gef is distributed under the MIT License (MIT)3333

gef is distributed under the MIT License (MIT)

Writing this book has been like discovering RabbitMQ itself—encountering a prob- lem that needed solving, but not knowing what the solution looked like. Until May 2010, we didn’t even know each other. We both had been active in the RabbitMQ com- munity for the past two years, but we’d never actually bumped into each other. Then one day a conversation with Alexis Richardson (Rabbit’s CEO at the time) introduced Alvaro and me to each other, and made what you hold in your hands possible. What we had in common was a desire to write down in a single place all the knowledge we had acquired about RabbitMQ the hard way. Back in 2010, that knowledge was (and today still largely is) scattered across the internet in a smattering of blog articles and terse technical tutorials. In other words, we both wanted to write the book we wished had existed when we started with RabbitMQ two years earlier. Neither of us came from a traditional messaging background, which made us fast friends and has largely informed the tone of RabbitMQ in Action; we wanted this book to be accessible for folks who’ve never heard of a queue or a binding before. In fact, when each of us discovered RabbitMQ, we didn’t even know what “messaging” was or that it was the solution to the problems we were having. My (Jason’s) situation was that my company needed a way to take the spam reportings we received from our custom- ers and process them out-of-band from our main stream of incoming messages. In Alvaro’s case, his company had a social network whose member communication sys- tem was creaking under the load of a 200 GB database. Like so many others who’ve come to messaging, both us had first tried to solve our queue-centric issues using data- base tables. Problems, like ensuring that only one application instance consumed any particular queue item, plagued our attempts at a database-driven solution and sent us looking for a better way. After all, we knew we couldn’t be the first people in the his- tory of software to have these issues. The solution for both of us came in a surprisingly similar way: a friend at Plaxo told me to check out this “RabbitMQ thing” as a way to solve my queue-centric problems, and an Erlang colleague of Alvaro’s in China gave him the same advice. Halfway around the world, both of us discovered RabbitMQ in the same way, and in response to trying to solve almost exactly the same problem! In fact, since you’re reading this book about RabbitMQ, it’s likely that similar challenges have led you to discover Rab- bitMQ in the same way. That speaks to the fact of why RabbitMQ is so popular: it eas- ily solves the basic problems of distributing data that each of us runs into again and again when trying to scale the software that we build. Our hope is that RabbitMQ in Action will help you design solutions to those chal- lenges more quickly and easily with RabbitMQ, so you can spend more time writing the software that will change the world and less time getting up to speed on the mes- saging broker that will help you do it. Perhaps, along the way, RabbitMQ will intro- duce you to an awesome coauthor who will become the lifelong friend you never expected. 1 This book is a product of how much we love writing software, and our hope is that it will help you do the same in ways you never thought possible. A LVARO V IDELA D ÜBENDORF , S WITZERLAND J ASON J. W. W ILLIAMS B OISE , I DAHO , U NITED S TATES

Numerous control and decision problems in networked systems can be posed as optimization problems. Examples include the framework of network utility maxi-mization for resource allocation in communication networks, multi-agent coordina-tion in robotics, and collaborative estimation in wireless sensor networks (WSNs). In contrast to classical distributed optimization, which focuses on improving compu-tational efficiency and scalability, these new applications require simple mechanisms that can operate under limited communication. In this thesis, we develop several novel mechanisms for distributed optimization under communication constraints, and apply these to several challenging engineering problems

Minghui Zhu和Sonia Martínez关于多智能体系统分布式优化方面的经典教材。

As the main building block of the smart grid systems, microgrid (MG) integrates a number of local distributed generation units, energy storage systems, and local loads to form a small-scale, low- and medium-voltage level power system. In gen- eral, an MG can operate in two modes, i.e., the grid-connected and islanded mode. Recently, in order to standardize its operation and functionality, hierarchical con- trol for islanded MG systems has been proposed. It divides the control structure into three layers, namely, primary, secondary, and tertiary control. The primary control is based on each local distributed generation (DG) controller and is realized in a de- centralized way. In the secondary layer, the frequency and voltage restoration control as well as the power quality enhancement is usually carried out. In the tertiary con- trol, economic dispatch and power flow optimization issues are considered. However, conventionally both the secondary and tertiary control are realized in a centralized way. There are certain drawbacks to such centralized control, such as high compu- tation and communication cost, poor fault tolerance ability, lack of plug-and-play properties, and so on. In order to overcome the above drawbacks, distributed control is proposed in the secondary control and tertiary optimization in this book. In the secondary control, restorations for both voltage and frequency in the droop- controlled inverter-based islanded MG are addressed. A distributed finite-time con- trol approach is used in the voltage restoration which enables the voltages at all the DGs to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed fre- quency control is proposed for frequency restoration, subject to certain control input constraints. The proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. Then the distributed control strategy is also employed in the secondary voltage unbalance compensation to replace the conventional centralized controller. The con- cept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each xvii xviii Distributed Control and Optimization Technologies in Smart Grid Systems local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. In the tertiary layer, a distributed economic dispatch (ED) strategy based on pro- jected gradient and finite-time average consensus algorithms is proposed. By de- composing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution for the optimization problem in a distributed manner with limited communication among neighbors. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Besides, two distributed multi-cluster optimization meth- ods are proposed for a large-scale multi-area power system. We first propose to divide all the generator agents into clusters (groups) and each cluster has a leader to com- municate with the leaders of its neighboring clusters. Then two different schemes are proposed for each agent to iteratively estimate a solution of the optimization prob- lem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. In addition, a novel hierarchical decentralized optimization architecture is proposed to solve the ED problem. Similar to distributed algorithms, each local generator only solves its own problem based on its own cost function and generation constraint. An extra co- ordinator agent is employed to coordinate all the local generator agents. Besides, it also takes the responsibility for handling the global demand supply constraint. In this way, different from existing distributed algorithms, the global demand supply con- straint and local generation constraints are handled separately, which would greatly reduce the computational complexity. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. A distributed optimal energy scheduling strategy is also proposed in the tertiary layer, which is based on a newly proposed pricing strategy named PD pricing. Con- ventional real-time pricing strategies only depend on the current total energy con- sumption. In contrast to this, our proposed pricing strategy also takes the incremen- tal energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. Two different dis- tributed optimization algorithms with different communication strategies are pro- posed to solve the problem.

The book is structured in two parts: principles and paradigms. The first chapter is a general introduction to the subject. Then come seven chapters on individual principles we consider most important: communication, processes, naming, synchronization, consistency and replication, fault tolerance, and security.

本文着重研究高阶非线性多智能体系统的自适应自适应模糊控制。 通信网络是具有固定拓扑的无向图。 每个代理由高阶积分器建模，该积分器具有未知的非线性动力学和未知的干扰。 在backstepping框架下，为每个代理设计了一个鲁棒的自适应模糊控制器，以使所有代理最终达成共识。 而且，从每个代理程序的控制器设计仅需要其自身及其邻居之间的相对状态信息的意义上说，这些控制器是分布式的。 一个四阶仿真实例证明了该算法的有效性。

一种基于ESPRIT的方法，用于大规模MIMO-sy系统中不连续分布的源的2D定位 这是关于“基于ESPRIT的方法在大规模MIMO系统中非相干分布源的二维定位”的论文和代码。 推荐引文：'A。 Hu，T。Lv，H。Gao等，“基于ESPRIT的方法，用于大规模MIMO系统中非相干分布源的二维定位”，IEEE J. Select。 主题信号处理，第一卷。 8号2014年10月，第5页，第996-1011页。”

Vert.x in Action书籍范例 :waving_hand: 欢迎！ 这些是由撰写并由编写的 （ISBN 9781617295621）的工作示例。 如何打开并运行示例？ 本书的读者应该直接从子文件夹打开项目：它们都是独立的。 您将找到每个项目的Gradle和Maven构建描述符，因此可以使用文本编辑器或集成开发环境（例如IntelliJ IDEA，Eclipse IDE或Microsoft Visual Studio Code）加载项目。 例如，如果要使用Gradle构建第1章，请打开终端并运行： $ cd chapter1 $ ./gradlew build 或使用Maven运行： $ cd chapter1 $ mvn package 本书示例在某些Unix环境下运行效果最佳：Linux，macOS或Microsoft提供Linux的Windows子系统。 存储库的结构是什么？ 可以使