Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Ruihai Dong"'
Publikováno v:
Virtual and Physical Prototyping, Vol 19, Iss 1 (2024)
ABSTRACTDigital light processing (DLP) is renowned for its precision, but the challenge lies in the identification of optimal print parameters to minimise print errors and enhance overall print accuracy. This study introduces a groundbreaking approac
Externí odkaz:
https://doaj.org/article/7725a83a7067488790eb5cf77ce9f7c9
Publikováno v:
IEEE Access, Vol 12, Pp 136148-136159 (2024)
Geological fault detection is a critical aspect of geological exploitation and oil-gas exploration. The automation of fault detection can significantly reduce the dependence on expert labeling. Current prevailing methods often treat fault detection a
Externí odkaz:
https://doaj.org/article/affddfd8270f4b52aeb1050c1ca916e1
Publikováno v:
IEEE Access, Vol 12, Pp 83391-83404 (2024)
The negative effects of media bias, such as influencing readers’ perceptions and affecting their social decisions, have been widely identified by social scientists. However, the combined impact of media bias and personalised news recommendation sys
Externí odkaz:
https://doaj.org/article/08e64d10c34645789d39f0df4d65636b
Autor:
Yu An, Ruihai Dong
Publikováno v:
IEEE Access, Vol 11, Pp 15058-15068 (2023)
As deep learning (DL) models have been successfully applied to various image processing tasks, DL models, particularly convolutional neural networks (CNN), have been introduced into the geosciences to assist geologists in faster seismic interpretatio
Externí odkaz:
https://doaj.org/article/a5f3bc310df54b65b7f8f8dcd6aa6679
Autor:
Qinqin Wang, Diarmuid Oreilly-Morgan, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor, Ruihai Dong
Publikováno v:
IEEE Access, Vol 10, Pp 71961-71972 (2022)
Despite many recent advances, state-of-the-art recommender systems still struggle to achieve good performance with sparse datasets. To address the sparsity issue, transfer learning techniques have been investigated for recommender systems, but they t
Externí odkaz:
https://doaj.org/article/c268e01f9598415e8e0eef74e399636d
Publikováno v:
Engineering Proceedings, Vol 39, Iss 1, p 30 (2023)
Identifying meaningful and actionable relationships between the price movements of financial assets is a challenging but important problem for many financial tasks, from portfolio optimization to sector classification. However, recent machine learnin
Externí odkaz:
https://doaj.org/article/e880ed761bdb4bea9ab9301c74df521f
Autor:
Bichen Shi, Elias Z. Tragos, Makbule Gulcin Ozsoy, Ruihai Dong, Neil Hurley, Barry Smyth, Aonghus Lawlor
Publikováno v:
IEEE Access, Vol 9, Pp 83340-83354 (2021)
Traditional Recommender Systems (RS) use central servers to collect user data, compute user profiles and train global recommendation models. Central computation of RS models has great results in performance because the models are trained using all th
Externí odkaz:
https://doaj.org/article/2c91b62aeae84cf5a29d7ebb4ac6d23e
Publikováno v:
Data in Brief, Vol 37, Iss , Pp 107219- (2021)
The lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today’s AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic
Externí odkaz:
https://doaj.org/article/d5b50824682149af981092f05818db92
Publikováno v:
Entropy, Vol 23, Iss 12, p 1635 (2021)
Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the sam
Externí odkaz:
https://doaj.org/article/a59b1bebe7f74965a6bd188d9c7b160a
Publikováno v:
Sensors, Vol 20, Iss 15, p 4192 (2020)
A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing modul
Externí odkaz:
https://doaj.org/article/52e36a63b5a447ec9abdb8fc351f1c36