Feature and viewpoint selection for industrial car assembly

Abstract. Quality assurance programs of today’s car manufacturers show increasing demand for automated visual inspection tasks. A typical example is just-in-time checking of assemblies along production lines. Since high throughput must be achieved, object recognition and pose estimation heavily rely on offline preprocessing stages of available CAD data. In this paper, we propose a complete, universal framework for CAD model feature extraction and entropy index based viewpoint selection that is developed in cooperation with a major german car manufacturer

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hence, various scalable GPs have been developed in the literature in order to improve the scalability while retaining desirable prediction accuracy. This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness. The numerical experiments on two toy examples and five real-world datasets with up to 250K points offer the following findings. In terms of scalability, most of the scalable GPs own a time complexity that is linear to the training size. In terms of capability, the sparse approximations capture the long-term spatial correlations, the local aggregations capture the local patterns but suffer from over-fitting in some scenarios. In terms of controllability, we could improve the performance of sparse approximations by simply increasing the inducing size. But this is not the case for local aggregations. In terms of robustness, local aggregations are robust to various initializations of hyperparameters due to the local attention mechanism. Finally, we highlight that the proper hybrid of global and local scalable GPs may be a promising way to improve both the model capability and scalability for big data.Comment: 25 pages, 15 figures, preprint submitted to KB

A decade of monitoring Atlantic cod Gadus morhua spawning aggregations in Massachusetts Bay using passive acoustics

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Caiger, P. E., Dean, M. J., DeAngelis, A. I., Hatch, L. T., Rice, A. N., Stanley, J. A., Tholke, C., Zemeckis, D. R., & Van Parijs, S. M. A decade of monitoring Atlantic cod Gadus morhua spawning aggregations in Massachusetts Bay using passive acoustics. Marine Ecology Progress Series, 635, (2020): 89-103, doi:10.3354/meps13219.Atlantic cod Gadus morhua populations in the northeast USA have failed to recover since major declines in the 1970s and 1990s. To rebuild these stocks, managers need reliable information on spawning dynamics in order to design and implement control measures; discovering cost-effective and non-invasive monitoring techniques is also favorable. Atlantic cod form dense, site-fidelic spawning aggregations during which they vocalize, permitting acoustic detection of their presence at such times. The objective of this study was to detect spawning activity of Atlantic cod using multiple fixed-station passive acoustic recorders to sample across Massachusetts Bay during the winter spawning period. A generalized linear modeling approach was used to investigate spatio-temporal trends of cod vocalizing over 10 consecutive winter spawning seasons (2007-2016), the longest such timeline of any passive acoustic monitoring of a fish species. The vocal activity of Atlantic cod was associated with diel, lunar, and seasonal cycles, with a higher probability of occurrence at night, during the full moon, and near the end of November. Following 2009 and 2010, there was a general decline in acoustic activity. Furthermore, the northwest corner of Stellwagen Bank was identified as an important spawning location. This project demonstrated the utility of passive acoustic monitoring in determining the presence of an acoustically active fish species, and provides valuable data for informing the management of this commercially, culturally, and ecologically important species.Thanks to Eli Bonnell, Genevieve Davis, Julianne Bonell, Samara Haver, and Eric Matzen for assistance in MARU deployments, Dana Gerlach and Heather Heenehan for help in passive acoustic data analysis, and the NEFSC passive acoustics group for useful discussions. Funding for 2007−2012 passive acoustic surveys was provided by Excelerate Energy and Neptune LNG to Cornell University. Fieldwork for 2013−2015 was funded through the 2013−2014 NOAA Saltonstall-Kennedy grant program (Award No. NA14NMF4270027), and jointly funded by The Nature Conservancy, Massachusetts Division of Marine Fisheries, and the Cabot Family Charitable Foundation. Funding for 2016 SoundTrap data was provided by NOAA’s Ocean Acoustics Program (4 Sanctuaries Project)