机器学习技术在数据挖掘中的商业应用

进入人工智能时代,机器学习技术在数据挖掘工作中已经得到了较为广泛的应用,医疗、市场营销、金融、网络分析、电信等领域普遍引入这一技术展开数据挖掘工作,极大地提升了数据挖掘的效率和质量。根据环境的不同,实践之中,也需要选用不同的机器学习技术展开数据挖掘。本文主要探讨机器学习技术在数据挖掘中的商业应用,旨在提供具有一定借鉴意义的参考。     

在体生物光学成像技术的研究进展

在体生物发光成像和在体荧光成像是近年来新兴的在体生物光学成像技术, 能够无损实时动态监测被标记细胞在活体小动物体内的活动及反应, 在肿瘤检测、基因表达、蛋白质分子检测、药物受体定位、药物筛选和药物疗效评价等方面具有很大的应用潜力. 本文详细介绍了在体生物发光成像和在体荧光成像的特点、系统及应用, 比较了它们的异同, 综述了在体生物光学成像技术的基本原理和应用领域, 讨论了将其应用于临床的进一步发展方向.     

比差ICA去噪方法在光学功能成像中的应用

在光学功能成像中,极低信噪比会使得样本协方差矩阵具有奇异性,因此导致Emir等人提出的ICA去噪方法在白化预处理过程会出现降维现象,最终使得该方法无法检测出信号。为解决一问题,利用原ICA去噪方法得到的噪声信号与观测信号之间的差异特性,提出了一种比差ICA去噪方法,该方法在信噪比-40 dB情况下能成功检测出信号。利用仿真得到的光学功能成像数据,对比分析了比差ICA去噪方法与传统滤波去噪方法在极低信噪比下的检测性能,结果表明比差ICA去噪方法不仅检测性能明显优于滤波去噪方法,且输出信噪比基本不随输入信噪比的下降而下降。     

ROC分析技术在机器学习中的应用

ROC(受试者工作特征)分析技术是一种用来衡量分类算法和图示它们性能的技术。与传统的正确率相比,ROC分析更能够全面地描述分类算法的分类性能。该方法具有可信度高,描述客观精确,特别是不受数据环境影响等优势。对国内外这一方法的研究成果进行了较为系统地介绍,详细分析了它的优缺点,最后对这一技术的发展进行了展望。     

机器学习在卫星遥测分析建模中的应用

卫星在研制和应用的各个环节产生了庞大的特征和相关性复杂的遥测数据,人工判读或使用专家策略进行分析均无法充分梳理出这些数据的相关性并进一步挖掘和应用它们的价值;利用Python强大的机器学习生态环境和算法,能够实现对卫星遥测数据的分析和建模;以星载铷钟遥测数据为样本,提出了对卫星遥测数据进行算法分析和建立模型的方法,为实现对铷钟的健康状态进行实时评估、预报、精准控制奠定了基础;除遥测数据外,该方法亦可用于分析卫星其它数据,为卫星研制和应用过程中利用机器学习技术提高智化能水平提供了参考。     

在线机器学习在智能钻井中的应用

随着国家对互联网、人工智能、机器学习等前沿计算机技术的推广,以及计算机技术所带来的高效能力,提供的先进决策方法,为智能钻井提供了新的解决方案.相较传统的离线学习的方法,在线机器学习在分析钻井过程中具有更强的适应性、更好的解析能力和纠错能力.本文简述了在线学习在钻井工程的应用.     

优化建模技术和机器学习理论的新发展

化工，炼油，冶金等制造业产生过程，新产品研制，以及经营管理的优化能给企业带来巨大的经济效益。优化成功的前提是需要建立能预报优化控制条件的数学模型。用机器学习技术从已有的数据中抽提出有用信息，是建立有效数学模型的关键。本文回顾了优化建模技术及其理论基础的几个发展阶段，指出从线性建模到非线性建模，从追求经验风险极小化到追求实际风险极小化，从采用单一算法到建立多种算法相结合的信息处理平台，从单纯根据古典统计数学到参照新发展的统计学习理论，使优化建模技术由粗到精，由低级到高级，在生产过程，新产品研制和经营管理的优化中发挥更大作用。     

机器学习在RoboCup中的应用研究

RoboCup is a particularly good domain for studying multi-agent systems.A wide variety of MAS issues can be studied in robotic soccer,in which the theory,algorithm and architecture of agent system can be evaluated.Because of the inherent complexity of MAS,there are many interests in using machine learning techniques to handle it.This paper investigates and discusses the machine-learning techniques used in RoboCup.The background is firstly presented and the application of machine learning in RoboCup is lately demonstrated with some top simulation teams.The machine-learning system in NDSocTeam is also introduced.Finally some open issues in this field are pointed out.     

Monte Carlo方法在扩散光学成像仿真中的应用

综述了描述前向问题的各种模型,包括解析解方法、数值方法和统计方法.特别地,就生物自发光多谱段光源的实例介绍了Monte Carlo方法.在光学成像领域,针对不同的成像模态、对成像质量的要求以及所需要的信息,MC方法有3种主要形式:连续波、时域和频域.不仅揭示了每种形式的基本原理,同时也相应地介绍了其在本领域的典型应用及软件.通过这些应用可以看出,MC方法对于扩散光学成像,特别是最近几年的在体无创实时成像的发展发挥着重要作用.     

光学遥感影像成像模拟研究综述

光学遥感影像成像模拟技术在航天器设计、卫星参数选择等方面有着积极的使用价值,可以极大地促进遥感事业的发展.本文从图像合成模拟模型、遥感物理模拟模型以及虚拟现实模拟模型对光学遥感影像成像模拟技术进行了介绍,对模拟过程中的主要影响因素、仍然存在的问题进行了分析,最后总结了光学遥感模拟技术的应用.     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic,data envelopment analysis and machine learning

The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three-stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta-heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.     

基于机器学习的流量识别技术综述与展望

流量识别是实现网络管理与网络安全的关键环节。随着基于端口号和深度包检测两种流量识别方法相继失效,基于机器学习的流量识别技术成为近十年流量识别领域最受关注的方法。鉴于流量识别技术的重要性,首先介绍流量识别技术的概况及相关基本概念,包括其应用场景、输入对象、识别类型及评价指标。然后详述机器学习背景下,流量识别过程中的数据集获取、特征提取与选择、识别模型设计等关键技术的进展,并对近年主要研究工作进行总结和比较。最后对基于机器学习的流量识别技术面临的主要挑战及未来的发展方向进行探讨与展望。     

Social media research: The application of supervised machine learning in organizational communication research.

Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary data. Based on a case study of Dutch employees’ work-related tweets, this paper compares the coding performance of three classifiers, Linear Support Vector Machine, Naïve Bayes, and logistic regression. The performance of these classifiers is assessed by examining accuracy, precision, recall, the area under the precision-recall curve, and Krippendorf’s Alpha. These indices are obtained by comparing the coding decisions of the classifier to manual coding decisions. The findings indicate that the Linear Support Vector Machine and Naïve Bayes classifiers outperform the logistic regression classifier. This study also compared the performance of these classifiers based on stratified random samples and random samples of training data. The findings indicate that in smaller training sets stratified random training samples perform better than random training samples, in large training sets (n = 4000) random samples yield better results. Finally, the Linear Support Vector Machine classifier was trained with 4000 tweets and subsequently used to categorize 578,581 tweets obtained from 430 employees.     

面向机器学习的分布式并行计算关键技术及应用

当前机器学习等算法的计算、迭代过程日趋复杂, 充足的算力是保障人工智能应用落地效果的关键。本文首先提出一种适应倾斜数据的分布式异构环境下的任务时空调度算法,有效提升机器学习模型训练等任务的平均效率;其次,提出分布式异构环境下高效的资源管理系统与节能调度算法,实现分布式异构环境下基于动态预测的跨域计算资源迁移及电压/频率的动态调节,节省了系统的整体能耗;然后构建了适应于机器学习/深度学习算法迭代的分布式异构优化环境,提出了面向机器学习/图迭代算法的分布式并行优化基本方法。最后,本文研发了面向领域应用的智能分析系统,并在制造、交通、教育、医疗等领域推广应用,解决了在高效数据采集、存储、清洗、融合与智能分析等过程中普遍存在的性能瓶颈问题。     

改进粒子群优化的极限学习机软测量建模方法

工业过程常含有显著的非线性、时变等复杂特性,传统的极限学习机有时无法充分利用数据信息,所建软测量模型预测性能较差。为了提高极限学习机的泛化能力和预测精度,提出一种改进粒子群优化的极限学习机软测量建模方法。首先,利用高斯函数正态分布的特点实现惯性权重的自适应更新,并线性变化学习因子以提高粒子群优化算法的收敛速度和搜索性能;然后将该算法用于优化极限学习机的惩罚系数和核宽,得到一组最优超参数;最后将该方法应用于脱丁烷塔过程软测量建模中。仿真结果表明,优化后的极限学习机模型预测精度有明显的提高,验证了所提方法不仅是可行的,而且具有良好的预测精度和泛化性能。     

A high optical efficiency 3D/2D convertible integral imaging display

We propose a high optical efficiency three‐dimensional (3D)/two‐dimensional (2D) convertible integral imaging display by using a pinhole array on a reflective polarizer. The 3D mode is realized by adopting a pinhole array on a reflective polarizer to generate a point light source array. Three‐dimensional/2D convertible feature is realized by electrically controlling a polarization switcher. The reflective polarizer can reflect the light that has the orthogonal polarization direction with the reflective polarizer and transmit the light that has the same polarization direction with the reflective polarizer. The reflected light is recycled, so the optical efficiencies for both 3D and 2D modes are enhanced. In the practical experiments, the optical efficiencies of the proposed integral imaging display increase by 8.04 times and 1.65 times in 3D and 2D modes comparing with the conventional integral imaging display that has no light recycle, respectively.     

Structured machine learning: the next ten years

The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.     

基于相关向量机的机器学习算法的研究与应用

机器学习的重要理论之一是统计学。在传统的统计学理论中,只有在样本数量足够多的情况下才能取得近乎理想的成果。文章主要提出了一种新型机器学习方法——相关向量机。与传统机器学习项目相比,相关向量机具有较多的优势,包含概率型输出的实现、核函数选择更加自由等,能够显著提升机器学习算法的科学性和合理性。     

大数据下的典型机器学习平台综述

由于大数据海量、复杂多样、变化快,传统的机器学习平台已不再适用,因此,设计一个高效的、通用的大数据机器学习平台成为目前的研究热点。通过介绍和分析机器学习算法的特点以及大规模机器学习的数据和模型并行化,引出常见的并行计算模型。简单介绍了整体同步并行模型（BSP）、SSP并行计算模型以及BSP、SSP模型与AP模型的区别,主要介绍了基于这些并行模型的典型的机器学习平台和这些平台的优缺点,并指出各个平台最适合处理何种大数据问题。最后从采用的抽象数据结构、并行计算模型、容错机制等方面对典型的机器学习平台进行了总结,并提出一些建议和展望。