大学物理实验数据分析与处理软件的研究与实现

本文针对开发计算机数据分析与处理软件来提高实验效率,保证实验教学的质量,介绍了软件的主要功能以及软件开发中遇到的一些问题和解决办法     

A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.     

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.     

SigmaPlot软件在化学实验数据处理中的应用

SigmaPlot是功能强大的数据分析和科学绘图软件,通过实例,初步介绍了SigmaPlot处理实验数据的方法和结果。结果表明,用SigmaPlot对实验数据作处理,解决了化学实验中数据多、处理麻烦、手工作图误差大等问题,使处理过程简单、快捷,提高了效率和准确性。     

天文观测相机低噪声CCD视频处理电路的分析与设计

在讨论天文观测相机低噪声视频处理电路的基础上,对前置放大、差分放大、可编程增益放大、相关双采样及16bit模数转换等关键组成部分进行了分析,给出各组成部分的设计方法。对所设计的低噪声视频处理电路进行了PSPICE仿真和实验测试,仿真和实验结果表明,该视频处理电路在本身引入噪声较小的同时,有效地抑制了CCD的复位噪声和1/f等低频噪声,使天文观测相机的整机噪声<15e-,达到了指标要求。     

高精度光电数据采集系统的实验研究

针对光电弱信号检测,采用Σ-ΔAD转换器AD7706和AVR单片机ATmega8515设计了适用于低频量测量的高精度数据采集器,采用Σ-ΔAD转换器ADC16471、AVR单片机ATmega8515、C51单片机C8051F020和以太网控制器RTL8019AS设计了适用于高精度线阵CCD的数据采集器,并分别对两套数据采集器进行了实验研究与性能分析。     

网格计算及其在图像处理中的应用

介绍网格计算的主要技术特点和优势,并探讨网格计算在图像处理中的应用.网格的并行计算能力有效的提高了超分辨率图像处理算法的计算效率,加快算法的运行时间.     

三层结构在医保数据传输中的应用

用powerbuilder 开发工具的分布式对象实现三层结构式的医保数据传输.三层结构式的医保数据传输是在医保数据库服务器与公网之间加上1台数据传输应用服务,这台数据传输应用服务器上安装数据传输服务端程序,向公网开放数据传输服务端口,可以访问互联网外,其它的服务端口全部关闭;定点医疗机构的客户端通过互联网访问应用服务器上的数据传输服务端,医保数据库服务器设置为不能访问互联网,也不能被互联网访问.应用三层结构式可以安全地传输医保数据.     

一种新的分类评测方法

分类算法的现有评价指标存在一些问题,主要是评测数值在不同的数据集上呈现剧烈波动。为解决这一问题,通过考察数据集对分类结果的影响,提出了一种对分类算法的新评测指标new-macro-F1。这一新评测指标将数据集的因素从评测过程中独立了出来,使得new-macro-F1表示的仅仅是分类算法本身。实验结果表明使用新评测指标,同一分类器在不同数据集上的表现波动较小;通过分类器在一个数据集上的表现,可以近似的估计该分类器在另一数据集上的分类质量。     

基于文本库的完全加权词间关联规则挖掘算法

提出了基于文本数据库的完全加权词间关联规则挖掘算法,给出了与其相关的定理及其证明过程。该算法采用三种剪枝策略,候选项集数量和挖掘时间明显减少,提高了挖掘效率。实验结果表明该算法的有效性,和现有算法比较,挖掘效率确实得到改善和提高。