The struggle for gender parity (sustainable development goal 5) sustains a controversial debate among authors, leaders, and organizations. Studies connecting social dominance orientation, status threat, and attitudes ...
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Open innovation (OI) has been implemented to develop competitive advantages based on the management of innovation with external players. As such, it is expected that the generalized adoption of OI practices needs to b...
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作者：
Costa, JoanaAmorim, InêsReis, JoãoMelão, NunoDEGEIT
Department of Economics Management Industrial Engineering and Tourism University of AveiroCampus Universitário de Santiago Aveiro 3810-193 Portugal GOVCOPP
Research Unit on Governance Competitiveness and Public Policies Campus Universitário de Santiago Aveiro 3810-193 Portugal INESCTEC
Institute for Systems and Computer Engineering Technology and Science R. Roberto Frias Porto 4200-465 Portugal FEP
Faculty of Economics University of Porto R. Roberto Frias Porto 4200-464 Portugal Department of Industrial Engineering and Management
Faculty of Engineering Lusofona University and EIGeS Campo Grande Lisbon 1749-024 Portugal CISeD
Research Center in Digital Services Polytechnic Institute of Viseu Campus Politécnico Viseu 3504-510 Portugal
With Industry 5.0 already on the horizon, firms need to adapt their strategies to better cater to an increasingly demanding and sustainability-conscious customer base. At the same time, the role of customers has shift...
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This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (...
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ISBN:
(纸本)9781665427883
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter *** this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.
Innovation capabilities are among the main driving sources of export performance;however, the literature on how exploration and exploitation innovation influence export performance in the context of emerging economies...
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This paper address the use of deep learning approaches for visual based navigation in confined underwater environments. State-of-the-art algorithms have shown the tremendous potential deep learning architectures can h...
This paper address the use of deep learning approaches for visual based navigation in confined underwater environments. State-of-the-art algorithms have shown the tremendous potential deep learning architectures can have for visual navigation implementations, though they are still mostly outperformed by classical feature-based techniques. In this work, we apply current state-of-the-art deep learning methods for visual-based robot navigation to the more challenging underwater environment, providing both an underwater visual dataset acquired in real operational mission scenarios and an assessment of state-of-the-art algorithms on the underwater context. We extend current work by proposing a novel pose optimization architecture for the purpose of correcting visual odometry estimate drift using a Visual-Inertial fusion network, consisted of a neural network architecture anchored on an Inertial supervision learning scheme. Our Visual-Inertial Fusion Network was shown to improve results an average of 50% for trajectory estimates, also producing more visually consistent trajectory estimates for both our underwater application scenarios.
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