FUZHUAN: Day 9-10 — SCRUM and PM Report

FUZHUAN 2024-12-21 00:48:58
Which course does this assignment belong toEE301
Where are the requirements for this assignment?Teamwork—beta Spring
Team Name

FUZHUAN

The objective of this assignmentDay 9-10 — SCRUM and PM Report
Other Reference DocumentsMethod of Construction ( 《构建之法》)

目录

SCRUM Part

PM Report

Expected Tasks Overview

Completed Tasks

Remaining Tasks

Burndown Chart

Changes in Task Volume


SCRUM Part

Team MemberAccomplishmentsTime SpentRemaining TasksIssues/ProblemsPlan for Tomorrow
Zhang YuxinCompleted final testing, fixed any remaining issues.6 hoursFinalize documentation and handover.Minor bugs found in final user flow.Conduct last testing on product purchase flow and finalize handover.
Luo YuxinFinalized product pages, made last adjustments based on feedback.5 hoursConduct final cross-browser testing.UI inconsistencies between browsers.Conduct final testing and resolve UI inconsistencies.
Yang RuoxinConducted final testing on all user flows.5 hoursFinalize documentation and submit for review.Some test cases failed in edge conditions.Resolve edge case issues and prepare final project review.
Lin JiahuiFinalized payment system and integration.6 hoursPrepare handover and final documentation.Final adjustments in payment gateway security.Ensure final security checks for payment system.
Sun XingCompleted final load testing and optimization of APIs.5 hoursDocument API performance and optimization.Stress testing results showed minor API lag.Finalize documentation for API optimizations and finalize stress testing.
Chen YikeFinalized database performance tuning and completed documentation.5 hoursComplete final query optimization report.Query performance slightly below expectations on heavy data sets.Finalize database optimization documentation and reporting.

 


PM Report

Expected Tasks Overview

TaskEstimated TimeIssues Count
Final testing and bug fixes10 hours2
Documentation and project review6 hours1

Completed Tasks

TaskTime SpentIssues Completed
Final testing and bug fixes8 hours2
Documentation and project review6 hours1

Remaining Tasks

TaskRemaining TimeRemaining Issues
Final testing and bug fixes2 hours1
Documentation and project review0 hours0

Burndown Chart

 

Changes in Task Volume

No major changes were encountered during the final testing phase, as all tasks were largely as expected. However, some minor issues were found with the final user flow, which extended the bug-fixing tasks.

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内容概要:本文系统研究了基于监督学习的多模态MRI脑肿瘤分割方法,重点聚焦于利用监督体素的纹理特征实现肿瘤区域的精确识别。文章首先阐述了监督学习的基本原理及多模态MRI在医学影像分析中的独特优势,进而深入探讨了灰度共生矩阵(GLCM)、局部二值模式(LBP)和小波变换等多种纹理特征提取技术。在此基础上,构建了基于传统特征工程的分类器模型以及融合深度学习的混合模型,并通过在公开数据集上的实验验证其分割性能,采用Dice系数、敏感度、特异度等指标进行全面评估。研究还剖析了当前面临的关键挑战,如高质量标注数据稀缺、模型泛化能力受限、可解释性不足以及肿瘤边界模糊等问题,进一步提出了结合半监督学习、多任务学习、模型可解释性增强及多模态信息深度融合等未来发展方向,为提升脑肿瘤自动分割的精度与临床实用性提供了系统的理论依据和技术路径。; 适合人群:具备医学图像处理或机器学习基础知识,从事生物医学工程、人工智能辅助诊断、计算机视觉等相关领域的科研人员及研究生。; 使用场景及目标:①掌握多模态MRI脑肿瘤分割中的关键技术流程与核心算法;②深入理解监督体素与纹理特征在医学图像分割中的具体应用价值;③为研发高精度、强可解释性的自动化脑肿瘤分割系统提供方法参考与技术支持。; 阅读建议:建议结合提供的Matlab代码实现同步学习,重点关注纹理特征提取与模型构建部分,动手复现实验过程以深化理解,同时重视数据预处理与结果评估环节,全面提升科研实践与创新能力。

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