Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Deep Ensemble"”.
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Cooperative Prediction and Planning Under Uncertainty for Autonomous Robots
… inference models such as Monte Carlo dropout and deep ensemble to probabilistically predict the motion of surrounding objects. Our probabilistic trajectory forecasting model showed improvement over standard deterministic approaches and could handle adverse scenarios such as sensor noise and …
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Risk-aware navigation for UAV digital data collection
… and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and …
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Adaptive systems for DDoS attacks detection and mitigation in IoT networks
… networks, the third objective is the design of a Deep Ensemble Learning with Pruning (DEEPShield) system that integrates CNN and LSTM architectures, optimized through post-training pruning and a novel preprocessing method. This system achieves high detection accuracy with low resource demand, …
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Smart Quality Assurance System for Additive Manufacturing using Data-driven based Parameter-Signature-Quality Framework
… effects simulated for the LPBF process. 3. Deep-ensemble-based neural networks with active learning for predicting and recommending a set of optimal process parameter values were developed to optimize optimal process parameter values for achieving the inverse design of desired mechanical …
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Bridging Machine Learning for Smart Grid Applications
… develops a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and a multivariate-linear regressor as a meta-learner. Historical measurements and states are …
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Learning-based crop management optimization using multi-stream convolutional neural networks
Improving crop management is an essential step towards solving the food security challenge. Despite the advances in precision agriculture, new methods are needed to create decision-support systems to help farmers increase productivity while accounting for environmental impacts and financial risks. …