Physically constrained data-driven correction for reduced-order modeling of fluid flows

We have recently proposed a data-driven correction reduced-order model (DDC-ROM) framework for the numerical simulation of fluid flows, which can be formally written as follows.

     

A Data-Driven Space-Time-Parameter Reduced-Order Model with Manifold Learning for Coupled Problems: Application to Deformable Capsules Flowing in Microchannels

An innovative data-driven model-order reduction technique is proposed to model dilute micrometric or nanometric suspensions of microcapsules, i.e., microdrops protected in a thin hyperelastic membrane, which are used in Healthcare as innovative drug vehicles. We consider a microcapsule flowing in a similar-size microfluidic channel and vary systematically the governing parameter, namely the capillary number, ratio of the viscous to elastic forces, and the confinement ratio, ratio of the capsule to tube size. The resulting space-time-parameter problem is solved using two global POD reduced bases, determined in the offline stage for the space and parameter variables, respectively. A suitable low-order spatial reduced basis is then computed in the online stage for any new parameter instance. The time evolution of the capsule dynamics is achieved by identifying the nonlinear low-order manifold of the reduced variables; for that, a point cloud of reduced data is computed and a diffuse approximation method is used. Numerical comparisons between the full-order fluid-structure interaction model and the reduced-order one confirm both accuracy and stability of the reduction technique over the whole admissible parameter domain. We believe that such an approach can be applied to a broad range of coupled problems especially involving quasistatic models of structural mechanics.     

联合监测过程位置参数和尺度参数的非参数Shewhart控制图

用于检测生产服务过程的传统控制图多数都假定过程的分布是已知的。这些控制困经常是在正态分布的假设下构建的,然而在服务质量实时监控中数据往往是非正态的。在这种情况下,基于正态分布假设的控制图的结果是不可靠的。为了解决这个问题,通常考虑非参数方法,因为在过程分布未知情况下,非参数控制图比参数图更加稳健有效。本文提出一个新的基于Van der Waerden和Klotz检验的Lepage型非参数Shewhart控制图(称为LPN图)用于同时检测未知连续过程分布的位置参数和尺度参数。文中给出了LPN图在不同参数下的控制限。依据运行长度分布的均值,方差和分位数,分析了LPN图在过程受控和失控时的性能,并与其他一些现有的非参数控制图进行比较。基于蒙特卡洛的模拟结果表明,LPN图对非正态分布具有很好的稳健性,并且在不同的过程分布下对检测位置参数和尺度参数,尤其对检测尺度参数的漂移都具有很好的性能。最后通过监控出租车服务质量说明LPN图在实际中的应用。     

Improved Battery Cycle Life Prediction Using a Hybrid Data-Driven Model Incorporating Linear Support Vector Regression and Gaussian

The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithium-ion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.     

基于Web的自动化测试框架的研究与实现

根据web系统测试的特点,为提高软件测试自动化脚本的可重用性,结合在实际项目中软件自动化测试的实践,提出基于Web的自动化测试框架(ACRAT)。分析了软件自动化测试框架的优点及局限性,阐述了5种基本的自动化测试框架,最后提出新的自动化测试框架。实践表明该框架具有降低脚本规模、提高测试效率、易于扩展等优越性。     

自私感知的数据驱动覆盖网络

为了解决数据驱动覆盖网络（DONet）中节点自私性问题,提出了一种自私感知的DONet,简称SA-DONet机制。SA-DONet赋于每个节点一个利他值,记录着该节点对其他节点的转发贡献;基于节点利他值设计的数据段请求和发送算法,保证利他值高的节点被优先服务。SA-DONet具有以下特点：首先,使用分布式方法来检测自私节点并动态的调整数据段请求和发送策略;其次,自私的评价（利他值）基于节点的历史数据,无需发送额外的探测和测量包;最后,SA-DONet保持着和DONet相当的计算复杂度。仿真结果表明,和DONet相比,即使存在一定比例的自私节点,SA-DONet机制也能提高应用层组播的总体性能,并且具有较低的控制负荷。     

网络综合布线管理软件设计和实现

综合布线管理软件用来管理网络布线中涉及到的设备、链路,同时提供一种可视化的管理方案,便于构造合适的网络,以及发现布线中可能存在的问题。阐述布线管理软件的发展历程、与网络管理软件的区别、以及目前已经有的产品和解决方案。同时针对布线管理软件中的关键问题:易扩充和可扩展性、链路查找准确和全面性、可视化管理提出了解决方法,实现一个利用数据驱动的可视化管理软件。最后,对布线管理软件的发展方向进行阐述,包括智能布线管理软件的出现和发展,以及与布线管理软件相关的国际标准。     

数据驱动二次相关滤波器红外目标检测

针对传统红外目标检测算法易受目标和背景先验样本质量、目标姿态和视角及噪声等的影响,提出了一种新的基于稀疏编码的数据驱动二次相关滤波器目标检测算法,其中给出了目标自相关矩阵基字典的概念,该数据驱动滤波器模型能包容多种姿态和视角的目标,并能抑制噪声和样本质量的影响,同时可以舍弃对无规律背景样本的依赖,通过对行人和车辆的实验验证了该算法的有效性.所提算法的设计思想对诸多滤波器算法的改进具有很好的借鉴意义.     

Sparse identification method of extracting hybrid energy harvesting system from observed data

Hybrid energy harvesters under external excitation have complex dynamical behavior and the superiority of promoting energy harvesting efficiency. Sometimes, it is difficult to model the governing equations of the hybrid energy harvesting system precisely, especially under external excitation. Accompanied with machine learning, data-driven methods play an important role in discovering the governing equations from massive datasets. Recently, there are many studies of data-driven models done in aspect of ordinary differential equations and stochastic differential equations (SDEs). However, few studies discover the governing equations for the hybrid energy harvesting system under harmonic excitation and Gaussian white noise (GWN). Thus, in this paper, a data-driven approach, with least square and sparse constraint, is devised to discover the governing equations of the systems from observed data. Firstly, the algorithm processing and pseudo code are given. Then, the effectiveness and accuracy of the method are verified by taking two examples with harmonic excitation and GWN, respectively. For harmonic excitation, all coefficients of the system can be simultaneously learned. For GWN, we approximate the drift term and diffusion term by using the Kramers-Moyal formulas, and separately learn the coefficients of the drift term and diffusion term. Cross-validation (CV) and mean-square error (MSE) are utilized to obtain the optimal number of iterations. Finally, the comparisons between true values and learned values are depicted to demonstrate that the approach is well utilized to obtain the governing equations for the hybrid energy harvester under harmonic excitation and GWN.     

第三方数据平台精准营销收费模式研究

第三方数据平台通过大数据分析技术为商家提供精准营销服务,本文研究了两个竞争商家通过同一个数据平台进行精准营销时,平台收费模式(费率佣金与按销量收费)对商家竞争以及平台与商家利润的影响。研究发现:1)费率佣金模式的绩效依赖于费率参数,在精准度高的情况下,它不能最大化平台与商家的利润。2)与费率佣金相比,按销量收费模式显著提高了商家的利润,并且平台与商家的利润都随费用参数的增加而增加。3)从收费模式选择的角度,精准度越高,按销量收费模式的优势越明显;费率佣金模式适用于垄断情况,而按销量收费模式更适用于竞争情况。4)费率佣金与按销量收费的结合可以实现平台与商家的共赢。研究结论为大数据驱动下的精准营销收费模式选择,以及“大数据平台”的盈利模式创新提供了理论参考。