1 - 25 of 9
| Creator | Title | Description | Subject | Date | ||
|---|---|---|---|---|---|---|
| 1 |
 |
Normann, Richard A.; Johansson,Torbjorn; Abbasi, Masoud; Huber, Robert J. | Three-dimensional architecture for a parallel processing photosensing array | A three-dimensional architecture for a photosensing array has been developed. This silicon based architecture consists of a 10 x 10 array of photosensors with 80 microns diameter, through chip interconnects to the back side of a 500 microns thick silicon wafer. Each photosensor consists of a 300 x 3... | Retina; Optics; Silicon; Photosensing | 1992 |
| 2 |
 |
Normann, Richard A.; Abbasi, Masoud; Johansson,Torbjorn | Silicon carbide enhanced thermomigration | The widespread acceptance of thermomigration technology to produce through-chip interconnects has been impaired by (i) a random walk of the Si-Al liquid eutectic inclusion as it traverses the wafer, and (ii) a ?surface barrier? which allows thermomigration of only relatively large inclusions. In ... | Silicon Dioxide; Thermometers; Transducers; Thermomigration Technology; Infrared Lamps | 1992 |
| 3 |
 |
Rahman, Aowabin | Deep recurrent neural networks for building energy prediction | This poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week). | Machine learning; Energy; Building energy modeling; Deep learning; Recurrent neural networks; Prediction | 2017-01-13 |
| 4 |
 |
Brown, Don R. | The design and implementation of partnet | PartNet is a federated database for providing interactive online access to mechanical parts catalogs. The data contained in the vendor's product database is exported to the federated database using a networkbased distributed database protocol. A Single coherent view of these vendor databases is pro... | 1995 | |
| 5 |
 |
Rahman, Aowabin | Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks | This paper presents a recurrent neural network model to make medium-to-long term predictions, i.e. time horizon of ≥ 1 week, of electricity consumption profiles in commercial and residential buildings at one-hour resolution. Residential and commercial buildings are responsible for a significant fr... | Building Energy Modeling; Machine learning; Recurrent neural networks; Deep learning; Electric load prediction | 2017 |
| 6 |
 |
Harman, Todd B. | Multiscale modeling of accidental explosions and detonations | Accidental explosions are exceptionally dangerous and costly, both in lives and money. Regarding worldwide conflict with small arms and light weapons, the Small Arms Survey has recorded more than 297 accidental explosions in munitions depots across the world that have resulted in thousands of deaths... | 2013-01-01 | |
| 7 |
 |
Ameel, Timothy A; Gale, Bruce K.; Harvey, Ian R. | A three-semester interdisciplinary educational program in microsystems engineering | Motivated by an NSF IGERT grant in the general area of microfluidics, a sequence of three interdisciplinary technical courses has been developed in the emerging area of microsystems engineering. Designed to be taken in series, these courses take students, both graduate and upper-level undergraduates... | 2004 | |
| 8 |
 |
Rahman, Aowabin | Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms | This paper evaluates the performance of deep recurrent neural networks in predicting heating demand for a commercial building over a medium-to-long term time horizon (≥ 1 week), and proposes a modeling framework to demonstrate how these longer-term predictions can be used to aid design of a strati... | Building Energy Modeling; Machine Learning; Recurrent Neural Networks; Deep Learning; Heating Load Prediction; Thermal Energy Storage | 2018 |
| 9 |
 |
Rahman, Aowabin | Predicting fuel consumption for commercial building with machine learning algorithms | This paper presents a modeling framework that uses machine learning algorithms to make longterm, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly ... | Building energy modeling; Machine learning; Prediction; Heating load; Data-driven modeling | 2017-08 |
1 - 25 of 9