疯狂 软件包go-craq实现了CRAQ（带有分摊查询的链式复制），如。 麻省理工学院许可。 CRAQ是一种复制协议，它允许从任何副本进行读取，同时仍保持强一致性。 CRAQ应该提供比Raft和Paxos更好的读取吞吐量。 读取性能随添加到系统中的节点数量线性增长。 与Raft和Paxos相比，网络颤动明显更低。 了解有关CRAQ的更多信息 +------------------+ | | +-----+ Coordinator | | | | Write | +------------------+ | v +---+----+ +--------+ +--------+

易数据 EasyData是一个轻量级的数据库库，用于在python中处理复杂的图形数据。 资料库 I.模式 数据库是字典，具有附加的结构。 数据库由模式，对象和属性组成，它们都在符号上相关。 模式定义标签和一组属性，并用于创建新对象。 我们可以定义一个模式来表示空间中的位置： db = Database() db.create_schema('point', ['x', 'y']) 对可以分配给架构属性的可接受值进行一些控制非常有用。 约束是针对给定架构的特定属性分配的命题函数。 我们可以在上面定义的点模式的x和y属性上定义约束。 这些约束确保分配给x或y的任何值都是非负整数。

嵌入式控制系统的很经典的一本书，作者是UC Berkeley的Edward Ashford Lee。也是最早提出CPS的一批人之一。此为英文版的第一版。

INTRODUCTION TO EMBEDDED SYSTEMS A CYBER-PHYSICAL SYSTEMS APPROACH Second Edition

微波辐射计系统入门经典书目，系统的讲解了辐射计的结构、种类以及各种评价参数。

文件是DJVU格式 Unix Systems for Modern Architectures: Symmetric Multiprocessing and Caching for Kernel Programmers 中文版 英文版我找不到

Systems Analysis and Design in a Changing World 7th Edition by John W. SatzingerSystems Analysis and Design in a Changing World 7th Edition by John W. SatzingerSystems Analysis and Design in a Changing World 7th Edition by John W. Satzinger

共三部分：清晰非扫描电子原版,Author(s): Richard C Dorf, Robert H. Bishop

动漫推荐系统| Python，惊喜，Jupyter 该项目的目标是开发基于协作的动漫推荐器系统，该系统能够基于数据库信息（包括总用户历史记录和评级ID用户反馈）生成个性化的独特和相关动漫推荐列表，数据来源来自Kaggle。 com 。 有两个关联的数据集，评级数据集和动漫数据集。 评分数据集包含来自7,516个用户的7,813,737个评分（评分等级：1-10），涉及12294种动漫，密度为0.92％； 动漫数据集包含有关每个动漫的信息，共有7列（anime_id，名称，类型，类型，剧集，评分和成员）。 我将python和SUPRISE软件包一起使用，并利用定制的内置数据清理和模型评估程序，研究了各种协作过滤（CF）算法，包括基于项目的KNNWithMeans，SVD，共聚和SVDpp。 SVD在基于nDCG（排名准确性指标）为用户推荐相关动漫的排名列表方面表现最好，而又不牺牲太多速

Recent developments in laser scanning technologies have provided innovative solutions for acquiring three-dimensional (3D) point clouds about road corridors and its environments. Unlike traditional field surveying, satellite imagery, and aerial photography, laser scanning systems offer unique solutions for collecting dense point clouds with millimeter accuracy and in a reasonable time. The data acquired by laser scanning systems empower modeling road geometry and delineating road design parameters such as slope, superelevation, and vertical and horizontal alignments. These geometric parameters have several geospatial applications such as road safety management. The purpose of this book is to promote the core understanding of suitable geospatial tools and techniques for modeling of road traffic accidents by the state-of-the-art artificial intelligence (AI) approaches such as neural networks (NNs) and deep learning (DL) using traffic information and road geometry delineated from laser scanning data. Data collection and management in databases play a major role in modeling and developing predictive tools. Therefore, the first two chapters of this book introduce laser scanning technology with creative explanation and graphical illustrations and review the recent methods of extracting geometric road parameters. The third and fourth chapters present an optimization of support vector machine and ensemble tree methods as well as novel hierarchical object-based methods for extracting road geometry from laser scanning point clouds. Information about historical traffic accidents and their circumstances, traffic (volume, type of vehicles), road features (grade, superelevation, curve radius, lane width, speed limit, etc.) pertains to what is observed to exist on road segments or road intersections. Soft computing models such as neural networks are advanced modeling methods that can be related to traffic and road features to the historical accidents and generates regression equations that can be used in various phases of road safety management cycle. The regression equations produced by NN can identify unsafe road segments, estimate how much safety has changed following a change in design, and quantify the effects of road geometric features and traffic information on road safety. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.