GAE SDK 1.4.0 发布了!

Google App Engine SDK 1.4.0 版本更新包括 AlwaysOn 功能,允许应用程序保持实例始终运行以减少延迟;新增 Warmup Requests 支持,帮助初始化新实例;Channel API 对所有用户开放;TaskQueue 正式发布并取消实验性标签;提高 URLFetch 响应大小限制至 32MB;增加 Memcache 批次操作总大小限制至 32MB;以及改进数据存储批量操作等。
  • The Always On feature allows applications to pay and keep 3 instances of their application always running, which can significantly reduce application latency.
  • Developers can now enable Warmup Requests. By specifying a handler in an app's appengine-web.xml, App Engine will attempt to send a Warmup Request to initialize new instances before a user interacts with it. This can reduce the latency an end-user sees for initializing your application.
  • The Channel API is now available for all users.
  • Task Queue has been officially released, and is no longer an experimental feature. The API import paths that use 'labs' have been deprecated. Task queue storage will count towards an application's overall storage quota, and will thus be charged for.
  • The deadline for Task Queue and Cron requests has been raised to 10 minutes. Datastore and API deadlines within those requests remain unchanged.
  • For the Task Queue, developers can specify task retry-parameters in their queue.xml.
  • Apps that have enabled billing are allowed up to 100 queues with the Task Queue API.
  • Metadata Queries on the datastore for datastore kinds, namespaces, and entity properties are available.
  • URL Fetch allowed response size has been increased, up to 32 MB. Request size is still limited to 1 MB.
  • The request and response sizes for the Images API have been increased to 32 MB.
  • The total size of Memcache batch operations is increased to 32 MB. The 1 MB limit on individual Memcache objects still applies.
  • The attachment size for outgoing emails has been increased from 1 MB to 10 MB. The size limit for incoming emails is still 10 MB.
  • Size and quantity limits on datastore batch get/put/delete operations have been removed. Individual entities are still limited to 1 MB, but your app may batch as many entities together for get/put/delete calls as the overall datastore deadline will allow for.
  • When iterating over query results, the datastore will now asynchronously prefetch results, reducing latency in many cases by 10-15%.
  • The Admin Console Blacklist page lists the top blacklist rejected visitors.
  • The automatic image thumbnailing service supports arbitrary crop sizes up to 1600px.
  • Overall average instance latency in the Admin Console is now a weighted average over QPS per instance.
  • Added a low-level AysncDatastoreService for making calls to the datastore asynchronously.
  • Added a getBodyAsBytes() method to QueueStateInfo.TaskStateInfo, this returns the body of the task state as a pure byte-string.
  • The whitelist has been updated to include all classes from javax.xml.soap.
  • Fixed an issue sending email to multiple recipients.
  • Revert the default logging level during GWT hosted mode back to INFO.
  • Fixed an issue with OpenId over SSL.

转自:http://code.google.com/p/googleappengine/wiki/SdkForJavaReleaseNotes#Version_1.4.0_-_December_02,_2010



本文是使用 B3log Solo简约设计の艺术 进行同步发布的
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 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,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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