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Subject: Computer and Information Science Subject: Engineering

1 to 10 of 27 Results

DatasetMathematical model for evaluating bicarbonate and lactate kinetics in metformin-associated lactic acidosis May 8, 2024 Pietribiasi, Mauro, 2024, "Mathematical model for evaluating bicarbonate and lactate kinetics in metformin-associated lactic acidosis", https://doi.org/10.18150/GZRAXA, RepOD, V1
DatasetResearch data for: "Semi-Active Control of a Shear Building based on Reinforcement Learning: Robustness to measurement noise and model error" Mar 20, 2024 Jedlińska, Aleksandra; Pisarski, Dominik; Mikułowski, Grzegorz; Błachowski, Bartłomiej; Jankowski, Łukasz, 2024, "Research data for: "Semi-Active Control of a Shear Building based on Reinforcement Learning: Robustness to measurement noise and model error"", https://doi.org/10.18150/BYAOT8, RepOD, V1
DatasetResearch data for: "Semi-active structural control using viscous dampers and reinforcement learning" Mar 20, 2024 Jedlińska, Aleksandra; Pisarski, Dominik; Mikułowski, Grzegorz; Błachowski, Bartłomiej; Jankowski, Łukasz, 2024, "Research data for: "Semi-active structural control using viscous dampers and reinforcement learning"", https://doi.org/10.18150/EVOYDG, RepOD, V1
DatasetResearch data for: A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations Mar 18, 2024 Ostrowski, Mariusz; Błachowski, Bartłomiej; Wójcik, Bartosz; Żarski, Mateusz; Tauzowski, Piotr; Jankowski, Łukasz, 2024, "Research data for: A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations", https://doi.org/10.18150/GYA7O7, RepOD, V1
DatasetData of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of deep learning Feb 24, 2024 - DataverseNutrition and Food Technology Przybył, Krzysztof, 2024, "Data of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of deep learning", https://doi.org/10.18150/J6R7UY, RepOD, V2
DatasetResearch on development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning. Feb 15, 2024 Skowron, Maciej, 2024, "Research on development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning.", https://doi.org/10.18150/XXY49N, RepOD, V1
DatasetResearch on fault diagnostics of permanent magnet synchronous motors based on deep neural networks and transfer learning. Feb 15, 2024 Skowron, Maciej, 2024, "Research on fault diagnostics of permanent magnet synchronous motors based on deep neural networks and transfer learning.", https://doi.org/10.18150/ZX3HB2, RepOD, V1
DatasetAnalysis of the precision of PMSM short-circuit detection systems using transfer learning of deep convolutional networks Feb 15, 2024 Skowron, Maciej, 2024, "Analysis of the precision of PMSM short-circuit detection systems using transfer learning of deep convolutional networks", https://doi.org/10.18150/TTZMEK, RepOD, V1
DatasetImpact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives Feb 15, 2024 Skowron, Maciej, 2024, "Impact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives", https://doi.org/10.18150/CPOAEQ, RepOD, V1
DatasetDiagnosis of stator winding and permanent magnet faults of PMSM drive using shallow and deep neural networks Feb 15, 2024 Skowron, Maciej, 2024, "Diagnosis of stator winding and permanent magnet faults of PMSM drive using shallow and deep neural networks", https://doi.org/10.18150/HQ4X9V, RepOD, V1

Mathematical model for evaluating bicarbonate and lactate kinetics in metformin-associated lactic acidosis

May 8, 2024

Pietribiasi, Mauro, 2024, "Mathematical model for evaluating bicarbonate and lactate kinetics in metformin-associated lactic acidosis", https://doi.org/10.18150/GZRAXA, RepOD, V1

Research data for: "Semi-Active Control of a Shear Building based on Reinforcement Learning: Robustness to measurement noise and model error"

Mar 20, 2024

Jedlińska, Aleksandra; Pisarski, Dominik; Mikułowski, Grzegorz; Błachowski, Bartłomiej; Jankowski, Łukasz, 2024, "Research data for: "Semi-Active Control of a Shear Building based on Reinforcement Learning: Robustness to measurement noise and model error"", https://doi.org/10.18150/BYAOT8, RepOD, V1

Research data for: "Semi-active structural control using viscous dampers and reinforcement learning"

Mar 20, 2024

Jedlińska, Aleksandra; Pisarski, Dominik; Mikułowski, Grzegorz; Błachowski, Bartłomiej; Jankowski, Łukasz, 2024, "Research data for: "Semi-active structural control using viscous dampers and reinforcement learning"", https://doi.org/10.18150/EVOYDG, RepOD, V1

Research data for: A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations

Mar 18, 2024

Ostrowski, Mariusz; Błachowski, Bartłomiej; Wójcik, Bartosz; Żarski, Mateusz; Tauzowski, Piotr; Jankowski, Łukasz, 2024, "Research data for: A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations", https://doi.org/10.18150/GYA7O7, RepOD, V1

Thumbnail of dataset Data of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of deep learning

Data of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of deep learning

Feb 24, 2024 - Nutrition and Food Technology

Przybył, Krzysztof, 2024, "Data of analysis of the influence of microparticle morphology on the qualitative state of spray-dried fruit with the use of deep learning", https://doi.org/10.18150/J6R7UY, RepOD, V2

Thumbnail of dataset Research on development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning.

Research on development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning.

Feb 15, 2024

Skowron, Maciej, 2024, "Research on development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning.", https://doi.org/10.18150/XXY49N, RepOD, V1

Thumbnail of dataset Research on fault diagnostics of permanent magnet synchronous motors based on deep neural networks and transfer learning.

Research on fault diagnostics of permanent magnet synchronous motors based on deep neural networks and transfer learning.

Feb 15, 2024

Skowron, Maciej, 2024, "Research on fault diagnostics of permanent magnet synchronous motors based on deep neural networks and transfer learning.", https://doi.org/10.18150/ZX3HB2, RepOD, V1

Thumbnail of dataset Analysis of the precision of PMSM short-circuit detection systems using transfer learning of deep convolutional networks

Analysis of the precision of PMSM short-circuit detection systems using transfer learning of deep convolutional networks

Feb 15, 2024

Skowron, Maciej, 2024, "Analysis of the precision of PMSM short-circuit detection systems using transfer learning of deep convolutional networks", https://doi.org/10.18150/TTZMEK, RepOD, V1

Thumbnail of dataset Impact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives

Impact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives

Feb 15, 2024

Skowron, Maciej, 2024, "Impact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives", https://doi.org/10.18150/CPOAEQ, RepOD, V1

Diagnosis of stator winding and permanent magnet faults of PMSM drive using shallow and deep neural networks

Feb 15, 2024

Skowron, Maciej, 2024, "Diagnosis of stator winding and permanent magnet faults of PMSM drive using shallow and deep neural networks", https://doi.org/10.18150/HQ4X9V, RepOD, V1

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