{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:08:09Z","timestamp":1769846889828,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China of QIANG FU","doi-asserted-by":"publisher","award":["62106283"],"award-info":[{"award-number":["62106283"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.<\/jats:p>","DOI":"10.3390\/e25050781","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T04:29:01Z","timestamp":1683779341000},"page":"781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["IH-TCGAN: Time-Series Conditional Generative Adversarial Network with Improved Hausdorff Distance for Synthesizing Intention Recognition Data"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7003-4722","authenticated-orcid":false,"given":"Siyuan","family":"Wang","sequence":"first","affiliation":[{"name":"Air Defense and Antimissile School, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[{"name":"Air Defense and Antimissile School, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"Air Defense and Antimissile School, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0962-0671","authenticated-orcid":false,"given":"Yafei","family":"Song","sequence":"additional","affiliation":[{"name":"Air Defense and Antimissile School, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"given":"Jiayi","family":"Liu","sequence":"additional","affiliation":[{"name":"Air Defense and Antimissile School, Air Force Engineering University, Xi\u2019an 710051, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_2","unstructured":"Yoon, J., Jarrett, D., and Van der Schaar, M. (2019, January 8\u201314). Time-series generative adversarial networks. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Asre, S., and Anwar, A. (2022). Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network. Electronics, 11.","DOI":"10.3390\/electronics11030355"},{"key":"ref_4","unstructured":"Snow, D. (2023, March 20). MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks. Available online: https:\/\/ssrn.com\/abstract=3616557."},{"key":"ref_5","unstructured":"Esteban, C., Hyland, S.L., and R\u00e4tsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. arXiv."},{"key":"ref_6","unstructured":"Wang, S., Rudolph, C., Nepal, S., Grobler, M., and Chen, S. (2020). Artificial Neural Networks and Machine Learning\u2013ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, 15\u201318 September 2020, Proceedings, Part I 29, Springer International Publishing."},{"key":"ref_7","unstructured":"Paul, J., Michael, B.-S., Pedro, M., Shubham, K., Rajbir, S.N., Valentin, F., Jan, G., and Tim, J. (2021). PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. arXiv."},{"key":"ref_8","first-page":"8098","article-title":"Conditional loss and deep euler scheme for time series generation","volume":"36","author":"Remlinger","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_9","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K. (2019). Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, 17\u201319September 2019, Proceedings, Part IV, Springer International Publishing."},{"key":"ref_10","unstructured":"Du, B., Sun, X., Ye, J., Cheng, K., Wang, J., and Sun, L. (2021). IEEE Transactions on Knowledge and Data Engineering, IEEE."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, Z., Jain, A., Wang, C., Fanti, G., and Sekar, V. (2020, January 27\u201329). Using gans for sharing networked time series data: Challenges, initial promise, and open questions. Proceedings of the ACM Internet Measurement Conference, Virtual Event.","DOI":"10.1145\/3419394.3423643"},{"key":"ref_12","unstructured":"Desai, A., Freeman, C., Wang, Z., and Beaver, I. (2021). TimeVAE: A variational auto-encoder for multivariate time series generation. arXiv."},{"key":"ref_13","first-page":"458","article-title":"Time Series Simulation by Conditional Generative Adversarial Net","volume":"14","author":"Fu","year":"2020","journal-title":"Int. J. Mech. Ind. Eng."},{"key":"ref_14","unstructured":"Chen, Y., Kempton, D.J., Ahmadzadeh, A., and Angryk, R.A. (2021). Artificial Intelligence and Soft Computing: 20th International Conference, ICAISC 2021, Virtual Event, 21\u201323 June 2021, Proceedings, Part I 20, Springer International Publishing."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"123403","DOI":"10.1016\/j.energy.2022.123403","article-title":"Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM","volume":"246","author":"Huang","year":"2022","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.cja.2022.11.018","article-title":"STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism","volume":"36","author":"Wang","year":"2023","journal-title":"Chin. J. Aeronaut."},{"key":"ref_17","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1109\/TITS.2019.2923964","article-title":"Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches","volume":"20","author":"Yu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xue, Y., Tong, W., Neri, F., and Zhang, Y. (2022). PEGANs: Phased Evolutionary Generative Adversarial Networks with Self-Attention Module. Mathematics, 10.","DOI":"10.3390\/math10152792"},{"key":"ref_20","unstructured":"Xue, Y., Chen, C., and S\u0142owik, A. (2023). IEEE Transactions on Evolutionary Computation, IEEE."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6804","DOI":"10.1109\/TII.2022.3184700","article-title":"Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search","volume":"19","author":"Xue","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10407","DOI":"10.1109\/TCYB.2021.3062396","article-title":"Hausdorff GAN: Improving GAN Generation Quality with Hausdorff Metric","volume":"52","author":"Li","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A Global Geometric Framework for Nonlinear Dimensionality Reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/759567","article-title":"Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework","volume":"2015","author":"Campadelli","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_25","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_26","unstructured":"Levina, E., and Bickel, P. (2004). Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/0020-0190(83)90042-X","article-title":"A linear time algorithm for the Hausdorff distance between convex polygons","volume":"17","author":"Atallah","year":"1983","journal-title":"Inf. Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1142\/S0218195995000064","article-title":"Computing the Fr\u00e9chet distance between two polygonal curves","volume":"5","author":"Alt","year":"1995","journal-title":"Int. J. Comput. Geom. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3233\/AIS-160372","article-title":"Human activity recognition using multisensor data fusion based on Reservoir Computing","volume":"8","author":"Palumbo","year":"2016","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., and Smolley, S.P. (2017, January 22\u201329). Least squares generative adversarial network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.image.2019.03.010","article-title":"Feature augmentation for imbalanced classification with conditional mixture WGANs","volume":"75","author":"Zhang","year":"2019","journal-title":"Signal Process. Image Commun."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/5\/781\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:50Z","timestamp":1760124770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/5\/781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["e25050781"],"URL":"https:\/\/doi.org\/10.3390\/e25050781","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}