Affective Temperature Control in Food SMEs using Artificial Neural Network
| Autor: | Tsuyoshi Okayama, Atris Suyantohadi, Mirwan Ushada, Nafis Khuriyati |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Temperature control
Workstation Artificial neural network business.industry Computer science 020209 energy 02 engineering and technology Industrial engineering Set point law.invention Research objectives Set (abstract data type) Light intensity Artificial Intelligence law 0202 electrical engineering electronic engineering information engineering Artificial intelligence business |
| Zdroj: | Applied Artificial Intelligence. 31:555-567 |
| ISSN: | 1087-6545 0883-9514 |
| DOI: | 10.1080/08839514.2017.1390327 |
| Popis: | This paper highlights modeling affective temperature control in food small and medium-sized enterprises (SMEs). Modeling defined that workstation temperature set point could be controlled based on worker heart rate and workstation environment using Artificial Neural Network (ANN). The research objectives were: 1) to propose modeling affective temperature control in food SMEs based on heart rate and workstation environment; and 2) to develop an ANN model for predicting workstation temperature set point. Training and validation data were collected from six food SMEs in Yogyakarta Special Region, Indonesia. The data of temperature set points were verified using a simulated confined room. The inputs of the ANN model were worker heart rate, workstation temperature, relative humidity distribution and light intensity. The output was temperature set point. Research results concluded satisfactory performance of ANN. The model could be used to provide environmental ergonomics in food SMEs. |
| Databáze: | OpenAIRE |
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