Alvor

Alvor是一款Eclipse插件,能在编译阶段检查Java程序中嵌入的SQL语句,避免运行时出现错误。它通过定位SQL字符串、分析表达式并验证SQL语法来确保代码质量。
In Java programs SQL statements are usually embedded in string literals. As they are just strings for compiler, SQL mistakes pop up only at runtime. Furthermore, if you have used conditionals for constructing the query, it's possible that a buggy branch is executed first time at your client's site. Alvor is an Eclipse JDT plug-in that checks embedded SQL statements at compile-time. It can be invoked explicitly for whole-program analysis or it can be configured to run incrementally as you write code (each time file is saved). Alvor's work process has 3 main steps: 1) Find expressions in Java code that should evaluate to correct SQL statements. For this, the user configures set of method names and argument positions (eg. java.sql.Connection#prepareStatement, argument 1) and Alvor locates respective method calls and argument expressions. 2) Perform string analysis on those argument expressions to find their possible values. Besides simple string literals, it can handle conditional assignments to variables and cases where parts of string come from method parameters or from methods returning strings. Possible values for each expression are represented as a regular expression. 3) Validate found strings either by testing all possible cases against actual test database (using Connection.prepareStatement) or by performing abstract parsing directly on the regular expressions constructed in previous step. Any errors found are presented via Eclipse error markers. Although Alvor is currently in beta, it has proven itself by finding 8 real SQL bugs in selected parts of Compiere ERP system (300 KLOC). For medium-sized projects the whole-program analysis takes 5-20 seconds. Incremental analysis usually completes in less than 0.5 seconds. Alvor is an open-source project. For more information and installation instructions see http://alvor.googlecode.com/
内容概要:本文介绍了基于Koopman算子理论的模型预测控制(MPC)方法,用于非线性受控动力系统的状态估计与预测。通过将非线性系统近似为线性系统,利用数据驱动的方式构建Koopman观测器,实现对系统动态行为的有效建模与预测,并结合Matlab代码实现具体仿真案例,展示了该方法在处理复杂非线性系统中的可行性与优势。文中强调了状态估计在控制系统中的关键作用,特别是面对不确定性因素时,Koopman-MPC框架能够提供更为精确的预测性能。; 适合人群:具备一定控制理论基础和Matlab编程能力的研【状态估计】非线性受控动力系统的线性预测器——Koopman模型预测MPC(Matlab代码实现)究生、科研人员及从事自动化、电气工程、机械电子等相关领域的工程师;熟悉非线性系统建模与控制、对先进控制算法如MPC、状态估计感兴趣的技术人员。; 使用场景及目标:①应用于非线性系统的建模与预测控制设计,如机器人、航空航天、能源系统等领域;②用于提升含不确定性因素的动力系统状态估计精度;③为研究数据驱动型控制方法提供可复现的Matlab实现方案,促进理论与实际结合。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注Koopman算子的构造、观测器设计及MPC优化求解部分,同时可参考文中提及的其他相关技术(如卡尔曼滤波、深度学习等)进行横向对比研究,以深化对该方法优势与局限性的认识。
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