Project

# Title Team Members TA Documents Sponsor
26 ML-based Weather Forecast on Raspberry Pi
Best Interdisciplinary
 Chenzhi Yuan
Xuanyu Chen
Zhenting Qi
Zheyu Fu
 design_document1.pdf
final_paper1.pdf
proposal2.pdf
 Cristoforo Dimartino
#Team Members

Zheyu Fu (zheyufu2@illinois.edu 3190110355)
Xuanyu Chen (xuanyuc2@illinois.edu 3190112156)
Chenzhi Yuan (chenzhi2@illinois.edu 3190110852)
Zhenting Qi (qi11@illinois.edu 3190112155)

#Problem

Weather forecasting is crucial in our daily lives. It allows us to make proper plans and get prepared for extreme conditions in advance. However, meteorologists always get it wrong half of the time and still keep their job :) To overcome the limitations of traditional weather forecasting, machine learning models have become increasingly important in weather forecasting. Building our own weather forecast ML system is a perfect idea for us to analyze vast amounts of area data and generate more accurate and timely weather predictions on the go in our surrounding areas.

#Solution Overview

A weather forecast system can be created by using a few different hardware components and software tools. Our solution mainly consists of two parts. For weather measurement and data collection, temperature, humidity, and barometric pressure sensors are considered the main components. A machine learning-based algorithm is to be applied for data analysis and weather predictions.

#Solution Components

##Hardware Subsystem

Due to the complexity of weather conditions, our system incorporates the following weather indicators and their corresponding collectors:
-a barometric pressure sensor, a temperature sensor, and a humidity sensor
-a digital thermal probe for heat distribution
-an anemometer for wind speed, wind vane for wind direction, and rain gauge for precipitation
The aforementioned equipment would be integrated into a single device, and weatherproof enclosures are needed to protect it. Plus, a Raspberry Pi, either with built-in wireless connectivity or a WiFi dongle, is required for conducting computations.

##Software Subsystem

A practically usable weather forecast system is supposed to make reliable predictions for real-world multi-variable weather conditions. We apply Machine Learning techniques to suffice such generalization to unseen data. To this end, a high-quality dataset for training and evaluating the Machine Learning model is required, and a specially designed Machine Learning model would be developed on such a dataset. Once a well-trained system is obtained, we deploy the such model on portable devices with easy-to-use APIs.

#Criterion for Success
1. The weather measurement prototype with sensors should be able to accurately collect the temperature, humidity, and barometric pressure. etc.
2. A machine learning algorithm should be successfully trained to make predictions on the weather conditions: rainy, sunny, thunderstorm, etc.
3. Our system can forecast the weather in Haining, in real-time, and/or longer-period forecast.
4. The forecasted weather information could be demonstrated elegantly through some UI interface. A display screen would be a baseline, and an application on phones would be extra credit if time permitted.
5. Extra: Make our own weather dataset for Haining. If good, make it open-source.

#Work Distribution

**EE Student Zheyu Fu**:
-Design the sensor module circuit
-Development of visualization interface

**ECE Students Xuanyu Chen & Zhenting Qi**:
-Weather data collection and analysis
-Build and test Machine Learning model on Raspberry Pi

**ME Student Chenzhi Yuan**:
-Physical structure hardware design
-Proper distribution of the sensors to collect accurate data on temperature, humidity, barometric pressure, etc.

Robotic T-Shirt Launcher Mark II

Hao Ding, Moyang Guo, Yixiang Guo, Ziyu Xiao

Featured Project

ROBOTIC T-SHIRT LAUNCHER MARK II

TEAM MEMBERS

Guo yixiang (yg16),

Guo moyang (moyangg2),

Xiao ziyu (ziyux2),

Ding hao (haod3)

PROBLEM

Our team has identified a problem with the launcher project that was completed last year. In particular, the previous design only included a single-shot launcher that required manual reloading and could only adjust the angle and direction automatically.

SOLUTION OVERVIEW

To address this issue, our team has proposed an improved design that will improve upon the limitations of the previous model. The Robotic T-shirt Launcher Mark II will be a fully automated system capable of launching multiple T-shirts by itself, without manual reloading. Our proposed design will also include more advanced features, such as the ability to adjust the trajectory of the launch. In addition, we will build it into a wearable device that could be carried on our shoulders.

SOLUTION COMPONENTS

The automatic launcher is comprised of several components that work together to provide a powerful and reliable weapon system. These components include:

Power Components: The power components of the system consist of an air pump, an air cylinder, a quick exhaust valve, and connecting elements. These components are responsible for providing the necessary power and pressure to the system to shoot out the bullet.

Function Components: The functional components of the system include the barrel, the two-axis gimbal (which is wearable), and the automatic loading system. The barrel provides the means for firing projectiles, while the gimbal allows for precise targeting and tracking of moving targets.

Control System: The control system is responsible for managing the various components of the system, including the electromagnetic valves that control the airflow, the actuator controllers for the loading mechanism, and the gimbal controller for targeting.

Human-Machine Interface (Advanced Requirement): For advanced users, the system could include a human-machine interface with features such as automatic firing, angle adjustment, and target recognition lock-on, allowing the user to engage targets effectively.

CRITERIA FOR SUCCESS:

Functionality: The launcher should be able to launch T-shirts accurately and consistently at a controlled angle and velocity. The system should be able to handle multiple T-shirts without the need for manual reloading, and the entire launch process and angle control should be initiated and controlled by a single button.

Airtight and Adequate Air Pressure: The launcher's air channel should have high airtightness and be able to generate sufficient air pressure to launch T-shirts effectively. The air pressure should be able to be adjusted and controlled to suit different launch scenarios.

Automation: The loading system should be fully automated, with T-shirts being automatically loaded into the air chamber without the need for manual intervention. The loading mechanism should be designed to be reliable and efficient, and the electrical control system should be able to manage the entire process automatically.

Safety and Cost-effectiveness: The launcher should be designed with safety in mind. Safety mechanisms, such as emergency stop buttons, should be included to prevent accidents or injuries. The design and construction of the launcher should be cost-effective, and any additional features should be carefully considered. Also, it is necessary to implement additional components to measure some critical values such as gas tightness in order to prevent gas leaks.