Zobrazeno 1 - 10
of 1 747
pro vyhledávání: '"PEDERSEN, TORBEN"'
Autor:
Xu, Jiachen, Li, Yushuai, Pedersen, Torben Bach, He, Yuqiang, Larsen, Kim Guldstrand, Li, Tianyi
Emerging digital twin technology has the potential to revolutionize voltage control in power systems. However, the state-of-the-art digital twin method suffers from low computational and sampling efficiency, which hinders its applications. To address
Externí odkaz:
http://arxiv.org/abs/2412.06940
Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehens
Externí odkaz:
http://arxiv.org/abs/2412.00034
Autor:
Dzaferagic, Merim, Ruffini, Marco, Slamnik-Krijestorac, Nina, Santos, Joao F., Marquez-Barja, Johann, Tranoris, Christos, Denazis, Spyros, Kyriakakis, Thomas, Karafotis, Panagiotis, DaSilva, Luiz, Pandey, Shashi Raj, Shiraishi, Junya, Popovski, Petar, Jensen, Soren Kejser, Thomsen, Christian, Pedersen, Torben Bach, Claussen, Holger, Du, Jinfeng, Zussman, Gil, Chen, Tingjun, Chen, Yiran, Tirupathi, Seshu, Seskar, Ivan, Kilper, Daniel
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical dom
Externí odkaz:
http://arxiv.org/abs/2407.01544
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. However, like other deep learning models, Transformers face limitations in
Externí odkaz:
http://arxiv.org/abs/2401.06524
Data cubes are used for analyzing large data sets usually contained in data warehouses. The most popular data cube tools use graphical user interfaces (GUI) to do the data analysis. Traditionally this was fine since data analysts were not expected to
Externí odkaz:
http://arxiv.org/abs/2312.08557
Big time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in various environments. Significant insights can be gained by mining temporal patterns from these time series. Temporal pattern mining (TPM) extends
Externí odkaz:
http://arxiv.org/abs/2306.10994
Autor:
Holm, Josefine, Chiariotti, Federico, Kalør, Anders E., Soret, Beatriz, Pedersen, Torben Bach, Popovski, Petar
Publikováno v:
IEEE Transactions on Communications, 2023
Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multip
Externí odkaz:
http://arxiv.org/abs/2306.03750
Autor:
Zhao, Yan, Deng, Liwei, Chen, Xuanhao, Guo, Chenjuan, Yang, Bin, Kieu, Tung, Huang, Feiteng, Pedersen, Torben Bach, Zheng, Kai, Jensen, Christian S.
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable rel
Externí odkaz:
http://arxiv.org/abs/2209.04635
Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applicat
Externí odkaz:
http://arxiv.org/abs/2206.14604
Publikováno v:
In Cleaner and Responsible Consumption December 2024 15
