基于CiteSpace的资源型产业研究知识图谱分析

资源型产业发展为推进国家经济增长和工业化进程提供了重要保障。为深入了解资源型产业的研究情况，以CNKI数据库为数据源，搜集2000—2020年关于资源型产业的核心及以上期刊论文，利用CiteSpace软件从发文作者与研究机构分布、关键词共现网络和时区图谱等方面，绘制知识图谱，进行可视化分析。研究发现：资源型产业领域的研究成果愈加丰富，但研究群体间联系合作较少，且现有的合作研究主要集中在所处地域资源富集和具有学科优势的研究机构及学者；资源型产业领域的研究热点可概括为产业发展、资源型城市、产业集群、产业结构、产业链和产业集聚等方面；针对资源型产业领域未来可从资源型产业相关理论研究、创新发展模式和可持续发展等方面深入展开。     

金属增材制造技术研究热点聚类分析

为深入分析金属增材制造技术分类和发展状况,采集中国期刊全文数据库(CNKI)收录的核心期刊上的768篇科技文献,借助文献分析可视化软件CiteSpace,对关键词聚类进行了全景式描绘,构建金属增材制造技术知识图谱,揭示该技术研究分类以及演化趋势。     

基于LP与小波包的复杂机电系统监测序列混沌降噪方法

针对流程工业生产系统监测数据存在强噪声和混沌性的特点,提出了一种局部投影方法(Local Projection Method)与小波包方法相结合的信号降噪方法。该方法先利用局部投影方法从动力学系统嵌入流形的角度进行多次迭代降噪,并根据关联维数来判定迭代终止;再利用小波包方法从频率的角度进行降噪,抑制高频噪声的干扰,取得了较好的降噪效果。用Lorenz时间序列进行仿真验证,对仿真时间序列加入不同程度的噪声,对比分析小波包、局部投影与该方法降噪后的相空间、SNR值和最大Lyapunov指数,证明了该方法对于中高强度噪声具有更好的降噪效果。并将该方法用于某压缩机组的实际监测数据降噪,评估三种方法的降噪效果,进一步验证了该方法的优越性。     

基于边缘和局部熵融合的视觉安全性评估方法

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性，引入图像的边缘特征，通过共有边缘来衡量加密图像与原始图像的边缘相似度，消除了块效应。由于局部熵对加密等级低的图像不敏感，边缘相似度对加密等级高的图像不敏感，将两个评估方法进行自适应融合，提出[SLEES]（Local Entropy and Edge Similarity，[SLEES]）指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试，实验结果表明，[SLEES]指标相比传统评估指标有更好的鲁棒性，评估范围更广。     

结合预训练模型和语言知识库的文本匹配方法

针对文本匹配任务,该文提出一种大规模预训练模型融合外部语言知识库的方法。该方法在大规模预训练模型的基础上,通过生成基于WordNet的同义—反义词汇知识学习任务和词组—搭配知识学习任务引入外部语言学知识。进而,与MT-DNN多任务学习模型进行联合训练,以进一步提高模型性能。最后利用文本匹配标注数据进行微调。在MRPC和QQP两个公开数据集的实验结果显示,该方法可以在大规模预训练模型和微调的框架基础上,通过引入外部语言知识进行联合训练有效提升文本匹配性能。     

Reduced basis method for managed pressure drilling based on a model with local nonlinearities

To circumvent restrictions of conventional drilling methods, such as slow control actions and inability to drill depleted reservoirs, a drilling method called managed pressure drilling (MPD) has been developed. In MPD, single-phase flow processes can be modeled as a feedback interconnection of a high-order linear system and a low-order nonlinear system. These nonlinearities appear locally both inside and at the boundaries of the computational domain. To obtain a fast simulation platform for real-time purposes (eg, online model-based controller implementation), model order reduction is required for MPD. However, the local nonlinearities render applying model order reduction techniques challenging. In this study, a new approach is proposed to deal with such nonlinearities within the reduced basis (RB) context and it is successfully tested on a model for MPD. Contrary to the classical RB technique, the proposed approach not only does not generate nonphysical spikes at the locations of these local nonlinearities but also yields high speedup factors. The obtained reduced-order model can be used for efficient online simulation and controller design for drilling systems with MPD.     

Structural optimization with an automatic mode identification method for tracking the local vibration mode

Traditional optimization methods, which take a specific order of modal frequency as the design constraint, could fail to obtain the desired solution because of modal substitution. An improved optimization model with continuous sizing variables is established to solve this problem, in which the minimum weight and a given local modal frequency are considered as the objective and the constraint. To capture accurately the expected mode of vibration, a local mode identification technique is proposed based on the strain energy ratio between the local area and the whole structure. With that scheme, an optimization system is developed, in which the local mode can be effectively identified and the constraint can be updated with it in the iteration process. Two numerical examples, of a reinforced plate and a satellite structure, are applied to illustrate the effectiveness and efficacy of the proposed method.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

煤矿局部通风机风筒保护装置设计应用

为了提高掘进巷道通风稳定性,降低局部通风机风筒损坏率及漏风系数,提高风筒使用寿命,潞安集团司马煤业公司通风队通过技术分析设计了一套风筒保护装置,该装置提高了风筒使用寿命,降低了风筒漏风量,避免了掘进巷道风量不足现象,取得了显著应用成效。