المؤلفون: Schläpfer, Markus, Chew, Hong Jun, Yean, Seanglidet, Lee, Bu-Sung

مصطلحات موضوعية: Electrical Engineering and Systems Science - Systems and Control

الوصف: The vehicle-to-grid (V2G) concept utilises electric vehicles as distributed energy storage and thus may help to balance out the intermittent availability of renewable energy sources such as photovoltaics. V2G is therefore considered to play an important role for achieving low-carbon energy and transportation systems in cities. However, the adequate planning of city-wide V2G infrastructures requires detailed knowledge of the aggregate mobility patterns of individuals and also needs to keep track with ongoing developments of urban transportation modes. Here, we introduce an initial framework that infers population-wide mobility patterns from anonymised mobile phone location data and subsequently superimposes a vehicle charging and discharging scheme. The framework allows for the estimation of the aggregate V2G energy supply and demand at fine-grained spatial and temporal scales under a given electric vehicle usage scenario. This information provides an adequate basis for assessing the role of V2G in the context of maximising the deployment of photovoltaics, as well as for the sizing and placement of the required vehicle (dis)charging infrastructure. The proposed framework is applied to Singapore as a case study.

الوصول الحر: http://arxiv.org/abs/2112.15006Test

المؤلفون: Chen, Yang, Ashizawa, Nami, Yean, Seanglidet, Yeo, Chai Kiat, Yanai, Naoto

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Social and Information Networks, Statistics - Machine Learning

الوصف: In the information age, a secure and stable network environment is essential and hence intrusion detection is critical for any networks. In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection. The deep autoencoding Gaussian mixture model comprises a compression network and an estimation network which is able to perform unsupervised joint training. However, the code generated by the autoencoder is inept at preserving the topology of the input space, which is rooted in the bottleneck of the adopted deep structure. A self-organizing map has been introduced to construct SOMDAGMM for addressing this issue. The superiority of the proposed SOM-DAGMM is empirically demonstrated with extensive experiments conducted upon two datasets. Experimental results show that SOM-DAGMM outperforms state-of-the-art DAGMM on all tests, and achieves up to 15.58% improvement in F1 score and with better stability.

الوصول الحر: http://arxiv.org/abs/2008.12686Test

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)

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).

وصف الملف: application/pdf

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

الإتاحة: https://doi.org/10.3390/s23094402Test
https://hdl.handle.net/10356/169535Test

المؤلفون: Potort, Francesco, Torres-Sospedra, Joaquín, Quezada Gaibor, Darwin, Jiménez, Antonio Ramón, Seco, Fernando, Perez-Navarro, Antoni, Ortiz, Miguel, Zhu, Ni, Renaudin, Valerie, Ichikari, Ryosuke, Shimomura, Ryo, Kaichi, Tomoya, Zhou, Baoding, Liu, Xu, Gu, Zhining, Yang, Chengjing, Wu, Zhiqian, Xie, Doudou, Huang, Can, Zheng, Lingxiang, Peng, Ao, Jin, Ge, Wang, Qu, Luo, Haiyong, Xiong, Hao, Bao, Linfeng, Zhang, Pushuo, Zhao, Fang, Yu, Chia-An, Hung, Chung-Hao, Antsfeld, Leonid, Chidlovskii, Boris, Jiang, Haitao, Xia, Ming, Yan, Dayu, Li, Yuhang, Dong, Yitong, Silva, Ivo, Pendão, Cristiano, Meneses, Filipe, Nicolau, Maria João, Costa, António, Moreira, Adriano, De Cock, Cedric, Plets, David, Opiela, Miroslav, Dzama, Jakub, Zhang, Liqiang, Li, Hu, Chen, Boxuan, Liu, Yu, Yean, Seanglidet, Lim, Bo Zhi, Teo, Wei Jie, Lee, Bu Sung, OH, HL, ohta, nozomu, Nagae, Satsuki, Kurata, Takeshi, dongyan, wei, Ji, Xinchun, Zhang, Wenchao, Kram, Sebastian, Stahlke, Maximilian, Mutschler, Christopher, Crivello, Antonino, Barsocchi, Paolo, GIROLAMI, MICHELE, Palumbo, Filippo, Chen, Ruizhi, Wu, Yuan, Li, Wei, Yu, Yue, Xu, Shihao, Huang, Lixiong, Liu, Tao, Kuang, Jian, Niu, Xiaoji, Yoshida, Takuto, Nagata, Yoshiteru, Fukushima, Yuto, Fukatani, Nobuya, Hayashida, Nozomi, Asai, Yusuke, Urano, Kenta, Ge, Wenfei, Lee, Nien-Ting, Fang, Shih-Hau, Jie, You-Cheng, Young, Shawn-Rong, Chien, Ying-Ren, Yu, Chih-Chieh, Ma, Chengqi, Wu, Bang, Zhang, Wei, Wang, Yankun, Fan, Yonglei, Poslad, Stefan, Selviah, David, Wang, Weixi, Yuan, Hong, Yonamoto, Yoshitomo, Yamaguchi, Masahiro

المساهمون: Universitat Oberta de Catalunya (UOC)

مصطلحات موضوعية: indoor positioning and navigation, evaluation, smartphone-based positioning, foot-mounted IMU, positioning in industrial scenarios and factories, vehicle-positioning, navegación y posicionamiento en interiores, evaluación, posiciónamiento basado en teléfonos inteligentes, IMU con patas, posicionamiento en escenarios industriales y fábricas, posicionamiento del vehículo, navegació i posicionament en interiors, evaluació, posicionament basat en teléfons inteligents, IMU amb cames, posicionament en escenaris industrials i fabriques, posicionament del vehicle, electronics in navigation, electrònica en la navegació, electrónica en la nevagación

الوصف: Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoorpositioning andnavigationpurposes.Throughfaircomparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1m for the Smartphone Track and 0.5m for the Footmounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

وصف الملف: application/pdf

العلاقة: IEEE Sensors Journal, 2022, 22(6); https://www.doi.org/10.1109/JSEN.2021.3083149Test; http://hdl.handle.net/10609/147005Test

الإتاحة: https://doi.org/10.1109/JSEN.2021.3083149Test
http://hdl.handle.net/10609/147005Test

المؤلفون: Potorti, Francesco, Torres-Sospedra, Joaquín, Quezada-Gaibor, Darwin, Jimenez, Antonio Ramon, Seco, Fernando, Perez-Navarro, Antoni, Ortiz, Miguel, Zhu, Ni, Renaudin, Valerie, Ichikari, Ryosuke, Shimomura, Ryo, Ohta, Nozomu, Nagae, Satsuki, Kurata, Takeshi, Wei, Dongyan, Ji, Xinchun, Zhang, Wenchao, Kram, Sebastian, Stahlke, Maximilian, Mutschler, Christopher, Crivello, Antonino, Barsocchi, Paolo, Girolami, Michele, Palumbo, Filippo, Chen, Ruizhi, Wu, Yuan, Li, Wei, Yu, Yue, Xu, Shihao, Huang, Lixiong, Liu, Tao, Kuang, Jian, Niu, Xiaoji, Yoshida, Takuto, Nagata, Yoshiteru, Fukushima, Yuto, Fukatani, Nobuya, Hayashida, Nozomi, Asai, Yusuke, Urano, Kenta, Ge, Wenfei, Lee, Nien-Ting, Fang, Shih-Hau, Jie, You-Cheng, Young, Shawn-Rong, Chien, Ying-Ren, Yu, Chih-Chieh, Ma, Chengqi, Wu, Bang, Zhang, Wei, Wang, Yankun, Fan, Yonglei, Poslad, Stefan, Selviah, David R., Wang, Weixi, Yuan, Hong, Yonamoto, Yoshitomo, Yamaguchi, Masahiro, Kaichi, Tomoya, Zhou, Baoding, Liu, Xu, Gu, Zhining, Yang, Chengjing, Wu, Zhiqian, Xie, Doudou, Huang, Can, Zheng, Lingxiang, Peng, Ao, Jin, Ge, Wang, Qu, Luo, Haiyong, Xiong, Hao, Bao, Linfeng, Zhang, Pushuo, Zhao, Fang, Yu, Chia-An, Hung, Chun-Hao, Antsfeld, Leonid, Silva, Ivo Miguel Menezes, Pendão, Cristiano Gonçalves, Meneses, Filipe, Nicolau, Maria João, Costa, António, Moreira, Adriano, Cock, Cedric De, Plets, David, Opiela, Miroslav, Jakub Džama, Zhang, Liqiang, Li, Hu, Chen, Boxuan, Liu, Yu, Yean, Seanglidet, Lim, Bo Zhi, Teo, Wei Jie, Lee, Bu Sung, Oh, Hong Lye

مصطلحات موضوعية: Sensor phenomena and characterization, Indoor navigation, Testing, Standards, Satellite broadcasting, Recurrent neural networks, Received signal strength indicator, Indoor positioning and navigation, evaluation, smartphone-based positioning, foot-mounted IMU, positioning in industrial scenarios and factories, vehicle-positioning, Ciências Naturais::Ciências da Computação e da Informação, Science & Technology

الوصف: Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements. ; Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. ...

وصف الملف: application/pdf

العلاقة: info:eu-repo/grantAgreement/EC/H2020/813278/EU; info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT; info:eu-repo/grantAgreement/FCT/POR_NORTE/PD%2FBD%2F137401%2F2018/PT; https://ieeexplore.ieee.org/document/9439493Test; F. Potortì et al., "Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition," in IEEE Sensors Journal, vol. 22, no. 6, pp. 5011-5054, 15 March15, 2022, doi:10.1109/JSEN.2021.3083149.; https://hdl.handle.net/1822/82092Test

الإتاحة: https://doi.org/10.1109/JSEN.2021.3083149Test
https://hdl.handle.net/1822/82092Test

المؤلفون: Lim, Yun Jie, Yean, Seanglidet, Lee, Bu Sung, Edwards, Peter

المصدر: Procedia Computer Science ; volume 201, page 390-397 ; ISSN 1877-0509

مصطلحات موضوعية: General Earth and Planetary Sciences, General Environmental Science

الإتاحة: https://doi.org/10.1016/j.procs.2022.03.052Test
https://api.elsevier.com/content/article/PII:S1877050922004653?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1877050922004653?httpAccept=text/plainTest

المؤلفون: Zhou, Jiazu, Tan, Shannon Xinyi, Yean, Seanglidet, Schläpfer, Markus, Lee, Bu Sung

المساهمون: Singapore-ETH Centre, National Research Foundation

المصدر: 2023 11th International Conference on Traffic and Logistic Engineering (ICTLE)

الإتاحة: https://doi.org/10.1109/ictle59670.2023.10508873Test
http://xplorestaging.ieee.org/ielx7/10508731/10508732/10508873.pdf?arnumber=10508873Test

المؤلفون: Goh, Yun Si, Chua, Wen Qing, Yean, Seanglidet, Lee, Bu Sung

المصدر: 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

الإتاحة: https://doi.org/10.1109/sitis61268.2023.00025Test
http://xplorestaging.ieee.org/ielx7/10472709/10472788/10472837.pdf?arnumber=10472837Test

المؤلفون: Tan, Ki In, Yean, Seanglidet, Lee, Bu Sung

المساهمون: Nanyang Technological University

المصدر: 2023 IEEE Sensors Applications Symposium (SAS)

الإتاحة: https://doi.org/10.1109/sas58821.2023.10254193Test
http://xplorestaging.ieee.org/ielx7/10253663/10253664/10254193.pdf?arnumber=10254193Test

المؤلفون: Chia, Bing Xun, Teo, Wei Jie, Swa, Ju Xiang, Yean, Seanglidet, Lee, Bu Sung, Oh, Hong Lye, Theng, Yin-Leng

المصدر: 2022 International Conference on Platform Technology and Service (PlatCon)

الإتاحة: https://doi.org/10.1109/platcon55845.2022.9932088Test
http://xplorestaging.ieee.org/ielx7/9932030/9932035/09932088.pdf?arnumber=9932088Test