Week 8

Software Team:

Python implementation of storing data in MongoDB

Finish working with the Linear SVM

• Determine X and y vectors to be used (training and target vectors)
• Begin testing on data, determine what class labels are returned and compare
• Further look into model persistence

Discover Image Processing Python Libraries: Keras, OpenCV, TensorFlow

Physical use cases and company examples that are similar to our IoT framework:

• To understand how we can match our project with ‘next level’ and to see if we are on the right path

Next Steps:

Learn more about pyMongo and associated packages

Write a python script to parse several data files into one file and push data into mongoDB

Finish testing the Linear SVM to ensure it’s accuracy

• Use more data sets

-Determine whether or not it is best to save the model after it is trained so it does not need to be retrained later

• As a file - joblib (specific to scikit learn)
• As a string – pickle

Week 7

Software Team

Attempt 3 damage state model implementation (Benchmark Material Laws)

Straight Edge Fatigue Data

Get initial Probabilities

Get Transition Matrix

GaussianHMM 3 components

Final Deliverable:

RUL Predictions

Research and implement Support Vector Machines : Supervised Learning Model

Model Persistence: Save a model in scikit-learn

Make a file reader that takes the data inputted by the system and constantly updates the matrix.

Make it so the matrix is converted to a pandas dataframe after data collection is done.

How process large sets of data efficiently? [store and access] And does our project fall in-line with any corporate applications/products?

Next Steps:

Finish implementing Support Vector Machines

Understanding current Machine Learning architecture in SHM

Cornell Cup: Autodesk Fusion updates

AIRBUS proposal due Friday

Hardware Team:

To implement the three raspberry pi network successfully

Be able to setup a NAS to allow for data sharing

Next Steps:

To troubleshoot the raspberry pi’s and see what the issues are

Finish up network of raspberry pi’s and NAS setup

IoT Applications:

Smart Grid

• Implemented smart grid technology and has reduced outage times by over 50 percent, saving over $1.4 million in operations costs during a single storm.
• The implementation of a smart grid, coupled with high-speed internet infrastructure, has provided a significant economic boost to the city.
• Allowing residents to more easily participate in battery storage, contributing to greener, cleaner cities.
• Promotes integration of the vast amount of renewable energy that is currently being mandated
• Uses sensors to change brightness of lightening based on activity in certain areas

Smart Parking:

How does it work?

IoT enabled smart parking makes use of low-cost sensors, a cloud and mobile applications to give real time information about availability and location of parking space and even provide reservation or payment features.

Beneficiaries

• Drivers – Flexibility to reserve, choose and modify parking spots
• Law enforcement – Prioritize high priority enforcement and space management
• Business owner - Space management and opportunity to lure more customers.