Students for a Smarter Planet awards were just given to student groups at UT Austin.
BUILDING FAULT DETECTION
Faults and system failures in residential buildings cause excess energy use and negatively affect buildings’ indoor environment. Experiments using UT’s full-scale test house will be conducted, in which common building faults are artificially introduced to identify indicators of these problems in energy consumption data and indoor environmental parameters. Based on these experiments, we plan to develop an algorithm for early detection of these faults. By reducing the occurrence and impact of faults in buildings through their improved detection, we can help reduce energy use in buildings, improve the comfort of occupants, and prevent damage to existing infrastructure.
Early detection of faults help resident reduce their energy use bills and prevents potential damage to building systems.
We will use Energy Simulation Modeling Software (EnergyPlus, EQuest, BEopt), “smart” meter and energy use monitoring equipment, and building environmental sensors (to monitor temperature, relative humidity, etc…). As a result of this project we plan to integrate this “smart” monitoring system into a PC interface, and in the future, a smart phone or other portable device application.
Campus Building Energy Use Reduction
In 2013, students from ESW and ASHRAE teamed with UT’s Energy and Water Conservation program to pilot Longhorn Lights Out (LLO), a program developed to reduce energy by encouraging students, faculty and staff to turn off lights and reduce plug loads on weekends. Our most recent pilot resulted in a reduction in demand of 111 kW, equal to power for 42 homes. To increase participation, we plan to integrate technology into our efforts through the development of a mobile application. We will use this to teach campus members and wider community how to use the app to participate in LLO and other energy conservation activities.
Our project – Real-time Emotion Analysis on Twitter – has completed. We are thinking about the extensibility of our project and we get some ideas.
We use spout and bolt in our project to process data. Spout is in master node and it is like the data file input in Hadoop. Bolt is in worker node and it is like the worker in Hadoop. In our project, the spout is to read Twitter Streaming data and send it to several bolts, which is the process of mapper. And bolt can send data to another bolt. So it is like a chain. Whenever we want to add some new features into our system, you just need to write a new bolt and add it into the processing chain.
In our project, there are two kinds of bolts. The first kind of bolt is to analyze the tweet and extract the useful information and add the emotion value. The the data will be sent to the next bolt, which is responsible to collect and gather the data from several source bolts and publish them into a Redis channel.
So here comes our scalability and extensibility. For the tweet analysis, if we also want to analyze the hashtags as well, all we need to do is to add a kind of bolt in the chain. Then you can either send the result to the reducer or just send the result into another redis channel.
Congratulations to students at Monterrey Institute of Technology and Higher Education for theit award winning proposal in the Mexico Lead with IBM Challenge.
The project, called “Goatee” is an authentication system that provides security to a client when they are making transactions, restricting to the client only the ability to make transactions. This is intended to avoid credit card theft and fraud. It works by a digital finger print reader, which is used as a key to have access to the debit or credit card. Banks benefit by decreasing losses due to fraud and card theft, while providing a real security system to the client.
Congratulations to students who were awarded SFSP awards for proposals in the Mexico Lead with IBM Challenge.
Cloud Medical Records at the Universidad Autónoma de Querétaro
Students proposed to create a Data Recorder system with cloud storage using RFID sensor technology. The system is intended to centralize all one’s medical records in one place for security and access regardless of time and place, allowing for search of your information and fast access to your personal information. It is desired to connect your home measurement devices to have medical tracing day by day as well.
Click on the title to download the entry details.
SHARCL community – China – Tsinghua and Sun Yat Sen University – Community for collaborative consumption.
Members: Conghui He, Lijun Wu, Lingzheng Zeng, Desching Liu, Weijia Li, Lin Gan
U-PLAY - Ireland – University College Cork – electronic toys for disabled children.
Members: Herman Alexander Jaeger, Andrea Zagoneanu, Tadhg Peter Lambe, Merisa Bradley, Fiona Edwards-Murphy
WIRELESS ROAD status notifier – Politecnico di Torino, Italy; Jordan University of Science and Technology, Paris Tech, France, and Applied Science Private University, Jordan. Sensor network to improve traffic management.
Members: Mhd Zaid Sukkar, Omar Alfarouk Alhaffar, Mahmood Qawasmeh, Anas Saci, Ahmad Safi
SOLAR TOWER POWER – Australia, Swinburne University of Technology – solar and wind towers to produce sustainable energy for remote areas.
Members: Danial Arif, Abdul Rehman Hafeez, Nishantaini Devi Chandrasekaran
solar energised power plant- Bangladesh University of Engineering & Technology, University of Dakah – solar powered electric generators for sustainable power.
Members: Parvez Ahammed, Md.Rafid Muttaki, Ismat Jarin, Md. Abdullah-Al-Kaiser, Souvik Sarker