连续流动分析仪应用于水中硫化物的测定

对连续流动法测定硫化物进行初步探讨和研究。同时与其他几种测定硫化物的方法进行了比较。实验结果表明，连续流动法对硫化物分析在线性、稳定性、最低检测限、准确性和精密度等方面均优于其他方法。     

货运调车场减速顶噪声及治理的探讨

铁路货物运输枢纽的调车场中，驼峰调车是主要的调车手段，为提高生产效率和确保安全，沿驼峰下滑道布置的减速器（减速闸，减速顶）的噪声是调车场噪声的主要声源之一，本文通过对昆明东站铁路降噪工程的观测试验项目，探讨了减速顶噪声的特性及治理措施。     

A stability classification method and its application to pipelined solution of linear recurrences

A new method of classification for numerical stability of parallel algorithms is proposed based on the theoretical foundation of forward error analysis. It partitions the algorithms according to their asymptotic stability—a measure introduced to relate the limiting behavior of the stability to the size of the problem. Using this method, the stability aspect of the pipelined solution technique for first-order and second-order linear recurrences—the core of a tridiagonal linear equation solver—is studied. In particular, it shows that the pipelined solution method of the first-order linear recurrences has the same degree of stability as the commonly used sequential evaluation algorithms. The stability problems of sequential and pipelined solution methods of the second-order linear recurrences are also studied.     

航天产品多源可靠性信息的收集与整理--建立可靠性信息数据库

航天产品可靠性信息的来源多样，可靠性信息的时间性、随机性、有价性、时效性和可追溯性等特征，决定了可靠性数据收集应满足真实性、连续性、完整性等基本要求。同时，对多源信息的收集应遵循先进行需求分析，再确定数据收集点并制定数据收集表格的程度进行。给出了信息的适用性准则和信息分类的三维模型。最后结合具体型号航天产品的可靠性工作的要求，给出了可靠性信息数据库系统的整体方案设计。     

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。     

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。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.