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Found 9 papers, 5 papers with code
Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Medical Materials
The circular economy paradigm is gaining interest as a solution to reduce both material supply uncertainties and waste generation.
Visual Material Characteristics Learning for Circular Healthcare
The linear take-make-dispose paradigm at the foundations of our traditional economy is proving to be unsustainable due to waste pollution and material supply uncertainties.
Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations
This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time.
Digital Twins for Marine Operations: A Brief Review on Their Implementation
While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature.
Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection
Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and another dataset for marine debris detection.
Lazy FSCA for Unsupervised Variable Selection
In this paper a "lazy" implementation of the FSCA algorithm (L-FSCA) is proposed, which, although not equivalent to FSCA due to the absence of submodularity, has the potential to yield comparable performance while being up to an order of magnitude faster to compute.
Material Measurement Units for a Circular Economy: Foundations through a Review
Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product.
Recovery of Linear Components: Reduced Complexity Autoencoder Designs
Reducing dimensionality is a key preprocessing step in many data analysis applications to address the negative effects of the curse of dimensionality and collinearity on model performance and computational complexity, to denoise the data or to reduce storage requirements.
An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training
Motivated by the potential for parallel implementation of batch-based algorithms and the accelerated convergence achievable with approximated second order information a limited memory version of the BFGS algorithm has been receiving increasing attention in recent years for large neural network training problems.
