Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
| Autor: | Simon Pearson, Ronald Bickerton, George Onoufriou, Georgios Leontidis |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning General Computer Science Computer science Distributed computing Machine Learning (stat.ML) 02 engineering and technology G700 Artificial Intelligence Domain (software engineering) Machine Learning (cs.LG) 020901 industrial engineering & automation Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Structure (mathematical logic) Signal processing business.industry Node (networking) Deep learning General Engineering G400 Computer Science Recurrent neural network Computer Science - Distributed Parallel and Cluster Computing Software deployment Control system 020201 artificial intelligence & image processing Artificial intelligence Distributed Parallel and Cluster Computing (cs.DC) G760 Machine Learning business |
| DOI: | 10.48550/arxiv.1906.01600 |
| Popis: | Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK National Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time. Comment: 25 pages, 13 figures, 4 tables, 2 appendices |
| Databáze: | OpenAIRE |
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