Towards Deep Learning in Industrial Applications Taking Advantage of Service-Oriented Architectures
| Autor: | Jan Lehr, Clemens Briese, Katarina Maurer, Marian Schlüter, Jörg Krüger |
|---|---|
| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
computer.internet_protocol Computer science business.industry Deep learning Distributed computing 02 engineering and technology Service-oriented architecture Reverse logistics Convolutional neural network Industrial and Manufacturing Engineering Identification (information) 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Artificial Intelligence Software deployment Artificial intelligence Information flow (information theory) business computer Digitization |
| Popis: | In reverse logistics, identification of products is necessary but due to uninterpretable markers information flow is not always consistent. Recent image-based recognition developments using Convolutional Neural Networks are promising but collecting required labeled data is time- and cost-intensive. To allow a quick deployment and usage of such systems, we present a conceptual service-oriented architecture that enables Deep Learning recognition systems to be used with initially small but growing data sets, as with every usage training data expands on run-time. An identification problem is reduced to digitization and labeling of data and as a side effect digital knowledge retention can be established in companies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|