Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

A quantitative real options method for aviation technology decision-making in the presence of uncertainty

The developments of new technologies for commercial aviation involve significant risk for technologists as these programs are often driven by fixed assumptions regarding future airline needs, while being subject to many uncertainties at the technical and market levels. To prioritize these developments, technologists must assess their economic viability even though standard methods used for capital budgeting are not well suited to handle the overwhelming uncertainty surrounding such developments. This research proposes a framework featuring real options to overcome this challenge. It is motivated by three observations: disregarding the value of managerial flexibility undervalues long-term research and development (R&D) programs; windows of opportunities emerge and disappear and manufacturers can derive significant value by exploiting their upside potential; integrating competitive aspects early in the design ensures that development programs are robust with respect to moves by the competition. Real options analyses have been proposed to address some of these points but the adoption has been slow, hindered by constraining frameworks. A panel of academics and practitioners has identified a set of requirements, known as the Georgetown Challenge, that real options analyses must meet to get more traction amongst practitioners in the industry. In a bid to meet some of these requirements, this research proposes a novel methodology, cross-fertilizing techniques from financial engineering, actuarial sciences, and statistics to evaluate and study the timing of technology developments under uncertainty. It aims at substantiating decision making for R&D while having a wider domain of application and an improved ability to handle a complex reality compared to more traditional approaches. The method named FLexible AViation Investment Analysis (FLAVIA) uses first Monte Carlo techniques to simulate the evolution of uncertainties driving the value of technology developments. A non-parametric Esscher transform is then applied to perform a change of probability measure to express these evolutions under the equivalent martingale measure. A bootstrap technique is suggested next to construct new non-weighted evolutions of the technology development value under the new measure. A regression-based technique is finally used to analyze the technology development program and to discover trigger boundaries which help define when the technology development program should be launched. Verification of the method is performed on several canonical examples and indicates good accuracy and competitive execution time. It is applied next to the analysis of a performance improvement package (PIP) development using the Integrated Cost And Revenue Estimation method (i-CARE) developed as part of this research. The PIP can be retrofitted to currently operating turbofan engines in order to mitigate the impact of the aging process on their operating costs. The PIP is subject to market uncertainties, such as the evolution of jet-fuel prices and the possible taxation of carbon emissions. The profitability of the PIP development is investigated and the value of managerial flexibility and timing flexibility are highlighted.The developments of new technologies for commercial aviation involve significant risk for technologists as these programs are often driven by fixed assumptions regarding future airline needs, while being subject to many uncertainties at the technical and market levels. To prioritize these developments, technologists must assess their economic viability even though standard methods used for capital budgeting are not well suited to handle the overwhelming uncertainty surrounding such developments. This research proposes a framework featuring real options to overcome this challenge. It is motivated by three observations: disregarding the value of managerial flexibility undervalues long-term research and development (R&D) programs; windows of opportunities emerge and disappear and manufacturers can derive significant value by exploiting their upside potential; integrating competitive aspects early in the design ensures that development programs are robust with respect to moves by the competition. Real options analyses have been proposed to address some of these points but the adoption has been slow, hindered by constraining frameworks. A panel of academics and practitioners has identified a set of requirements, known as the Georgetown Challenge, that real options analyses must meet to get more traction amongst practitioners in the industry. In a bid to meet some of these requirements, this research proposes a novel methodology, cross-fertilizing techniques from financial engineering, actuarial sciences, and statistics to evaluate and study the timing of technology developments under uncertainty. It aims at substantiating decision making for R&D while having a wider domain of application and an improved ability to handle a complex reality compared to more traditional approaches. The method named FLexible AViation Investment Analysis (FLAVIA) uses first Monte Carlo techniques to simulate the evolution of uncertainties driving the value of technology developments. A non-parametric Esscher transform is then applied to perform a change of probability measure to express these evolutions under the equivalent martingale measure. A bootstrap technique is suggested next to construct new non-weighted evolutions of the technology development value under the new measure. A regression-based technique is finally used to analyze the technology development program and to discover trigger boundaries which help define when the technology development program should be launched. Verification of the method is performed on several canonical examples and indicates good accuracy and competitive execution time. It is applied next to the analysis of a performance improvement package (PIP) development using the Integrated Cost And Revenue Estimation method (i-CARE) developed as part of this research. The PIP can be retrofitted to currently operating turbofan engines in order to mitigate the impact of the aging process on their operating costs. The PIP is subject to market uncertainties, such as the evolution of jet-fuel prices and the possible taxation of carbon emissions. The profitability of the PIP development is investigated and the value of managerial flexibility and timing flexibility are highlighted.Ph.D