Generation of Vessel Track Characteristics Using a Conditional Generative Adversarial Network (CGAN)

References

• Arjovsky, M., S. Chintala, and L. Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia, PMLR.
   Google Scholar
• Bao, J., D. Chen, F. Wen, H. Li, and G. Hua. 2017. CVAE-Gan: Fine-grained image generation through asymmetric training. In 2017 IEEE International Conference on Computer Vision (ICCV), October. doi:10.1109/iccv.2017.299.
   Google Scholar
• Brenninkmeijer, B. 2019. On the generation and evaluation of tabular data using GANs. thesis, Master Thesis Data Science.
   Google Scholar
• Campbell, J. 2021. Machine learning literacy and applications in defence and security. Defence research and development Canada - scientific report. November.
   Google Scholar
• Campbell, J., M. Ferreira, and A. Isenor. 2023. Developing generative adversarial networks (GANs) for the generation of synthetic vessel movement data. Defence research and development Canada - scientific report. December.
   Google Scholar
• Campbell, J. N. A., A. W. Isenor, and M. Dais Ferreira. 2022. Detection of invalid AIS messages using machine learning techniques. Procedia Computer Science 205:229–29. doi:10.1016/j.procs.2022.09.024.
   Google Scholar
• Chen, L., S. Dai, C. Tao, D. Shen, Z. Gan, H. Zhang, Y. Zhang, and L. Carin. 2020. Adversarial text generation via feature-mover’s distance. arXiv.org August 12, 2020. https://arxiv.org/abs/1809.06297.
   Google Scholar
• Chen, X., Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. 2016. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems, ed. D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, and I. Guyon, 2180–2188. Red Hook, NY, USA: Curran Associates, Inc.
   Google Scholar
• Esteban, C., S. L. Hyland, and G. Rätsch. 2017. Real-valued (Medical) time series generation with recurrent conditional GANs. ArXiv abs/1706.02633.
   Google Scholar
• Gao, M., and G.-Y. Shi. 2020. Ship collision avoidance anthropomorphic decision-making for structured learning based on AIS with SEQ-CGAN. Ocean Engineering 217 (December):107922. doi:10.1016/j.oceaneng.2020.107922.
   Web of Science ®Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative Adversarial Nets. Advances in Neural Information Processing Systems 27 63:139–144.
   Google Scholar
• Gupta, A., J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi. 2018. Social GAN: Socially acceptable trajectories with generative adversarial networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2255–64.
   Google Scholar
• Hazra, T., and K. Anjaria. 2022. Applications of game theory in deep learning: A survey. Multimedia Tools and Applications 81 (6):8963–94. doi:10.1007/s11042-022-12153-2.
   PubMed Web of Science ®Google Scholar
• Jin, G., Q. Wang, X. Zhao, Y. Feng, Q. Cheng, and J. Huang. 2019. Crime-Gan: A context-based sequence generative network for crime forecasting with adversarial loss. In 2019 IEEE International Conference on Big Data (Big Data), December. doi:10.1109/bigdata47090.2019.9006388.
   Google Scholar
• Kosaraju, V., A. Sadeghian, R. Martín-Martín, S. H. R. Ian Reid, and S. Savarese. 2019. Social-Bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. arXiv.org July 17, 2019. https://arxiv.org/abs/1907.03395.
   Google Scholar
• Liang, K., S. Zhou, M. Liu, Y. Liu, W. Tu, Y. Zhang, L. Fang, Z. Liu, and X. Liu. 2024. Hawkes-enhanced spatial-temporal hypergraph contrastive learning based on criminal correlations. Proceedings of the AAAI Conference on Artificial Intelligence 38 (8):8733–41. doi:10.1609/aaai.v38i8.28719.
   Google Scholar
• Liao, W., K. Hu, M. Ying Yang, and B. Rosenhahn. 2022. Text to Image Generation with Semantic-Spatial Aware Gan. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June. doi:10.1109/cvpr52688.2022.01765.
   Google Scholar
• Li, X., K. Mao, F. Lin, and Z. Feng. 2023. Feature-aware conditional gan for category text generation. arXiv.org. Accessed August 2, 2023. https://arxiv.org/abs/2308.00939.
   Google Scholar
• Liu, S., H. Liu, H. Bi, and T. Mao. 2020. CoL-GAN: Plausible and collision-less trajectory prediction by attention-based GAN. IEEE Access 8:101662–71. doi:10.1109/ACCESS.2020.2987072
   Web of Science ®Google Scholar
• Liu, Y., R. Yu, S. Zheng, E. Zhan, and Y. Yue. 2019. NAOMI: Non-autoregressive multiresolution sequence imputation. In Guide Proceedings. Accessed December 1, 2019. https://dl.acm.org/doi/10.5555/3454287.3455295.
   Google Scholar
• Lu, X., M. He, J. Ma, H. Wang, and Y. Shao. 2023. Vessel Trajectory Prediction with the Introduction of Channel Information. In 2023 7th International Conference on Transportation Information and Safety (ICTIS). Accessed August 4, 2023. doi:10.1109/ictis60134.2023.10243977.
   Google Scholar
• MarineCadastre.gov. 2024. “Vessel Traffic Data.” MarineCadastre.Gov | Vessel Traffic Data. Accessed February 29, 2024. https://marinecadastre.gov/AIS/.
   Google Scholar
• McArthur, B. A., and A. W. Isenor. 2021. Applying spatial mutual information to AIS DATA. The Journal of Navigation.” Cambridge Core 75 (1):95–105. Accessed October 1, 2021. doi:10.1017/s0373463321000734.
   Web of Science ®Google Scholar
• Mirza, M., and S. Osindero. 2014. Conditional Generative Adversarial Nets.” arXiv.org, Accessed November 6, 2014. https://arxiv.org/abs/1411.1784.
   Google Scholar
• Mogren, O. 2016. C-RNN-Gan: Continuous recurrent neural networks with adversarial training.” arXiv.org, Accessed November 29, 2016. https://arxiv.org/abs/1611.09904.
   Google Scholar
• Nan, G., H. Xue, W. Shao, S. Zhao, K. Kai Qin, A. Prabowo, M. Saiedur Rahaman, and F. D. Salim. 2022. Generative adversarial networks for spatio-temporal data: A Survey. ACM Transactions on Intelligent Systems and Technology 13 (2):1–25, February 6. doi:10.1145/3474838.
   Web of Science ®Google Scholar
• Nielsen, M. A. 2015. Neural networks and deep learning. Determination Press. http://neuralnetworksanddeeplearning.com/.
   Google Scholar
• NOAA. 2024. AIS data for 2020. NOAA office for coastal management. Accessed February 29, 2024. https://coast.noaa.gov/htdata/CMSP/AISDataHandler/2020/index.html.
   Google Scholar
• OpenAI. 2024a. Dall‧·E 2. Dall‧e 2. Accessed February 29, 2024a. https://openai.com/dall-e-2.
   Google Scholar
• OpenAI. 2024b. Introducing Chatgpt. Introducing ChatGPT. Accessed February 29, 2024b. https://openai.com/blog/chatgpt.
   Google Scholar
• Radford, A., L. Metz, and S. Chintala. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv.org, Accessed January 7, 2016. https://arxiv.org/abs/1511.06434.
   Google Scholar
• Sadeghian, A., V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese. 2019. SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.
   Google Scholar
• Saxena, D., and J. Cao. 2021. Generative adversarial networks (Gans). ACM Computing Surveys 54 (3):1–42. doi:10.1145/3446374.
   Web of Science ®Google Scholar
• Scikit-learn developers. 2024. Sklearn.Preprocessing.powertransformer. scikit. Accessed February 29, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html.
   Google Scholar
• SDMetrics. 2024a. “BoundaryAdherence.” BoundaryAdherence - SDMetrics. Accessed February 29, 2024a. https://docs.sdv.dev/sdmetrics/metrics/metrics-glossary/boundaryadherence.
   Google Scholar
• SDMetrics. 2024b. “CorrelationSimilarity.” CorrelationSimilarity - SDMetrics. Accessed February 29, 2024b. https://docs.sdv.dev/sdmetrics/metrics/metrics-glossary/correlationsimilarity.
   Google Scholar
• SDMetrics. 2024c. Kscomplement. KSComplement - SDMetrics. Accessed February 29, 2024c. https://docs.sdv.dev/sdmetrics/metrics/metrics-glossary/kscomplement.
   Google Scholar
• SDMetrics. 2024d. “NewRowsynthesis.” NewRowSynthesis - SDMetrics. Accessed February 29, 2024d. https://docs.sdv.dev/sdmetrics/metrics/metrics-glossary/newrowsynthesis.
   Google Scholar
• SDMetrics. 2024e. What’s included? What’s included? - SDMetrics. Accessed February 29, 2024e. https://docs.sdv.dev/sdmetrics/reports/quality-report/whats-included.
   Google Scholar
• Syms, M. S., A. W. Isenor, B. Chivari, A. DeBaie, A. Hogue, and B. Glessing. 2021. Building a maritime picture in the era of big Data: The development of the geospatial communication Interface+. In 2021 International Conference on Military Communication and Information Systems (ICMCIS). Accessed May 4, 2021. doi:10.1109/icmcis52405.2021.9486392.
   Google Scholar
• Tao, M., H. Tang, F. Wu, X. Jing, B.-K. Bao, and C. Xu. 2022. DF-Gan: A simple and effective baseline for text-to-image synthesis. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June. Accessed February 29, 2024. doi:10.1109/cvpr52688.2022.01602.thispersondoesnotexist.com. https://thispersondoesnotexist.com/.
   Google Scholar
• Yonghong, L., X. Cai, Y. Zhang, J. Xu, and X. Yuan. 2018. Multivariate Time Series Imputation with Generative Adversarial Networks. Neural Information Processing Systems 31:1603–1614.
   Google Scholar
• Yoon, J., D. Jarrett, and M. van der Schaar. 2019. Time-series Generative Adversarial Networks. Neural Information Processing Systems 32:5508–5518.
   Google Scholar
• Yu, H., Z. Li, G. Zhang, P. Liu, and J. Wang. 2020. Extracting and predicting taxi hotspots in spatiotemporal dimensions using conditional generative adversarial neural networks. IEEE Transactions on Vehicular Technology 69 (4):3680–92. doi:10.1109/TVT.2020.2978450
   Web of Science ®Google Scholar
• Zhang, B., S. Gu, Z. Bo, J. Bao, D. Chen, B. Guo, Y. Wang, and F. Wen. 2022. StyleSwin: Transformer-Based GAN for High-Resolution Image Generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, New Orleans, LA, USA.
   Google Scholar
• Zhang, W., W. Jiang, Q. Liu, and W. Wang. 2023. AIS data repair Model based on generative adversarial network. Reliability Engineering & System Safety 240 (December):109572. doi:10.1016/j.ress.2023.109572
   Web of Science ®Google Scholar