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Short-term load forecasting using deep neural networks (DNN)
2017 North American Power Symposium (NAPS), 2017Load forecasting is an important electric utility task for planning resources in Smart grid. This function also aids in predicting the behavior of energy systems in reducing dynamic uncertainties. The efficiency of the entire grid operation depends on accurate load forecasting.
Tareq Hossen +4 more
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EAST-DNN: Expediting architectural SimulaTions using deep neural networks
Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion, 2019A rapid and accurate architectural simulator is a cornerstone for an efficient design-space exploration of computing systems. In this paper, we introduce EAST-DNN, a feed-forward deep neural network, to accelerate architectural simulations. EAST-DNN achieves $> 10^{6}\times$ speedup with an average prediction error of 4.3% over the baseline simulator.
Arko Dutt +4 more
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Deep Neural Networks (DNN) for Day-Ahead Electricity Price Markets
2018 IEEE Electrical Power and Energy Conference (EPEC), 2018This work investigates the application of a multilayered Perceptron (MLP) deep neural network for the day-ahead price forecast of the Iberian electricity market (MIBEL) which serves the mainland areas of the Spain and Portugal. The 3-month and 6-month period of price and energy data are treated as a historical dataset to train and predict the price for
Radhakrishnan Angamuthu Chinnathambi +4 more
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DNN-VolVis: Interactive Volume Visualization Supported by Deep Neural Network
2019 IEEE Pacific Visualization Symposium (PacificVis), 2019In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively ‘reversed’ given the volumetric data and a static volume rendered image under the desired rendering effect.
Fan Hong, Can Liu, Xiaoru Yuan
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SRS-DNN: a deep neural network with strengthening response sparsity
Neural Computing and Applications, 2019Inspired by the sparse mechanism of biological neural systems, an approach of strengthening response sparsity for deep learning is presented in this paper. Firstly, an unsupervised sparse pre-training process is implemented and a sparse deep network is begun to take shape.
Chen Qiao, Bin Gao, Yan Shi
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Deep Neural Networks (DNN) based Sports Balls Classification
2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 2023S.Lakshmi Srikar +5 more
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Proceedings of the 55th Annual Design Automation Conference, 2018
Deep Neural Networks (DNNs) represent the state-of-the-art in many Artificial Intelligence (AI) tasks involving images, videos, text, and natural language. Their ubiquitous adoption is limited by the high computation and storage requirements of DNNs, especially for energy-constrained inference tasks at the edge using wearable and IoT devices.
Shubham Jain +5 more
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Deep Neural Networks (DNNs) represent the state-of-the-art in many Artificial Intelligence (AI) tasks involving images, videos, text, and natural language. Their ubiquitous adoption is limited by the high computation and storage requirements of DNNs, especially for energy-constrained inference tasks at the edge using wearable and IoT devices.
Shubham Jain +5 more
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Intrusion Detection System using Deep Neural Networks (DNN)
2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021V.K Navya +4 more
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Review: DeepFake Detection Techniques using Deep Neural Networks (DNN)
2023 6th International Conference on Advances in Science and Technology (ICAST), 2023Harsh Chotaliya +3 more
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