English In August

本文讲述了作者在暑假期间坚持每日英语学习的经历,包括与同伴练习口语、个人自学等多个方面。通过持续的努力,作者在英语听力和口语方面取得了显著进步。

          Our  Summer vacation will be over, In those days, I think I have a good time.Everyday,I can   plan  my time  appropriately. Sometimes  I have a lot of work needed to be done.Maybe , I feel tired  but I think that I have a great progress.

      Of course,English is my Daily compulsory course.Everyday,during 7:30--8:00,I and Cathy  talk about AJ mini story and main text,sometimes,we also have a little speech about anything. This can great exercise my English pronunciation and this also bring me confidence. Finish that,I will have two hours to learn English by myself,sometimes,I repeat loudly,and sometimes, maybe I watch American TV series. Through so many days of exercise,I find that I have a good listening,and I can understand others who speak English. I am very proud of myself. I believe that if you do something hard,you must can get something that out of your imagine.

       After more than one week , I will go back my school. There is no question,I will became very busy,  but I believe myself  that I can deal with that and I won't lose English learning.


Group Assignment - Case Study BACKGROUND BeachBoys BikeShare is a bike share service provider where users can take and return bikes at any of the 70 sta�ons on their network. The company wants to leverage their data to beter understand and, hopefully, op�mize their opera�ons. BikeShare has decided to start by harnessing analy�cs to enhance opera�ons in the logis�cs department, by improving the redistribu�on of bikes between sta�ons to meet demand, and ensuring that there are bikes and return docks available when and where users need them. As a key step towards tackling this challenge, management has tasked you to develop a model capable of predic�ng the net rate of bike ren�ng for a given sta�on, which is defined as the number of bikes returned to, minus the number of bikes taken from, the given sta�on in a given hour. In other words, your model should enable BikeShare's logis�cs team to make the statement - "In the next hour, the quan�ty of bikes at sta�on A will change by X”. In addi�on, management would also like you to: • Help them understand the factors that affect bike rental, which could inform future decisions on where to locate BikeShare's sta�ons. • Help them conceptualize how your predic�on may be used to improve the redistribu�on of bikes within the network. • Highlight any assump�ons or drawbacks of the analysis, if any, and suggest how they may be verified or addressed in the future. ASSIGNMENT Explore, transform, and visualize the given data as appropriate, before using it to train and evaluate an appropriate ML model for the problem. Address the issues highlighted by management as described above, albeit in a less in-depth manner. Finally, ar�culate your findings and recommenda�ons in a concise, coherent, and professional manner, making reference to any earlier results or diagrams as appropriate to support your conclusions. Please use Python to complete this task, using any libraries you might deem necessary for your analysis, e.g., pandas, sklearn, etc. Detail your code, analysis findings, and recommenda�ons clearly in a reproducible Jupyter notebook with appropriate comments and documenta�on, so that an individual viewing the notebook will be able to follow through your steps and understand the reasoning involved and inferences made. DELIVERABLES You should upload the following deliverables in a .zip file: • A Jupyter notebook detailing your analysis and findings for this project, • A PDF-ed copy of the Jupyter notebook above, which should not exceed 30 pages, • The datasets used in your analysis, which should be loaded into your notebook, • Addi�onal files relevant to your analysis, which should be described in your notebook. Finally, please prepare presenta�on slides for the group presenta�on (10-15 mins). All team members need to present in English. THE DATA The company has collected informa�on on the sta�ons, trips taken, and on weather condi�ons in each of the ci�es from September 2014 to August 2015. You can find the data here - bikes_data.zip (3.1 MB). Below, you will also find detailed informa�on on all the fields available in the dataset. The way you include this informa�on in your model is up to you and should be clearly jus�fied and documented in your report. You are free to use any other data sources provided you specify a link to this informa�on in your report. Sta�on Data • ld: sta�on ID number • Name: name of sta�on • Lat: la�tude • Long: longitude • Dock Count: number of total docks at sta�on • City: one of San Francisco, Redwood City, Palo Alto, Mountain View, or San Jose Please note that during the period covered by the dataset, several sta�ons were moved. Sta�ons 23, 25, 49, 69, and 72 became respec�vely sta�ons 85, 86, 87, 88, 89 (which in turn became 90 a�er a second move). Trip Data • Trip ld: numeric ID of bike trip • Dura�on: �me of trip in seconds • Start Date: start date of trip with date and �me, in Pacific Standard Time • Start Sta�on: sta�on id of start sta�on • Start Terminal: numeric reference for start sta�on • End Date: end date of trip with date and �me, in Pacific Standard Time • End Sta�on: sta�on id for end sta�on • Subscrip�on Type: Subscriber (annual or 30-day member) or Customer (24hour or 3-day member) Weather Data • Date: day for which the weather is being reported • Temperature (day min, mean and max): in F • Dew point (day min, mean and max): Temperature in F below which dew can form • Humidity (day min, mean and max): in % • Pressure (day min, mean and max): Atmospheric pressure at sea level in inches of mercury • Visibility (day min, mean and max): distance in miles • Wind Speed (day max and mean): in mph • Max Gust Speed: in mph • Precipita�on: total amount of precipita�ons in inches • Cloud Cover: scale of 0 (clear) tons (totally covered) • Events: Special meteorological events • Wind Direc�on: in degrees • Zip: area code for San Francisco (94107), Redwood City (94063), Palo Alto (94301), Mountain View (94041), and San Jose (95113)
08-07
基于STM32 F4的永磁同步电机无位置传感器控制策略研究内容概要:本文围绕基于STM32 F4的永磁同步电机(PMSM)无位置传感器控制策略展开研究,重点探讨在不依赖物理位置传感器的情况下,如何通过算法实现对电机转子位置和速度的精确估计与控制。文中结合嵌入式开发平台STM32 F4,采用如滑模观测器、扩展卡尔曼滤波或高频注入法等先进观测技术,实现对电机反电动势或磁链的估算,进而完成无传感器矢量控制(FOC)。同时,研究涵盖系统建模、控制算法设计、仿真验证(可能使用Simulink)以及在STM32硬件平台上的代码实现与调试,旨在提高电机控制系统的可靠性、降低成本并增强环境适应性。; 适合人群:具备一定电力电子、自动控制理论基础和嵌入式开发经验的电气工程、自动化及相关专业的研究生、科研人员及从事电机驱动开发的工程师。; 使用场景及目标:①掌握永磁同步电机无位置传感器控制的核心原理与实现方法;②学习如何在STM32平台上进行电机控制算法的移植与优化;③为开发高性能、低成本的电机驱动系统提供技术参考与实践指导。; 阅读建议:建议读者结合文中提到的控制理论、仿真模型与实际代码实现进行系统学习,有条件者应在实验平台上进行验证,重点关注观测器设计、参数整定及系统稳定性分析等关键环节。
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