1,625 research outputs found
Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach
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A comprehensive review of renewables and electric vehicles hosting capacity in active distribution networks
© Copyright 2023 The Author(s). The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639
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