Data-Driven Prediction for Industrial Processes and Their Applications (Information Fusion and Data Science) By 作者: Jun Zhao – Wei Wang – Chunyang Sheng ISBN-10 书号: 3319940503 ISBN-13 书号: 9783319940502 Edition 版本: 1st ed. 2018 Release Finelybook 出版日期: 2018-08-20 pages 页数: (443） Springer出版超清 This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

信息安全_数据安全_Protect privacy in a data driven 数据分析 安全分析 漏洞分析 应急响应 应急响应

Enhance Security Awareness with Data Mining Data-driven Intrusion Detection Solutions in Alibaba Cloud

matlab三次样条插值函数代码分析仪器信号的数据驱动和粗略到精细的基线校正 您可以使用此程序来实现对来自各种分析仪器（包括但不限于质谱仪，离子迁移谱仪和色谱仪）的信号进行基线自适应校正。 该算法通过自适应定位和去除高振幅频谱峰值，克服了经验模式分解算法的模式混合问题。 通过定性和定量分析，与传统的最小二乘拟合和稀疏表示相比，基于经验模态分解（DD-CF）的算法在处理时间和基线拟合效果上具有更好的优势。 同时，该算法的最大特点是无需用户干预即可实现质谱仪，色谱仪和离子迁移谱的数据驱动基线校正。 与传统算法相比，DD-CF算法具有更强的自适应能力。 有关该算法的详细信息，请参阅此算法的原始参考。（DOI：10.1016 / j.aca.2021.338386） 算法介绍 图1：DD-CF流程图 步骤1： 多项式拟合方法获得粗略基线 第2步： 三次样条插值以消除光谱峰 第三步： EMD并分离基线主导的IMF 第四步： 信号重建 如何使用 代码文件夹包含两个MATLAB脚本文件： DD_CF_v1.m是DD-CF算法的功能脚本； testCode.m是一个测试程序，该程序调用DD_CF_v1

Building Standalone Data-Driven Applications using SQL Server Compact 4.0

data driven model

茄子：Clojure的行为驱动开发（BDD）库。 简单是关键

D3 的全称是（Data-Driven Documents），顾名思义可以知道是一个被数据驱动的文档。听名字有点抽象，说简单一点，其实就是一个 JavaScript 的函数库，使用它主要是用来做数据可视化的。

Digital technology has not only revolutionized the way designers work, but also the kinds of designs they produce. The development of the computer as a design environment has encouraged a new breed of digital designer; keen to explore the unique creative potential of the computer as an input/output device. Data-driven Graphic Design introduces the creative potential of computational data and how it can be used to inform and create everything from typography, print and moving graphics to interactive design and physical installations. Using code as a creative environment allows designers to step outside the boundaries of commercial software tools, and create a set of unique, digitally informed pieces of work. The use of code offers a new way of thinking about and creating design for the digital environment. Each chapter outlines key concepts and techniques, before exploring a range of innovative projects through case studies and interviews with the artists and designers who created them. These provide an inspirational, real-world context for every technique. Finally each chapter concludes with a Code section, guiding you through the process of experimenting with each technique yourself (with sample projects and code examples using the popular Processing language supplied online to get you started).

资源宝贵，请及时下载 Book Description The Selenium WebDriver 3.x Technology is an open source API available to test both Browser and Mobile applications. It is completely platform independent in that tests built for one browser or mobile device, will also work on all other browsers and mobile devices. Selenium supports all major development languages which allow it to be tied directly into the technology used to develop the applications. This guide will provide a step-by-step approach to designing and building a data-driven test framework using Selenium WebDriver, Java, and TestNG. The book starts off by introducing users to the Selenium Page Object Design Patterns and D.R.Y Approaches to Software Development. In doing so, it covers designing and building a Selenium WebDriver framework that supports both Browser and Mobile Devices. It will lead the user through a journey of architecting their own framework with a scalable driver class, Java utility classes, JSON Data Provider, Data-Driven Test Classes, and support for third party tools and plugins. Users will learn how to design and build a Selenium Grid from scratch to allow the framework to scale and support different browsers, mobile devices, versions, and platforms, and how they can leverage third party grids in the Cloud like SauceLabs. Other topics covered include designing abstract base and sub-classes, inheritance, dual-driver support, parallel testing, testing multi-branded applications, best practices for using locators, and data encapsulation. Finally, you will be presented with a sample fully-functional framework to get them up and running with the Selenium WebDriver for browser testing. By the end of the book, you will be able to design your own automation testing framework and perform data-driven testing with Selenium WebDriver. Contents 1: BUILDING A SCALABLE SELENIUM TEST DRIVER CLASS FOR WEB AND MOBILE APPLICATIONS 2: SELENIUM FRAMEWORK UTILITY CLASSES 3: BEST PRACTICES FOR BUILDING SELENIUM PAGE OBJECT CLAS