Ten most commonly used design patterns

设计模式:外观、适配与桥接模式解析
本文介绍了三种设计模式。外观模式为子系统提供统一接口,便于使用;适配器模式将类的接口转换为客户端期望的接口,使不兼容的类能协同工作;桥接模式将抽象与其实现解耦,让二者可独立变化,还提及了共性/可变性分析。

1. The Facade Pattern
   Provide a unified interface to a set of interfaces in a subsystem. It defines a higher-level interface that makes the subsystem easier to use.

   Only works when:
   1.use a subset of the system's capabilities
   2.interact with it in a particular way

   Variations:
   1.reduce the number of objects a client must work with
   2.provide new functions
   3.hide the system

2. The Adapter Pattern
   Convert the interface of a class into another interface that the clients expect.It lets classes work together that could not otherwise because of incompatible interfaces.

   When to use:
   Create a new interface for an object that does the right stuff but has the wrong interface.

   How to use:
   1.define the interface and then implement in derived classes
   2.the derived class contains the existing class which to be used but has wrong interface, then passes request made to it on through to that class
  
   It frees you from worrying about existing interfaces.

   Two types:
   1.Object Adapter pattern - it relies on one object containing another.
   2.Class Adapter pattern - implement useing multiple inheritance

   Difference: A Facade simplifies an interface while an Adapter converts the interface into a preexisting interface.

3. The Bridge Pattern
   De-couple an abstraction from its implementation so that the two can vary independently.
   -- This pattern is a bit hard to understand at first! But it is a challenging pattern to learn cause it is so powerful.
 
   *** implementation here means the objects that the abstract class and its derivations use to implement themselves with, not the derivations of the abstract class,which are called concrete classes.

   You cant always see the variations at the beginning of the problem.
   Bottom line: During requirements definition, explore for variations early and often!

   It is more useful to focus on the context of the pattern - the problem it is trying to solve.This lets you know when and why to use it.
   You can identify when to apply a pattern before knowing exactly how to implement it.
  
   The bridge pattern is useful when:
     You have an abstraction that has different implementations. It allows the abstraction and the implementation to vary independently of each other.

   Commonality/Variability analysis:
   Commonality - the search for common elements that helps us understand how family members are the same.Thus, the process of finding out how things are common defines the family in which these elements belong(where things vary).
                 It seeks structure that is unlikely to change over time.
   Variability - reveals how family members vary.It only makes sense within a given commonality.
                 It captures structure that is likely to change.

   It is suggested that using commonality/variability analysis as a primary tool in creating objects is a better approach than looking at just nouns and verbs.

基于TROPOMI高光谱遥感仪器获取的大气成分观测资料,本研究聚焦于大气污染物一氧化氮(NO₂)的空间分布与浓度定量反演问题。NO₂作为影响空气质量的关键指标,其精确监测对环境保护与大气科学研究具有显著价值。当前,利用卫星遥感数据结合先进算法实现NO₂浓度的高精度反演已成为该领域的重要研究方向。 本研究构建了一套以深度学习为核心的技术框架,整合了来自TROPOMI仪器的光谱辐射信息、观测几何参数以及辅助气象数据,形成多维度特征数据集。该数据集充分融合了不同来源的观测信息,为深入解析大气中NO₂的时空变化规律提供了数据基础,有助于提升反演模型的准确性与环境预测的可靠性。 在模型架构方面,项目设计了一种多分支神经网络,用于分别处理光谱特征与气象特征等多模态数据。各分支通过独立学习提取代表性特征,并在深层网络中进行特征融合,从而综合利用不同数据的互补信息,显著提高了NO₂浓度反演的整体精度。这种多源信息融合策略有效增强了模型对复杂大气环境的表征能力。 研究过程涵盖了系统的数据处理流程。前期预处理包括辐射定标、噪声抑制及数据标准化等步骤,以保障输入特征的质量与一致性;后期处理则涉及模型输出的物理量转换与结果验证,确保反演结果符合实际大气浓度范围,提升数据的实用价值。 此外,本研究进一步对不同功能区域(如城市建成区、工业带、郊区及自然背景区)的NO₂浓度分布进行了对比分析,揭示了人类活动与污染物空间格局的关联性。相关结论可为区域环境规划、污染管控政策的制定提供科学依据,助力大气环境治理与公共健康保护。 综上所述,本研究通过融合TROPOMI高光谱数据与多模态特征深度学习技术,发展了一套高效、准确的大气NO₂浓度遥感反演方法,不仅提升了卫星大气监测的技术水平,也为环境管理与决策支持提供了重要的技术工具。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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