University of Agder

SFI Offshore Mechatronics

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11 to 20 of 394 Results

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 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.
main.py 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. Python Source Code - 14.2 KB - MD5: d69365b0d4241ba0018aae90f31927d5 Main script file
model.py 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. Python Source Code - 4.0 KB - MD5: a3d78306f675376e4895213660a14255 Model configuration
preprocess_data.py 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. Python Source Code - 5.2 KB - MD5: e006c550ef9aee497f120f172918073f Data preprocessing
PyTools.py 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. Python Source Code - 3.0 KB - MD5: bf4e23a1c30e805ca2d7d0be1c6a98a1 Helper tools
Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Jan 6, 2026 Feodor Svetlanov Konomaev; Mithuss Tharmalingam; Kjetil M. D. Hals, 2025, "Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets", https://doi.org/10.18710/CN2VGX, DataverseNO, V2 Replication Data for the article "Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets" submitted in Physical Review B (2025). The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, whi...
01_README.txt Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Plain Text - 4.0 KB - MD5: 42b31aefa61f60e8884c48f5c0188f41 Description of the dataset.
Fig2.txt Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Plain Text - 119.6 KB - MD5: dd350a7493ec7b1e9ef46ec96b525ac3 The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, which is plotted in Figure 2 in the paper.

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

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 -

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 -

Description of the dataset.

main.py

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.

Python Source Code - 14.2 KB -

Main script file

model.py

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.

Python Source Code - 4.0 KB -

Model configuration

preprocess_data.py

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.

Python Source Code - 5.2 KB -

Data preprocessing

PyTools.py

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.

Python Source Code - 3.0 KB -

Helper tools

Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Jan 6, 2026

Feodor Svetlanov Konomaev; Mithuss Tharmalingam; Kjetil M. D. Hals, 2025, "Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets", https://doi.org/10.18710/CN2VGX, DataverseNO, V2

Replication Data for the article "Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets" submitted in Physical Review B (2025). The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, whi...

01_README.txt

Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Plain Text - 4.0 KB -

Description of the dataset.

Fig2.txt

Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Plain Text - 119.6 KB -

The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, which is plotted in Figure 2 in the paper.

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