CSE416 S03 – Fall 2024 Master Use Case ListPython

Java Python Master Use Case List (10/8/24)

CSE416, S03 – Fall 2024

Terminology

.    Districting (District plan) – a collection of congressional districts in a state generated by the ReCom algorithm. Each such districting will be a random and will be a subset of all of the possible graph partitions that are constrained by the user-specified constraints on population equality and compactness.

.    Population – the population of a region (e.g., state, precinct, etc.) refers to the total

population as defined by the US Census Bureau. Some calculations might refer to the voting age population (VAP) or citizen voting age population (CVAP). You can use any measure of  population, but it should be done consistently throughout the application.

.    Ensembles – the collection of district plans generated on the SeaWulf. There will beat least one ensemble for SMD (single member districting) and MMD (multi-member districting).

Notation

Use cases listed below include a categorization following the use case title. Three categories of  use cases are provided as “required,” “preferred,” and “optional.” Use cases with an “SD,” “AD,” and “part of GUI SD” indicate that the use case might be requested in the design review with

“SD” referring to a sequence diagram and “AD” referring to an activity diagram. For some use cases, the GUI and the server part can be combined into one use case diagram. Many ofthe use cases that are related and that require activity diagrams can and should be combined into one  activity diagram.

General GUI (11 required)

GUI-1.  Display a pan-able and zoom-able map of the US (required)

The initial page will have a pan-able and zoom-able map, covering the entire United States. It will have a max zoom-out level and abound on how much the user can pan such that the User is guided to stay near the selectable states. Note: this map does not need to be pan-able and zoom- able if the state map is pan-able and zoom-able.

GUI-2.  Select state to display (required) (SD)

User can pick a state through a dropdown menu and also through clicking on the state in the map of the US. The stateselection will cause the map display to show the state centered in the GUI map area and at a zoom level so that it nearly fills the map area. The selected state should display the current district plan.

GUI-3.  Map view filter (preferred)

The GUI should employ a filter that allows selected boundaries to be displayed (or not displayed). The filter should include the current district plan as well as any random district plans available on the server. Multiple filters can be set, so the corresponding boundaries will be displayed simultaneously. Line color and thickness should be set so that the display of multiple boundaries can be easily understood by the user. The demographic heatmap (GUI-XX) will also be available in the filter (if calculated).

GUI-4.  Display summary of SMD/MMD ensembles (required) (SD)

A summary of the SMD and MMD ensembles will be displayed, either as a default part ofthe GUI or in response to a userselection. For each ensemble, the summary will include the number of district plans, average number of minority representatives per plan, average equal population measure, average Republican/Democratic split, and other implemented measure values. The MMD ensemble summary will include the layout for the state (e.g., 3, 3, 4).

GUI-5.  Display whether an MMD district is 3/4/5. (preferred)

When a MMD district is displayed on the map, the user should be able to determine the number of representatives for each district. This can be done with different colors for districts,a tooltip, or some other interface approach.

GUI-6.  Display Detailed Election Data for Simulated Elections (preferred)

The user should have the option to view the detailed election results of an “interesting” simulated election in an ensemble. Details include the district number, number of representatives for that district, party affiliation, etc.  The user should be able to view the results for all candidates, winners, and losers, along with vote totals.

GUI-7.  Display results of MMD simulation with unequal number of candidates (preferred)

Within the MMD ensemble, compare the results of the MMD election simulation for an equal number of candidates with the results for an unequal number of candidates. For example, one of your election simulations might have 3 Republican candidates and 5 Democratic candidates for a 3-representative district. The goal of this use case is to determine the impact of additional candidates for a political party.

GUI-8.  Display the names of actual candidates (preferred)

Display the names of election winners and losers when the names of actual candidates are used in the election simulation.

GUI-9.  Compare enacted plan results with the “average” MMD random district plan

(required)

Display a table comparing the data for the enacted plan with the data for the “average” MMD  random district plan. Items for comparison include Republican/Democratic splits, number of opportunity representatives, vote share, and seat share.

GUI-10.              Display available district 

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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