| dc.date.accessioned | 2020-03-30T07:43:05Z | |
| dc.date.available | 2020-03-30T07:43:05Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | http://hdl.handle.net/10852/74263 | |
| dc.language.iso | en | en_US |
| dc.relation.haspart | Paper I: Brandsæter, A. and Vanem, E. (2018). Ship speed prediction based on full scale sensor measurements of shaft thrust and environmental conditions. Ocean Engineering, 162:316 – 330. DOI: 10.1016/j.oceaneng.2018.05.029. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.oceaneng.2018.05.029 | |
| dc.relation.haspart | Paper II: Brandsæter, A., Vanem, E., and Glad, I. K. (2019). Efficient on-line anomaly detection for ship systems in operation. Expert Systems with Applications, 121:418–437. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.eswa.2018.12.040 | |
| dc.relation.haspart | Paper III: Vanem, E. and Brandsæter, A. (2019). Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine. Journal of Marine Engineering & Technology, 1 – 18. DOI: 10.1080/20464177.2019.1633223. The article is included in the thesis. Also available at: https://doi.org/10.1080/20464177.2019.1633223 | |
| dc.relation.haspart | Paper IV: Brandsæter, A. and Knutsen, K. (2018). Towards a framework for assurance of autonomous navigation systems in the maritime industry. In Safety and Reliability–Safe Societies in a Changing World: Proceedings of ESREL 2018, (pp. 449-457). CRC Press. The article is included in the thesis. | |
| dc.relation.haspart | Paper V: Brandsæter, A. and Glad, I. K. (2019). Explainable artificial intelligence: How subsets of the training data affect a prediction. Submitted for publication. To be published. The paper is not available in DUO awaiting publishing. | |
| dc.relation.uri | https://doi.org/10.1016/j.oceaneng.2018.05.029 | |
| dc.relation.uri | https://doi.org/10.1016/j.eswa.2018.12.040 | |
| dc.relation.uri | https://doi.org/10.1080/20464177.2019.1633223 | |
| dc.title | Data-driven methods for multiple sensor streams, with applications in the maritime industry | en_US |
| dc.type | Doctoral thesis | en_US |
| dc.creator.author | Brandsæter, Andreas | |
| dc.identifier.urn | URN:NBN:no-77369 | |
| dc.type.document | Doktoravhandling | en_US |
| dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/74263/1/PhD-Brandsaeter-2020.pdf | |