MUS104: Quiz #3 2024Web

Java Python MUS104: Quiz #3 2024

QUESTION 1. Identify the key; complete a Roman numeral analysis, and determine cadence type. Circle any passing or neighbour notes. You lose one mark per incorrect/missing label. (20 marks)

NB: some harmonies have been indicated for you as they have not been covered yet in this course.

Key: _____ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___

Cadence:                   

QUESTION 2. Identify at least five voice-leading errors in this excerpt. Four marks for each correctly identified error. You can draw on the score to show the errors and describe them in words, e.g. 'parallel 5ths between beat 1 and 2 in the Tenor and Bass.' (20 marks)

NB: the Soprano and Bass lines and the Roman Numerals are correct. All errors are in the inner voices.

1. _____________________________________________________________

2. _____________________________________________________________

3. _____________________________________________________________

4. _____________________________________________________________

5. _____________________________________________________________ < MUS104: Quiz #3 2024Web /p>

6. (bonus) _____________________________________________________

QUESTION 3. Correct the voice-leading errors in the previous question by realising the harmony below, following correct voice leading rules. You are penalised 1 mark for each broken rule. (20 marks)

NB: the Soprano and Bass lines and the Roman Numerals are correct and should be retained.

You can use these bars for any working out.

PART 2: This part of the Quiz consists of four prepared musical excerpts. Record them in ONE continuous video. You then submit it, along with the first page written questions to Canvas by the quiz deadline. You can upload the video as a file upload. Each excerpt will be worth 10 marks, for a total of 40 marks.

QUESTION 4: Sing the following two excerpts with solfegge syllables or pitch numbers.

QUESTION 5: Record both of these excerpts. Speak the upper part with takadimi syllable, while clapping the bottom part. You will be assessed on the accuracy of the rhythm, consistency of tempo. You can find a guide to the takadimi system in the Classical Stream Module on Canvas. We recommend you choose a steady tempo of approximately crotchet = 60         

内容概要:本文档详细介绍了基于MATLAB实现的无人机三维路径规划项目,核心算法采用蒙特卡罗树搜索(MCTS)。项目旨在解决无人机在复杂三维环境中自主路径规划的问题,通过MCTS的随机模拟与渐进式搜索机制,实现高效、智能化的路径规划。项目不仅考虑静态环境建模,还集成了障碍物检测与避障机制,确保无人机飞行的安全性和效率。文档涵盖了从环境准备、数据处理、算法设计与实现、模型训练与预测、性能评估到GUI界面设计的完整流程,并提供了详细的代码示例。此外,项目采用模块化设计,支持多无人机协同路径规划、动态环境实时路径重规划等未来改进方向。 适合人群:具备一定编程基础,特别是熟悉MATLAB和无人机技术的研发人员;从事无人机路径规划、智能导航系统开发的工程师;对MCTS算法感兴趣的算法研究人员。 使用场景及目标:①理解MCTS算法在三维路径规划中的应用;②掌握基于MATLAB的无人机路径规划项目开发全流程;③学习如何通过MCTS算法优化无人机在复杂环境中的飞行路径,提高飞行安全性和效率;④为后续多无人机协同规划、动态环境实时调整等高级应用打下基础。 其他说明:项目不仅提供了详细的理论解释和技术实现,还特别关注了实际应用中的挑战和解决方案。例如,通过多阶段优化与迭代增强机制提升路径质量,结合环境建模与障碍物感知保障路径安全,利用GPU加速推理提升计算效率等。此外,项目还强调了代码模块化与调试便利性,便于后续功能扩展和性能优化。项目未来改进方向包括引入深度强化学习辅助路径规划、扩展至多无人机协同路径规划、增强动态环境实时路径重规划能力等,展示了广阔的应用前景和发展潜力。
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