Students’ views on technology and writing: The power of personal history

As scholars, writers, and teachers, I believe that we should try harder to understand students’ perspectives on the use of computers in their academic work. This article begins to provide a sense of students’ perspectives on questions of technology, thus presenting a fuller picture of the context within which we teach. Drawing on a variety of methods, including a survey and the writings of a small group of students enrolled in a Writing and Technology course, this article expresses some of these stories generally hidden from an instructor's perspective and reveals that, despite what the media might tell us, students are not as prepared to utilize technology as we might assume. Furthermore, the student narratives suggest that English departments and writing programs can play an important role in assisting students who are unfamiliar with computer technologies, helping them to gain the computer literacy they need to succeed at the university.     

方差分量模型中回归系数的线性Bayes估计

本文研究了方差分量模型中在平方和损失函数和矩阵损失函数下回归系数的线性Ｂａｙｅｓ估计。     

Deep particle image velocimetry supervised learning under light conditions

Particle image velocimetry (PIV) is an important fluid visualization technology which extracts the velocity field from two successive particle images. Recently, some researchers have begun to use convolutional neural network (CNN) to tackle the PIV problem successfully. Some supervised learning methods make use of the PIV dataset with ground truth for network training. However, the existing dataset is composed of pairs of particle images under ideal light conditions and does not take into account the changes in actual experimental conditions. In this paper, we firstly generated a new and more challenging dataset called Light-PIV which fully simulates the change of the brightness of particle images in the real PIV experiment. Secondly, we present here a novel approach for fluid motion estimation which is based on an optical flow network LiteFlowNet. The proposed approach is verified by the application to a diversity of synthetic and experimental PIV images. We not only improve the structure, but also combine the traditional prior assumptions knowledge with the loss function to better guide the network training. The proposed approach is verified by the application to a diversity of synthetic and experimental PIV images. The experimental results show that our proposed method has advantages of high accuracy, obtaining detailed information and strong robustness in our PIV dataset compared with classical PIV methods such as HS optical flow and WIDIM, and even outperforms these existing approaches in some flow cases.     

不确定先验信息综合方法的研究

贝叶斯方法是可靠性分析和可靠性预测的重要方法之一 ,贝叶斯方法估计的准确度取决于先验信息的准确度。本文对综合不确定先验信息的四种重要方法进行了介绍、分析 ,并对各自的应用场合及存在的问题做了说明     

基于深度学习的图像去模糊方法研究进展

近年来,随着便携、轻巧的数码成像设备的日益普及,人们获取图像的手段日益方便与灵活,数字图像在视频监控、医疗诊断、太空探测等领域起到了重要的作用.然而,在现有的成像过程中存在诸多问题,如相机的感光单元质量差、摄影者专业水平低、拍摄环境恶劣等,往往导致最终得到的图像含有明显的模糊以及噪声.如何使计算机自动地从模糊图像中把清...     

捷联惯组验前分布随机加权分散融合估计

在小子样理论中,验前分布的获取与表示是一个关键问题.获取验前分布必须利用各种验前信息,而这些信息是在不同实验条件下得到的,针对同一对象可能获取多种验前信息,如何合理地利用这些验前信息给出验前分布,这样就出现了多源验前信息融合的问题.针对捷联惯性测量组合(捷联惯组)历次测试数据验前分布的获取问题,讨论了在异总体情况下,多种验前信息的融合问题.提出了通过随机加权分散融合法来实现多种异源信息融合,获取捷联惯组历次测试数据验前分布,从而减小了小样本情况下的统计分析误差.通过实例检验取得了良好的效果.     

浅谈硫酸二系统净化工段的技术改造

本文介绍了长沙锌厂硫酸二系统净化工段的技术改造情况     

基于先验阈值面的夜空云图分割算法

夜空背景易受到大气污染、月光及黄道带光等影响,导致云图变化较大。传统阈值法仅利用像素点灰度值及相应的邻域信息,难以准确分割背景变化较大且灰度不均匀的云图。文中对大量云图统计分析,发现夜空云图的两个先验特征,提出一种基于先验阈值面的云分割算法。首先根据先验特征分割出可信的背景区域,再根据背景区域进行多项式拟合得到一个随图像区域信息变化的阈值面,阈值面上的各个值介于目标和背景之间,从而可将云从背景分割出来。实验证明,相对于传统的单阈值算法,本文算法分割精确度更高,应用范围更广,尤其对受干扰的云图有更好的分割效果。     

房屋震害预测Bayes方法的改进

Bayes逐步判别法是经济预测中常用的方法。该方法有两个基本假设:母体正态分布假设和各母体协方差矩阵基本相同假设,因此该方法在使用时有很大的局限性。本文从马氏距离的概念出发,以各母体先验概率为权,改变各统计样本的重要性,得出了在母体任意分布情况下,与Bayes逐步判别法相类似的表达式。并且证明了各母体协方差矩阵有较大不同时,判别结果的变化也是很小的。通过用本文方法对乌鲁木齐市、海城镇、营口市、唐山市及东川市的震害进行判别,证明是可行的。     

Spatially constrained ICA algorithm with an application in EEG processing

Independent Component Analysis (ICA) aims at blindly decomposing a linear mixture of independent sources. It has lots of applications in diverse research areas. In some applications, there is prior knowledge on the sources and/or the mixing vectors. This prior knowledge can be incorporated in the computation of the independent sources. In this paper we provide an algorithm for so-called spatially constrained ICA (scICA). The algorithm deals with the situation when one mixing vector is exactly known. Also the generalization to more mixing vectors is discussed. Numerical experiments are reported that allow us to assess the improvement in accuracy that can be achieved with these algorithms compared to fully blind ICA and to a previously proposed constrained algorithm. We illustrate the approach with a biomedical application.