دورية أكاديمية
extendGAN+: transferable data augmentation framework using WGAN-GP for data-driven indoor localisation model
| العنوان: | extendGAN+: transferable data augmentation framework using WGAN-GP for data-driven indoor localisation model |
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| المؤلفون: | Yean, Seanglidet, Goh, Wayne, Lee, Bu-Sung, Oh, Hong Lye |
| المساهمون: | School of Computer Science and Engineering, Singtel Cognitive and Artificial Intelligence Lab (SCALE@NTU) |
| سنة النشر: | 2023 |
| المجموعة: | DR-NTU (Digital Repository at Nanyang Technological University, Singapore) |
| مصطلحات موضوعية: | Engineering::Computer science and engineering, Indoor Localisation, Generative Adversarial Networks |
| الوصف: | For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%. ; Agency for Science, Technology and Research (A*STAR) ; Nanyang Technological University ; Published version ; This research was funded by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is supported by A*STAR under its Industry Alignment Fund (LOA Award number: I1701E0013). |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| العلاقة: | I1701E0013; Sensors; Yean, S., Goh, W., Lee, B. & Oh, H. L. (2023). extendGAN+: transferable data augmentation framework using WGAN-GP for data-driven indoor localisation model. Sensors, 23(9), 4402-. https://dx.doi.org/10.3390/s23094402Test; https://hdl.handle.net/10356/169535Test; 2-s2.0-85159216576; 23; 4402 |
| DOI: | 10.3390/s23094402 |
| الإتاحة: | https://doi.org/10.3390/s23094402Test https://hdl.handle.net/10356/169535Test |
| حقوق: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
| رقم الانضمام: | edsbas.7F9A3D08 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14248220 |
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| DOI: | 10.3390/s23094402 |