| Title | Energy Efficient Fill-Level Monitoring for Recycling Glass Containers |
|---|---|
| ID_Doc | 10503 |
| Authors | Markovic, N; Raza, A; Wolf, T; Romahn, P; Zinn, AH; Kolossa, D |
| Title | Energy Efficient Fill-Level Monitoring for Recycling Glass Containers |
| Year | 2024 |
| Published | |
| Abstract | Monitoring the fill levels of glass containers is important for smart cities, to simultaneously save energy and traffic by preventing unneeded pick-up routes, and to support the circular economy by ensuring that containers are always available for new recycling glass. Here, we present a novel and highly energy-efficient method for reliable monitoring of glass container fill levels. This was achieved by framing the problem as a classification problem of the container fill state, and by using a dataset consisting of over 100,000 accelerometer recordings from 106 different containers for training hybrid models that combine the best aspects of deep learning and probabilistic inference. We propose the use of hybrid models, via optimal sequential decision making based on a probabilistic output of the deep neural network. With this approach, the overall accuracy increases by more than 10% while preventing sudden changes in state prediction. Finally, we have optimized the network efficiency. For this purpose, we investigated four techniques of explainable artificial intelligence methods for time series to investigate which feature are important for classification. The final results show that this allows for training a classification model of roughly comparable performance by using only 5% of the input features, which leads to an additional improvement of 97 % in terms of energy consumption of the smart sensor. |
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