EE308 Lab2-2

m0_57137642 m0_57137642 墨衍会员 · AI 创作全网分发 墨衍智能分发 通过墨衍一键同步至各平台 可同步平台 CSDN微信公众号微博知乎掘金百家号博客园抖音小红书 你的文章也可以这样分发 墨衍会员 ¥399起/年,写一次发全网 我也要 + 加入社区
The Link Your Classhttps://bbs.csdn.net/forums/MUEE308FZU202201
The Link of Requirement of This Assignmenthttps://bbs.csdn.net/topics/608859318
MU STU ID and FZU STU ID20124601_832002211
Teammate's MU STU ID and FZU STU ID20123671_832002217
Teammate's blog linkhttps://bbs.csdn.net/topics/609128168
GitHub linkhttps://github.com/cjiejiejie/lab2
Video demo linkhttps://www.bilibili.com/video/BV1rv4y1S7Fe/?vd_source=74ee6e134a8d3b785417c5e89eb0e8dd

CONTENTS

  • 1. PSP
  • 2. Learning progress bar
  • 3. The key or difficult functions and programming thinking
  • · How to get well organized prototype code
  • · How to jump between multiple html files and how to transfer variables
  • · How buttons and text boxes fit on the screen
  • 4. Cooperation photos
  • 5. Pair programming experience
  • 6. The events that take a long time and great gains
  • · Using prototype software Figma
  • · Connecting prototype model code and logic code
  • · Coding
  • 7. summary


1. PSP

StagesEstimated time (min)Real time(min)
Planning3035
Estimate4040
Development3045
Analysis4560
Design Spec1010
Design Review1515
Code standard1010
Design90120
Coding600800
Code review400600
Test2030
Reporting4525
Test Report3030
Size Measurement1030
Postmortem&Process Improvement6090
Total14351940

2. Learning progress bar

Week NAdded code (lines)Cumulative code (lines)Study Time this week (hours)Cumulative learning events (hours)Important Growth
11001001010Learn about various prototyping software and compared their advantages and disadvantages, and learned how to make prototypes.
22303301323Understand the underlying code logic of figma and master the ability to write html files independently.

3. The key or difficult functions and programming thinking

· How to get well organized prototype code

At first we used ink knife,but it exported a file that was not easy to modify. Then the prototype is completed on figma, the prototype code we export is multiple files with both css files and html files, and there is no relevant code to jump between pages. Such code is not only complex but also incomplete. So we decided to write our own html code, so that we can understand the html code idea of the prototype while learning html.

· How to jump between multiple html files and how to transfer variables

In order to solve the problem of using code to realize page jump and transfer variables between different pages, we searched a lot of information, and finally solved these two problems by using window.location.href and location.search to obtain the url link string.

· How buttons and text boxes fit on the screen

At the beginning, when we fixed the position of buttons and text boxes on the page with code, the position of buttons and text boxes always changed with the change of screen size. After some discussion and search, we finally replaced the pure value with percentage, thus solving the problem that buttons and text boxes adapt to the screen. But it still have problem when we use firefox edge.

4. Cooperation photos

img

5. Pair programming experience

Experience 1: Rational division of labor enables everyone to perform their duties, so as to complete the project efficiently.
Experience 2: Communicate with teammates in time when encountering problems, so as to brainstorm and solve problems efficiently.
· In general, in the process of pair programming, we realized that one plus one is more effective than two. Because of pairing programming, we can think of more functions and details to make our project more complete, and we spend less time to solve the problems we encounter. The most important point is that when ideas get stuck, our communication can make the problems seem less difficult to solve, so that we have more confidence than we have when doing projects alone

6. The events that take a long time and great gains

· Using prototype software Figma

We spent a lot of time on interface design when we used figma to make prototype models. We know how a program realizes the interface jump function, how to beautify a program, and how to improve the user experience.

· Connecting prototype model code and logic code

In the process of learning how to realize this problem, we had a lot of discussions. During the discussion, we learned that not all prototyping software can export prototype code. Figma can only export code for adjusting parameters. We need to supplement the complete code by ourselves.

· Coding

The process of learning the web language html has lasted for a long time. In this process, we have understood the underlying code logic of figma and mastered the ability to write html files independently.

7. summary

First of all, it was the first time for me to make a web page, and it was also the first time for me to use html language to write code, which enhanced my ability of independent learning and exploration, and also made me familiar with HTML, CSS and JavaScript. Our cooperation also improved my collaboration and analysis ability. This semi-open assignment also allowed me to develop a user-friendly program as best as I could by combining the thinking of users and developers.

...全文

相关推荐

EE-308 LAB4 final version.7z

这是本次实验的压缩包

晨起剪桃花

科技中介服务机构如何提升客户在技术评估与需求匹配环节的服务效率与专业度.docx

科技中介服务机构如何提升客户在技术评估与需求匹配环节的服务效率与专业度

BIZKEEN

51单片机的温湿度检测报警系统仿真资料

51单片机的温湿度检测报警proteus仿真系统

易学长

YOLO26算法星形目标识别+训练好的模型+100张数据集+pyqt可视化界面.zip

附训练好的 YOLO 权重与 PyQt 可视化检测界面,页面底部含可视化效果预览,毕设/课设开箱即用。 【数据集概况】 · 检测类别(中文):[星星(Star)] 【训练曲线与评估图】 【模型训练配置】 参数 | 值 模型 | yolo26n 训练轮数 | 100 epochs 输入尺寸 | 640x640 批次大小 | 24 优化器 | auto 初始学习率 | 0.01 训练设备 【关键指标汇总】 训练了 85 个 epoch,最终轮指标: 指标 | 数值 mAP50 | **0.9950** mAP50-95 | 0.8477 Precision | 1.0000 Recall | 0.9954 train/box_loss | 0.8235 train/cls_loss | 0.7275 val/box_loss | 0.7248 val/cls_loss | 0.9621 【训练过程分析】 85 轮训练后,我们得到了一个 mAP50 为 0.9950 的模型,这真是让人惊喜的成绩。一开始,模型的训练过程进展非常迅速,loss 在前 20 轮内从 1.5000 下降到 1.0000 附近,此后,loss 依然保持稳步下降,但速度明显放缓,特别是在第 30 轮到第 50 轮之间,loss 曲线进入了一个平台期,大约在 0.85 附近徘徊了 10 轮,直到第 60 轮左右才再度加速下降。这样的表现说明模型在前期阶段收敛良好,但到了后期,确实需要更多的训练轮次以确保模型的稳定性。 在验证集上,val_loss 保持平稳,没有出现明显的波动,这进一步证明了模型没有过拟合。然而,我们注意到在第 80 轮和第 85 轮之间,val_loss 似乎有所微弱的反弹,尽管这种波动并不显著。在训练集上,前 50 轮训... 使用场景:毕业设计、课程设计、深度学习入门、工业场景落地验证。

guo_xiao_xiao_

国央企创新负责人如何通过平台识别并引进关键技术?.docx

科易网基于AI模型+行业大数据,在技术转移、成果转化、技术经纪、知识产权、产业创新、科技招商等垂直领域的多样化应用场景,推出科技创新领域的AI+工具与数智化解决方案,推动科技创新与产业创新智能化发展。

BIZKEEN
评论
收藏 已收藏
分享
返回顶部