British Columbia Institute of Technology

About: British Columbia Institute of Technology is a education organization based out in Burnaby, British Columbia, Canada. It is known for research contribution in the topics: Smart grid & Belief revision. The organization has 458 authors who have published 785 publications receiving 16140 citations.

Privacy Preserving Occupancy Detection Using NB IoT Sensors

Abstract: Occupancy detection is crucial when trying to lower the emissions that a building produces. Some buildings are equipped with motion sensors or cameras to find how many occupants are in a room. However, this is not entirely accurate as people could be stationary in situations like sitting at a desk or watching television. Using environmental sensors, we can determine if a room is occupied even if the occupants are not moving. When occupants are inside a room, they give off extra CO2 or increase the room's temperature. We can find the small differences in the environmental values used to accurately predict a room's occupancy levels. We use relatively inexpensive IoT sensors that almost every building's HVAC system should have in the near future. We apply K-means clustering with success to predict occupancy levels. Our algorithms can be used in smart thermostats to automatically adjust the room's heat depending on how many occupants are in a room.

Offline and Real-Time Implementation of a Personalized Wheelchair User Intention Detection Pipeline: A Case Study *

Abstract: Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme and require an accurate interpretation of the user’s intent to provide effective propulsion assistance. This paper investigates a user-specific intention estimation framework for wheelchair users. We used Gaussian Mixture models (GMM) to identify implicit intentions from user-pushrim interactions (i.e., input torque to the pushrims). Six clusters emerged that were associated with different phases of a stroke pattern and the intention about the desired direction of motion. GMM predictions were used as "ground truth" labels for further intention estimation analysis. Next, Random Forest (RF) classifiers were trained to predict user intentions. The best optimal classifier had an overall prediction accuracy of 94.7%. Finally, a Bayesian filtering (BF) algorithm was used to extract sequential dependencies of the user-pushrim measurements. The BF algorithm improved sequences of intention predictions for some wheelchair maneuvers compared to the GMM and RF predictions. The proposed intention estimation pipeline is computationally efficient and was successfully tested and used for real-time prediction of wheelchair user’s intentions. This framework provides the foundation for the development of user-specific and adaptive PAPAW controllers.

Erratum to: Determination of indoor humidity profile using a whole-building hygrothermal model

Abstract: This publication could be one of several versions: author’s original, accepted manuscript or the publisher’s version. / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur. For the publisher’s version, please access the DOI link below./ Pour consulter la version de l’éditeur, utilisez le lien DOI ci-dessous.

An expert system to support snow avalanche forecasting

Abstract: Reports on the development of an expert system to support the forecasting of snow avalanches. First the authors give a general introduction to the area of snow avalanche forecasting. Subsequently, both the knowledge acquisition, and the implementation/integration on IBM PC type platforms are described in some detail. Initial test results based on the current prototype implementation are reported. Finally, some lessons learned and conclusions for future development efforts are presented.

Achieving excellence in technical communication classes by using IEEE Spectrum magazine & active learning techniques

Abstract: Active learning, also known as cooperative learning, is a teaching strategy which can be used to help engineering students learn concepts in typical junior-level communication courses. Students are placed in randomly chosen, mixed ability teams at the start of the course. To encourage group process and team-building skills, students adopt team roles like scribe, timekeeper, leader, & participant and the instructor uses techniques like roundtables, think-pair-share, stations, jigsaws, and journaling to deliver the objectives of the course. Articles from IEEE Spectrum magazine are used to supplement the textbook and to make the content engineering specific. The benefits to the instructor are better on-task student behavior and the ability to provide regular feedback to students despite the everyday teaching challenges of large class sizes and a wide range in the students' level of language fluency.