Abstract
The rapid expansion of Asian hornets poses a high threat for the honey bee survival, as these invaders pray on them. Furthermore, they also pose a threat to people who are allergic, whose sting can lead to death. This study proposes a Decision Support System that uses Computer Vision techniques to automatically detect signs of Vespa velutina through images from GPS equipped camera. The goal of the system is to provide timely information about the presence of these invaders, allowing park managers and beekeepers to act quickly in removing the Vespidae. The proposed methodology obtained an 85% accuracy in the detection of V. velutina using the Mask RCNN architecture, enabling the system to perform detection at 3 FPS. © 2020 MIR Labs.

Abstract

Abstract
The industrial growth of the last decades created a need for intelligent and autonomous systems that can propose solutions to scheduling problems efficiently. The job shop scheduling problem (JSSP) is the most common formulation of these real-world scheduling problems and can be found in complex fields, such as transportation or industrial assemblies, where the ability to quickly adapt to unforeseen events is critical. Using the Markov decision process mathematical framework, this paper details a formulation of the JSSP as a reinforcement learning (RL) problem. The formulation is part of a proposal of a novel environment where RL agents can interact with JSSPs that is detailed on this paper, including a comprehensive explanation of the design process, the decisions that were made and the key lessons learnt. Considering the need for better scheduling approaches on modern manufacturing environments, the limitations that current techniques have and the major breakthroughs that are being made on the field of machine learning, the environment proposed on this paper intends to be a major contribution to the JSSP landscape, enabling academics from different areas to focus on the development of new algorithms and effortlessly test them on academic and real-world benchmarks. © 2020 MIR Labs.

Abstract

Abstract

Abstract