Innovative Projects Realized

Instrumented based bridge evaluation

Bridges in North America are aging and need repair or replacement. Precise evaluation of load carrying capacity of bridges is a way to prevent economic and environmental impact due to replacement and repairs. Current method of evaluation requires the evaluator to make conservative assumptions on load and resistance of bridges. In recent years as technology develops, more precise data can be collected and be processed. Sensors can be mounted at carefully selected positions on bridges to provide sufficient data for more precise bridge evaluation. In this work, available data from long-term monitoring of two bridges will be used to investigate the impact of instrumentation and structural monitoring on the structural performance and evaluation of bridges.

Optimizing Wall Formwork Shuttering Design Using Prefabricated Panels in Concrete Construction

Determining an optimal formwork shuttering solution is not a well answered problem, relying heavily on the designer’s preferences and past experiences rather than efficient algorithms and standardized procedures. This results in inefficient solutions that increase overall costs and construction time. Globally, formwork is nearly a $6 billion USD/ year industry; even minor improvements in efficiency of design will result in huge cost savings.

This research aims to develop a standardized methodology and identify general heuristics for solving typical wall formwork problems. The intent is to also parameterize the important variables that can affect design decisions and be used to increase efficiency of design based on regional differences in labor, material, and transportation costs, or other design requirements. A generalized solution has previously been shown to be computationally infeasible so the intended approach is to make locally greedy design choices and show that the resulting greedy solution is bounded by a certain efficiency.

A Raman spectral characterization of thin-film carbon

Informed thin-film carbon device design requires a detailed understanding of the material properties of thin-film carbon. In this project, we will develop a detailed understanding of the fundamentals underlying this material. Raman spectroscopy, wherein the interaction of an incident light source with the vibrating molecules within a given sample is probed, will be the technique through which this study will be performed. In particular, the Raman spectrum and its relationship with the constellation of material properties commonly examined in the characterization of thin-film carbon, will be studied. Our industrial partner, Allied Corporation, a Kelowna, British Columbia based firm, is interested in this project for two reasons: (1) they are interested in developing a new generation of thin-film carbon-based commercial products, and (2) they are interested in contributing to the cultivation of a culture of scientific excellence in the local Okanagan region, that they believe that they can ultimately benefit from. This particular project focuses on the fundamentals, the envisaged future applications of thin-film carbon being developed later through the use of other sources of funding.

Sensors data transmission with the Internet of Things (IoT) for water purification systems in indigenous communities

The major contributing factor to waterborne outbreaks in Canada in small drinking water systems is the operators’ lack of technical expertise. Training of small system operators do not cover hands-on training specific to the treatment technologies used in their plants. A simplified smartphone app with real-time monitoring can assist the operators with the decision making process. Aqua Intelligent Technology Inc. is providing this smart solution for small water treatment systems. The vital step in this technology is receiving real-time data from the sensors in such facilities. Having no cellular or WiFi in these plants requires the engineers to use other communication methods for transmitting the data. In this project, an adaptive method that automatically detects the type of sensors in the facility is used. This method also uses the Internet of Things (IoT) communication protocols to provide a reliable link between the sensors’ data in the system and the application server.

Measurement and modelling of stream thermal regimes in the Ottawa River watershed

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Accelerating Neural Networks on FPGAs through High-Level Synthesis

Computer hardware is enjoying a widespread renaissance with the emergence of the compute-intensive and challenging machine learning workloads. ABR develops and maintains NENGO, a biologically-plausible model for neural networks and is keen to develop hardware support for efficient realizations of these networks. FPGA (Field-Programmable Gate Arrays), are an attractive target for this if we can overcome the communication bottlenecks that limit the effectiveness of these designs. This project aims to develop strategies for implementing communication between neuron populations mapped onto an FPGA and deploy them to the cloud for demonstration.

Health benefits of living in a wellness-centred community

The general objective of this project is to test for improvements in the physiological and psychosocial health of residents in a wellness-centred community (e.g. the Wellness Suites) who engage in a prescribed healthy lifestyle that emphasizes regular exercise and a modified vegan (i.e. Pegan) diet. Physiological, molecular, and psychosocial assessments will be used to identify participant progress from day of intake to six months, and to identify new biomarkers of healthy aging. The study will address the importance of a targeted healthy lifestyle to improve quality of life (healthspan) in older adults, improve health recommendations for enhancing healthy aging, and reduce healthcare burdens.

Network anomaly detection of Building Automation Systems

This project aims to monitor the communications among the devices connected in a building automation system. It is important to detect any changes in the normal communication pattern of the devices. Such changes often signify a change of operational behavior, caused either by a failure or an impending breakdown. In the worst-case scenario, anomalies deviated from normal communication pattern may indicate the system is under a cyberattack, which would have serious consequences to the people inside and the building itself. Therefore, it is necessary to develop software system to detect such anomalies and to alert the appropriate personnel. The partner organization is a building automation system technology company, with international installations all over the world in public places and commercial buildings. By participating in the anomaly identification project, the partner organization may be able to leverage the research result and improve their product features, and establish their leading role in the industry nationally and internationally.

Costing, rewards, and SEM analysis: Application to a B2C platform launch

We use optimization and analytics, within a dynamic experiment setting, to help determine a business-to-consumer matching platform transaction fees and improve customer engagement. During the course of the project, a graduate student intern will gain deeper understanding of designing and running dynamic experiments and learn more about using optimization within game-theory models. The intern will also further understand the demands of running a matching platform. The industry partner will gain insights on setting transaction fees and increasing customer engagement.

Identifications of various defect types during a fused deposition modeling process based on deep learning technology

This project is to purpose to use computer vision to identify the various error types during the operation of 3D printers to boost their throughput and enhance their application in the manufacturing industry. Nevertheless, due to the lack of precision and controllability inside the printers, engineers cannot achieve a reliable printing process and acceptable quality of final 3D printing products. In this research, a monitoring system equipped with computer vision is proposed to address this challenge. The computer vision program can monitor the printing process and easily detect the error type and inform the operators immediately when a failure occurs. As a result, this program will significantly reduce waste and improve productivity for 3D printers.

Automating Asset Management Solution for Electric Utilities’ Industrial Control Systems: An Integrated Approach

Increasing visibility into asset inventory and gaining situational awareness in industrial control system (ICS) environment are of critical importance for electric utilities to effectively manage cybersecurity risks. In this project, we aim to investigate the solution design for electric utilities to automate asset management in an ICS, addressing the unique challenges such as device heterogeneity and legacy technology. Moreover, we aim to augment this solution by consolidating information from operational technology (OT) and information technology (IT) to improve situational awareness for electric utilities. By reviewing the substation devices and existing security systems in BC Hydro’s environment, we aim to tailor the solution for BC Hydro that aligns with its cybersecurity and risk management requirements.

Reinforcement Learning for anomaly detection in real-time camera feed

How to automatically monitor wide critical open areas is a challenge to be addressed. In this project we are looking for using CNN+LSTM technique for identifying anomalies and by using a deep reinforcement learning approach, classify them into one or more groups such as health, crime, accidents etc. This project aims to alleviate this problem by using deep learning reinforcement algorithms to emergency conditions in a video feed. In this way, the intern should work on this real-time data to, at first, finding anomalies from the live video, then, categorize them into relevant classes. Moreover, since the intern currently works on Artificial Consciousness in his PhD studies as well as have a good background in Machine Learning techniques, depends on the provided facilities, wondering he has an idea to apply Consciousness concept to the model in order to increase the ability of anomaly detection like a human.