Zobrazeno 1 - 10
of 1 162
pro vyhledávání: '"Seq2Seq"'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 8, Pp 3327-3338 (2024)
Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects. However, most neural network models only use the data from a single monitoring poi
Externí odkaz:
https://doaj.org/article/5d1ffd94c1eb400990f4463ba8b42225
Publikováno v:
Alexandria Engineering Journal, Vol 101, Iss , Pp 219-233 (2024)
Camber in hot-rolled plates significantly impacts product quality and rolling process stability, making accurate camber prediction crucial. However, it is challenging to measure asymmetric factors impacting camber in real production, hindering the ab
Externí odkaz:
https://doaj.org/article/3b18e88e0fb74838adeafea249588853
Autor:
Yuqian Zhao, Jiuchun Ren
Publikováno v:
IEEE Access, Vol 12, Pp 184275-184284 (2024)
Named Entity Recognition (NER) can be divided into three subtasks: Flat, Nested, and Discontinuous. They are usually handled separately and independently, and most traditional approaches rely on representations in the form of Token and Span, which li
Externí odkaz:
https://doaj.org/article/054cee7493fa49a3887eb8fc19774efc
Autor:
Yike Guo
Publikováno v:
IEEE Access, Vol 12, Pp 102637-102648 (2024)
Dialogue systems are an important research direction in artificial intelligence, with broad application prospects and market value. In order to improve system efficiency and user satisfaction, an open domain generative dialogue system integrating kno
Externí odkaz:
https://doaj.org/article/e93e919e5652486d9bd631a08f250fcd
Autor:
Pedram Babakhani, Andreas Lommatzsch, Torben Brodt, Doreen Sacker, Fikret Sivrikaya, Sahin Albayrak
Publikováno v:
IEEE Access, Vol 12, Pp 66085-66099 (2024)
This paper presents a comprehensive study on generating subjective inquiries for news media posts to empower public engagement with trending media topics. While previous studies primarily focused on factual and objective questions with explicit or im
Externí odkaz:
https://doaj.org/article/0fbc3111e8bd4d0db4747b36ae75e62b
Autor:
Abdul Aziz Hulleck, Aamna AlShehhi, Marwan El Rich, Raviha Khan, Rateb Katmah, Mahdi Mohseni, Navid Arjmand, Kinda Khalaf
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 1715-1724 (2024)
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based
Externí odkaz:
https://doaj.org/article/4951d61b677e4f8eb1b5ee5d4f8095f6
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 248-256 (2024)
Channel state information (CSI) is crucial for enhancing the performance of wireless systems by allowing to adjust the transmission strategies based on the current channel conditions. However, obtaining precise CSI is difficult because of the fast-ch
Externí odkaz:
https://doaj.org/article/aa44b5417c254b8e9ce9974c07d34a95
Publikováno v:
Batteries, Vol 10, Iss 11, p 389 (2024)
This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditio
Externí odkaz:
https://doaj.org/article/178ba371e8c94dc0b8a19edfbebf2e0e
Autor:
Bharathi BHAGAVATHSINGH, Aarthi Vilapakkam SATHISH, Akilan KALAISELVAN, Christina Eunice JOHN
Publikováno v:
Revista Română de Informatică și Automatică, Vol 33, Iss 4, Pp 99-108 (2023)
Artificial Intelligence (AI) has led to advancements in multiple fields of research, and music has always been a field of high interest. Music is an important part of life and various studies have shown the link between better living and listening to
Externí odkaz:
https://doaj.org/article/ede369acd7a24943a91eaf74864264ba
Publikováno v:
Applied Sciences, Vol 14, Iss 14, p 6019 (2024)
This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the
Externí odkaz:
https://doaj.org/article/d35bb2e45a7e4abb8dc5fd83fdbb66ea