UI生成统一JSR?(转载自Metawidget创始人博客)

本文讨论了UI生成的不同方法及特点,并探索了将其统一的可能性。文章提出了几个关键问题:静态或运行时生成、是否需要专用建模语言、生成UI的范围、跨平台支持等。

source: http://kennardconsulting.blogspot.com/2008/10/unified-theory.html

I just had an interesting exchange with the guys from the OpenXava project. We discussed differences in our two approaches, as well as those of other UI generation projects, and what it would take to unify them all under a JSR one day. I think we're a long way from that day, primarily because UI generation isn't particularly 'mainstream' yet (at least, not in the sense of ORM). Still, it's often said in physics that even though we don't know what the Unified Theory is, we know something about what features it must have.

Can we say something similar about UI generation? I'll list here all those features I think are being explored, either by Metawidget, OpenXava or one of the other projects, and see if we can update this page over the years to form consensus.

Static or Runtime

Should the generation happen statically or at runtime? If runtime, how do you allow customisation? If statically, how do you allow re-running the generation when the domain model changes (without losing any customisations)?

Modeling Language

Should the generator have its own modeling language, which developers use to describe the UI, or should it try and derive the UI automatically? Do modeling languages introduce error-prone duplication? Is automatic derivation too inflexible? Is there enough metadata to drive automatic derivation, or do we have to 'guess and fill in the gaps'?

Production or Prototype

Should we expect UI generation to be able to be used in production applications, or only during a prototyping phase?

Customisation

What sort of customisations of the generated UI are important? Graphics? Layouts? How should we facilitate them?

Bounds of Generation

Should we try to automatically generate the whole UI, or just pieces of it? Is generating the whole UI flexible enough? Is just generating pieces useful enough?

Multiple Platforms

Is supporting multiple platforms (eg. desktop, web, mobile) important?

Consistency

Given the same domain model, should we try and produce a consistent UI across all platforms? Does this risk a 'lowest common denominator'? Does tailoring uniquely to each platform introduce too much work for the developer?

Diverse Architectures

Should the generator care about diverse architectures? Is mandating the technology stack of the application too restrictive? Or should we try to enforce 'good coding' that way? Does supporting multiple versions of everything introduce too much complexity? Is the ability to retrofit existing applications an important goal?

Third Party Components

Should we support third-party UI components? What if they are not available on all platforms (eg. desktop, web)?

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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