Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues ...
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Nowadays, environmental pollution levels are a worldwide concern due to the risks for people's health. The demand for cutting-edge technologies to determine the sensible areas where pollutants are present in dange...
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ISBN:
(数字)9781728166360
ISBN:
(纸本)9781728166377
Nowadays, environmental pollution levels are a worldwide concern due to the risks for people's health. The demand for cutting-edge technologies to determine the sensible areas where pollutants are present in dangerous concentrations is ever increasing, along with the stringent requirements of avoiding the exposure of human operators to toxic substance. For this reason, the proposed work presents a novel method for the surveillance and monitoring of large areas by means of unmanned aerial vehicles. The work encompasses both hardware and software integration of a low-power multi-sensor platform hosted by the aerial vehicle. The platform comprises a multi-spectral camera, digital optics sensors and toxic/pollutant gas concentration detectors coupled with low-power computing and wireless communication capabilities. Furthermore, a base station platform is implemented to collect data and to provide the analytical tools that facilitate the responsible authorities in the decision of intervention.
Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues ...
详细信息
ISBN:
(数字)9788395541674
ISBN:
(纸本)9781728141473
Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins's Q(λ), and Sarsa(λ), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins's Q(λ) performs best in minimizing the total tardiness of the scheduling process.
The LEXIS project (Large-scale EXecution for Industry & Society, H2020 GA825532) provides a platform for optimised execution of Cloud-HPC workflows, reducing computation time and increasing energy efficiency. The ...
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The LEXIS project (Large-scale EXecution for Industry & Society, H2020 GA825532) provides a platform for optimised execution of Cloud-HPC workflows, reducing computation time and increasing energy efficiency. The system will rely on advanced, distributed orchestration solutions (Atos YSTIA Suite, with Alien4Cloud and Yorc, based on TOSCA), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximising efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing). LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-computing use cases in Industry (SMEs) and Science: i) Simulations of complex turbomachinery and gearbox systems in Aeronautics, ii) Tsunami simulations and earthquake loss assessments which are time-constrained to enable immediate warnings and to support well-informed decisions, and iii) Weather and Climate simulations where massive amounts of in-situ data are assimilated to improve forecasts. A user-friendly LEXIS web portal, as a unique entry point, will provide access to data as well as workflow-handling and remote visualisation functionality. As part of its back-end, LEXIS builds an elaborate system for the handling of input, intermediate and result data. At its core, a Distributed Data Infrastructure (DDI) ensures the availability of LEXIS data at all participating HPC sites, which will be federated with a common LEXIS Authentication and Authorisation Infrastructure (with unified security model, user database and policies). The DDI leverages best of breed data-management solutions from EUDAT, such as B2SAFE (based on iRODS) and B2HANDLE. REST APIs on top of it will ensure a smooth interaction with LEXIS workflows and the orchestration layer. Last, but not least, the DDI will provide functionalities for Research Data Management following the FAIR principles ("Findable, Interoperable, Accessible, Reusable"),
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