This program will parse all the important data in all the log file that in place in the same folder with this program into 1 single csv log file. This only parse the power data with the unit of kW. There are 3 mode: 1 (5 min data), 3 (15 min data), and 12 (hourly data).
Some of the label of the output log file is explained below:
- Utility: The total power of the system
- P_ab: Power from panel a and b (similar to Utility)
- PV: Power generated from PV system
- Demand: Power used by load in the house
- P_unmetered: Power of loads that are not metered by the AccuRev
When run the program, it will ask for the path of the folder. Put in the full path of the log folder. Don’t use any extra character. Then when it ask for output file name don’t put in “.csv”.
Similar to powerParser in term of parsing data, this program is suitable for parsing a single or small amount of log files. Put in the “files” list all the name of log files you want to parse in the form of Python string and run the program.
From ENGR 100 lab. Basically it will parse the PJMLMP, PJM Load data and PV data and generate graphs.
This MATLAB program will plot all the power data from all channel and also generate another plot with only load power data. Besides, another output is the txt file with has maximum, minimum, mean, median of each channel.
Put all the data file in one folder to do analysis.
This summer I worked on kinematic data capture framework for KICR (Kinematic Information Capture and Reporting) system. This research is funded by Bucknell Program for Undergraduate Research, under supervisor of Professor Michael Thompson.
Figure 1: My workspace
The KICR project‘s main goal is to develop a system that collects a ADHD patient’s movement data from mobile computing devices, which is then aggregated, analyzed, and delivered to a clinician as relevant, quantitative information. As part of the KICR project, my main job is to develop data capture app that collect sensor data of smartphone and have those data ready for analysis. We expected that the app is going to run for long time on the phone, so it is needed to optimize the app so that it use reasonable amount of phone resources. Although the framework is specifically designed for the KICR project, it can act as the tool for researcher to explore motion sensor data of mobile devices.
I developed the app on two different app development framework: PhoneGap, a multiplatform framework using HTML5 and Android native app development framework. PhoneGap platform is somewhat limited and also consume a lot of resources, so in the end I only focus on making the native Android app. To optimize resources, I put all sensor activities into background service so that it can still record even when the phone is sleeping. I also add some customizable features such as choosing sensor, leveraging sensor capture rate and email log files. The final output of the app is sensor log files in form of csv file, which is convenient for analysis.
Figure 2: Human subject capture test
I did some analysis on app performance and the consistency of sensor log files, and the result is that the phone can capture data well. The app that I developed is used in fidget movement capture test with the purpose of understanding human’s movement data captured by the phone.
More details of my work can be found on Bucknell Website: Hang Ha ’18, electrical engineering, research
Figure 3: My research poster