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11 to 20 of 138,100 Results
oil_field_model.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 4.2 KB - MD5: 3afdf981934adb3916a638c92b3e0ef4
openloop_simulation.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 4.4 KB - MD5: ab8287accb12c0c577c099c750b6910e
Readme.md
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Markdown Text - 155 B - MD5: 0c05268200c7ad6592a1f7d9edf5d792
update_states.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 319 B - MD5: 22c8cc35407cf3e6460003bceedc8911
Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process
Feb 24, 2026 - University of South-Eastern Norway
Hvidsten, Jan Inge; Mirlekar, Gaurav, 2026, "Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process", https://doi.org/10.18710/UFXAVM, DataverseNO, V1
The dataset represents dynamic simulation and estimation data for a CO2 scrubbing process in a bio‑CO2 purification plant, used to design and test a real‑time optimization and Kalman filter–based control framework for solvent flow. Purpose: To estimate unmeasured mass flows (CO2 inlet gas flow and rich solvent flow) in a CO2 scrubber using first‑ a...
00_ReadMe.txt
Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process
Plain Text - 3.4 KB - MD5: dbfc3c3b6015107309c71c89a07b6dcc
DataFile.pdf
Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process
Adobe PDF - 626.5 KB - MD5: 1f3dc4cda3f8cf07b511f22967cf4f43
Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.
Feb 24, 2026 - University of Agder
Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1
The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was...
00_ReadMe.md
Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.
Markdown Text - 5.8 KB - MD5: 1100ca3456432e3de7660367408bf319
Description of the dataset.
00_ReadMe.txt
Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.
Plain Text - 6.5 KB - MD5: e5cab828e3e375849aec71ffd9f6c8a6
Description of the dataset.
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