紧凑ROI,在此区域内进行模板匹配,对目标进行精确定位;在目标跟踪的过程中采用自适应模板更新策略,以保证在目标发生变化时跟踪的稳定性;实验结果表明,该技术在稳定性、准确性和实时性等方面均优于传统方法。     

基于TMS320F28069的自平衡机器人控制系统设计

通过分析自平衡机器人的工作原理,设计了自平衡机器人控制系统的硬件电路.将TI公司研制的TMS320F28069作为主控制器,利用外围电路控制自平衡机器人保持自身平衡,并完成前进、后退和转向运动.最后,采用PID控制算法对自平衡机器人的姿态角和电机转速进行闭环控制,大大提高了控制精度.     

基于微电压调节的太阳能充放电系统设计

基于微电压调节的太阳能充放电系统,是以微控制器LPC1114为核心的太阳能电池板充放电电路,是实现高效率的智能充放电设计方案。微电压调节理论整合了经典电压回授法和多种太阳能最大功率点追踪思想,利用简单合理的DC／DC平衡技术,充分考虑系统功耗等问题,以简单的电路设计和创新的软件思想实现更高效率的太阳能系统。随着能源危机的加剧,高效率、低成本的太阳能系统备受社会关注,其在民用、交通等领域的运用也更加重要。     

中职学校提高《数字电路》课程教学质量的探讨

《数字电路》是中职电类专业的一门专业基础课程。结合目前中职学校教学现状中存在的问题,如何让学生把这门课学好,笔者从精选教材、课堂导入、教学手段、考核评价等方面进行了探讨。指出了只有充分激发起学生的学习兴趣和学习积极性,才是提高教学质量的关键。     

轮胎成型机旋转密封技术研究

在经济全球化的背景下,随着我国轮胎生产技术的提高,轮胎企业和世界汽车工业的迅猛发展,我国轮胎的生产量不断增加。近年来,随着轮胎生产技术不断提高,轮胎成型机科技成果不断涌现,消费者对于轮胎的耐用性、安全性、舒适度和环保方面更加优质的产品的需求不断增加。本文对轮胎成型机旋转密封的结构、系统稳定性以及使用寿命进行简单介绍,讨论了机械结构的工艺性和稳定性。     

Boosting the Microbial Electrosynthesis of Acetate from CO2 by Hydrogen Evolution Catalysts of Pt Nanoparticles/rGO

Catalysis Letters - Microbial electrosynthesis (MES) is an effective approach to driving the CO2 reduction to multi-carbon organic products using renewable energy. In this work, the MES of acetate...     

深层入岩钻孔灌注桩干成孔作业下的后注浆技术的研究

在极软岩的地质条件下,采用自创的新型注浆设备,通过不同入岩深度及成桩措施进行桩基施工,根据静载荷试验进行分析对比,得出入岩深度增加和后注浆对桩基沉降量的影响及自创注浆设备的实用性.