恒温式热量计测定煤发热量准确度的探讨

通过采用恒温式热量计测定煤发热量的试验，得出影响测定结果准确度的主要因素是热容量、内筒中的水量、室温与外筒间温差的结论。     

散射式浊度测量仪

浊度是一项评定水质的重要技术指标。如何提高测量浊度的精度是国内外一直在研究的问题之一。文章介绍了作者在散射式浊度测量仪设计过程中，为提高浊度的测量精度和稳定性所做的工作。在测量仪的部件（光学系统，电路系统等）设计中，采取了许多特殊方法，使浊度测量的各项精度指标都得到了较大的提高。     

加速器磁场测量的霍尔多探头装置

描述了用于加速器磁场测量的霍尔多探头装置,它包括一个具有三只恒温盒(内有58只霍尔片)的测磁长臂,高精度磁场检定系统以及数据自动获取系统。分别叙述了霍尔片的定位、恒温、校准、数据采集、处理等内容。并提出了多探头装置测量中霍尔片稳定性的重要性。给出了在我们条件下的一些具体数据。本装置已应用在兰州重离子研究装置(HIRFL)的建造过程中,实践证明它能有效地高质量地满足加速器磁场测量的要求。     

电子引伸计的数字智能化研究

由于目前的电子引伸计精度不高、不快捷、智能化程度不高,对其数字智能化方向的研究十分必要。从数字智能式电子引伸计的特点、关键技术、技术指标及应用入手,制定开发的技术路线,并介绍相应的信号处理技术。     

平行双关节坐标测量机的精度设计

平行双关节坐标测量机是一种新型非正交坐标测量设备,具有结构简单,精度高,便携等优点。文章首先介绍了平行双关节坐标测量机的测量原理,然后分析了影响仪器测量精度的误差源及误差源的可能修正精度,计算了仪器测量总误差。根据仪器设计精度进行了误差分配,并对误差分配进行了仿真计算,总结了仪器误差分布规律。本文的研究为指导测量机的结构设计和标定提供了依据,为实现平行双关节坐标测量机的测量精度指标提供了扎实的理论基础。     

灰色理论的位移传感器标定不确定度评定

硅晶元等高精度平面度测量时,为提高测量可信性,对所用位移传感器标定已成为业界常规。本研究针对用于基于逐次两点法(STPM)测量硅平面的两电容式位移传感器2804/3890 J设计了一种压电陶瓷标定系统,实现其标定间隔达到8nm/1 mV,提高了标定的精确度。建立基于灰色理论的GM(0,2)系统数学模型,确立待标定传感器测量结果的回归模型,以得到传感器特性的最佳估计,并确定其联合不确定度,得到被标传感器的测量不确定度的最佳评估。与传统回归模型比较,得出GM(0,2)模型具有很好的精度,满足要求。通过STPM测量过程的模拟,得到用此传感器实施的STPM测量的测量精度约为56 nm(λ=20 nm)。     

认知定位系统的自适应精度研究

为了能够实现定位精度的自适应,本文分析了认知无线电定位系统(cognitive positioning systems,CPS)的原理。该系统利用频谱及环境感知等模块中的传感器单元来获取所需的定位参数,并将其与到达时间(time of arrival,TOA)定位估计算法相结合,以克拉美罗下界(Cramer-Rao lower bound,CRLB)为准则分析最大似然(maximum likelihood,ML)定位精度自适应算法。本文还对单径和多径环境条件下定位精度自适应性能进行了比较。仿真结果表明,该算法能够实现定位精度的自适应,并能通过改善系统可用的频带带宽、分散频谱的数目、信号的调制方式等因素来提高定位精度。     

Digital twin modeling method based on biomimicry for machining aerospace components

High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder.     

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. In this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.     

一种新的考虑注意力机制的微博谣言检测模型

随着社交媒体的迅速发展,谣言通过社交媒体迅速传播,识别社交媒体网络上的谣言是社交网络研究中一个至关重要的问题.本文提出了一种新的考虑注意力机制的微博谣言检测模型,考虑到卷积神经网络(CNN)提取到的特征对输出结果影响力问题,在经典的文本卷积神经网络(Text CNN)上引入了注意力机制,通过CNN中的卷积层学习微博窗口的特征表示,再根据每个特征表示对输出结果的影响力不同通过注意力机制赋予不同的权重来进行谣言事件的检测.研究结果表明,本文提出的微博谣言检测模型准确率达到了96.8%,并且在召回率和F1值上也有提升,即本文提出的新的微博谣言检测模型具有更好的谣言识别能力.