@InCollection{MaximianoSantShig:2022:ArNeNe,
               author = "Maximiano, Renato de Sousa and Santiago J{\'u}nior, Valdivino 
                         Alexandre de and Shiguemori, Elcio Hideiti",
                title = "Artificial Neural Networks to Analyze Energy Consumption and 
                         Temperature of UAV On-Board Computers Executing Algorithms for 
                         Object Detection",
            booktitle = "Intelligent Systems: BRACIS 2022",
            publisher = "Springer",
                 year = "2022",
               editor = "Xavier-Junior, J. C. and Rios, R. A",
                pages = "523--538",
                 note = "Lecture Notes in Computer Science, 13654",
             keywords = "UAVs, artificial neural networks, deep learning, object detection, 
                         energy consumption, temperature.",
             abstract = "When incorporating object detection models into unmanned aerial 
                         vehicles (UAVs) on-board computers, two aspects are relevant 
                         aspects. Firstly, the energy consumption required by the computer 
                         on board the UAV during the mission, since low-cost electric UAVs 
                         currently have low flight autonomy. Moreover, during the mission, 
                         the computers processor may suffer overheating caused by the 
                         running algorithm, which may directly impair the continuity of a 
                         given task or burn the computer. In this study, we aim to estimate 
                         the energy consumption and make temperature predictions of a 
                         computer embedded in UAVs for missions involving object detection. 
                         We propose a method, Analyzing Energy Consumption and Temperature 
                         of On-board computer of UAVs via Neural Networks (ETOUNN), which 
                         uses a multilayer perceptron (MLP) network to estimate the energy 
                         consumption and a long short-term memory (LSTM) network for 
                         predicting temperature. Our experiment relied on a Raspberry Pi 4 
                         8 GB computer running nine popular models of object detectors 
                         (deep neural networks): eight of which are pre-trained models of 
                         the YOLO family, and one Mask R-CNN network. Regarding energy 
                         consumption, we compared our method to multivariate and simple 
                         regression-based on two metrics: mean squared error (MSE) and the 
                         R2 regression score function. As for temperature prediction and 
                         considering the same metrics, ETOUNN was compared to the 
                         Autoregressive Integrated Moving Average (ARIMA), the Neural Basis 
                         Expansion Analysis for interpretable Time Series forecasting 
                         (N-BEATS), and a gated recurrent unit (GRU) network. In both 
                         comparisons, our method presented superior performances, showing 
                         that it is a promising strategy.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-3-031-21689-3_37",
                  url = "http://dx.doi.org/10.1007/978-3-031-21689-3_37",
             language = "en",
        urlaccessdate = "09 maio 2024"
}