Real world vehicle emissions: Using computer vision in instantaneous vehicle emissions modelling

2020 ◽  
Author(s):  
Anna Schroeder ◽  
Adam Boies
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Vehicle Emissions ◽  
Emissions Modelling

scholarly journals Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters

2015 ◽  
Vol 8 (3) ◽  
pp. 2881-2912 ◽  
Author(s):  
J. M. Wang ◽  
C.-H. Jeong ◽  
N. Zimmerman ◽  
R. M. Healy ◽  
D. K. Wang ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Black Carbon ◽  
Time Resolution ◽  
Real World ◽  
Vehicle Emissions ◽  
Particle Number ◽  
Emission Factors ◽  
Air Pollutant ◽  
Automated Identification ◽  
Vehicle Fleet

Abstract. An automated identification and integration method has been developed to investigate in-use vehicle emissions under real-world conditions. This technique was applied to high time resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number, black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg−1 and 7.7 × 1014 kg−1, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25%) contributed significantly to total fleet emissions; 95, 93, 76, and 75% for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter. However, regulatory strategies to more efficiently target multi-pollutants mixtures may be better developed by considering the co-emitted pollutants as well.

scholarly journals Real-World Vehicle Emissions: A Summary of the Twelfth Coordinating Research Council On-Road Vehicle Emissions Workshop

2003 ◽  
Vol 53 (2) ◽  
pp. 152-167 ◽  
Author(s):  
Steven H. Cadle ◽  
Robert A. Gorse, Jr. ◽  
Brent K. Bailey ◽  
Douglas R. Lawson
Keyword(s):  
Real World ◽  
Vehicle Emissions ◽  
Research Council ◽  
Road Vehicle

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

Analysis of emissions from various driving cycles based on real driving measurements obtained in a high-altitude city

2020 ◽  
Vol 234 (6) ◽  
pp. 1563-1571 ◽  
Author(s):  
Meng Lyu ◽  
Xiaofeng Bao ◽  
Yunjing Wang ◽  
Ronald Matthews
Keyword(s):  
High Altitude ◽  
Real World ◽  
Vehicle Emissions ◽  
Control Strategies ◽  
Driving Cycle ◽  
Specific Power ◽  
Test Cycle ◽  
Light Duty ◽  
And Control ◽  
Light Duty Vehicles

Vehicle emissions standards and regulations remain weak in high-altitude regions. In this study, vehicle emissions from both the New European Driving Cycle and the Worldwide harmonized Light-duty driving Test Cycle were analyzed by employing on-road test data collected from typical roads in a high-altitude city. On-road measurements were conducted on five light-duty vehicles using a portable emissions measurement system. The certification cycle parameters were synthesized from real-world driving data using the vehicle specific power methodology. The analysis revealed that under real-world driving conditions, all emissions were generally higher than the estimated values for both the New European Driving Cycle and Worldwide harmonized Light-duty driving Test Cycle. Concerning emissions standards, more CO, NOx, and hydrocarbons were emitted by China 3 vehicles than by China 4 vehicles, whereas the CO2 emissions exhibited interesting trends with vehicle displacement and emissions standards. These results have potential implications for policymakers in regard to vehicle emissions management and control strategies aimed at emissions reduction, fleet inspection, and maintenance programs.

Real-World Vehicle Emissions: A Summary of the Third Annual CRC-APRAC On-Road Vehicle Emissions Workshop

Air & Waste ◽  
1993 ◽  
Vol 43 (8) ◽  
pp. 1084-1090 ◽  
Author(s):  
Steven H. Cadie ◽  
Robert A. Gorse ◽  
Douglas R. Lawson
Keyword(s):  
Real World ◽  
Vehicle Emissions ◽  
Road Vehicle ◽  
The Third

Image Based 3D Modeling and Rendering from Single View Perspective Images

2013 ◽  
pp. 604-620
Author(s):  
S. Mohan ◽  
S. Murali
Keyword(s):  
Computer Vision ◽  
3D Modeling ◽  
Real World ◽  
3D Models ◽  
Edge Length ◽  
Geometric Information ◽  
Single View ◽  
Perspective Image ◽  
Cad Cam ◽  
Cam Software

In computer vision, 3D modeling refers to the process of developing 3D representation of the real world objects with systematic procedure. The 3D models can be built based on geometric information about the object or scene to be modeled using CAD/CAM software. However, this approach needs prior knowledge of the objects in the scene like dimension, size of objects, distance from the object to camera, et cetera. To make the 3D models more photo realistic and convenient, images of the objects can be used to build the 3D models. In this chapter, the authors propose a method to extract 3D model from single view perspective image. The approach is based on edge length and exploiting symmetric objects in the scene. Later, an application of touring into picture is discussed with the proposed method.

Sixth Sense Technology

2016 ◽  
pp. 188-215 ◽  
Author(s):  
Zeenat S. AlKassim ◽  
Nader Mohamed
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Real World ◽  
Digital World ◽  
The Future ◽  
Computer Interaction

In this chapter, the authors discuss a unique technology known as the Sixth Sense Technology, highlighting the future opportunities of such technology in integrating the digital world with the real world. Challenges in implementing such technologies are also discussed along with a review of the different possible implementation approaches. This review is performed by exploring the different inventions in areas similar to the Sixth Sense Technology, namely augmented reality (AR), computer vision, image processing, gesture recognition, and artificial intelligence and then categorizing and comparing between them. Lastly, recommendations are discussed for improving such a unique technology that has the potential to create a new trend in human-computer interaction (HCI) in the coming years.

scholarly journals TCM: Temporal Consistency Model for Head Detection in Complex Videos

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sultan Daud Khan ◽  
Ahmed B. Altamimi ◽  
Mohib Ullah ◽  
Habib Ullah ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Real World ◽  
Experimental Evaluation ◽  
Research Problem ◽  
Temporal Consistency ◽  
Single Image ◽  
Average Precision ◽  
Consistency Model ◽  
Head Detection

Head detection in real-world videos is a classical research problem in computer vision. Head detection in videos is challenging than in a single image due to many nuisances that are commonly observed in natural videos, including arbitrary poses, appearances, and scales. Generally, head detection is treated as a particular case of object detection in a single image. However, the performance of object detectors deteriorates in unconstrained videos. In this paper, we propose a temporal consistency model (TCM) to enhance the performance of a generic object detector by integrating spatial-temporal information that exists among subsequent frames of a particular video. Generally, our model takes detection from a generic detector as input and improves mean average precision (mAP) by recovering missed detection and suppressing false positives. We compare and evaluate the proposed framework on four challenging datasets, i.e., HollywoodHeads, Casablanca, BOSS, and PAMELA. Experimental evaluation shows that the performance is improved by employing the proposed TCM model. We demonstrate both qualitatively and quantitatively that our proposed framework obtains significant improvements over other methods.

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