Zobrazeno 1 - 10
of 366
pro vyhledávání: '"Nguyễn Thế Dương"'
Publikováno v:
Tạp chí Khoa học Đại học Cần Thơ, Vol 60, Iss 2 (2024)
Nghiên cứu được thực hiện nhằm xác định kiểu đáy thích hợp trong nuôi thương phẩm ốc hương (Babylonia areolata) trong hệ thống tuần hoàn. Thí nghiệm được bố trí gồm 5 nghiệm thức và được l
Externí odkaz:
https://doaj.org/article/808482ca8a1f4b63819f7ab81b78e0b9
Autor:
Nguyễn Thế Dương, Nguyễn Tấn Khoa
Publikováno v:
Tạp chí Khoa học và Công nghệ, Pp 23-30 (2023)
Nghiên cứu này khảo sát cấu kiện cột bê tông cốt thép chịu nén-uốn đồng thời thông qua việc thiết lập biểu đồ tương tác nén-uốn và đánh giá sự làm việc của cột thô
Externí odkaz:
https://doaj.org/article/5f35e23dd0a544658e86fbf580b9de2e
Autor:
Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia, Nguyen, Phi Le, Huynh, Thanh Trung
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant ch
Externí odkaz:
http://arxiv.org/abs/2410.03070
Autor:
Nguyen, Minh Hieu, Nguyen, Huu Tien, Nguyen, Trung Thanh, Nguyen, Manh Duong, Hoang, Trong Nghia, Nguyen, Truong Thao, Nguyen, Phi Le
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data pertu
Externí odkaz:
http://arxiv.org/abs/2410.03067
Autor:
Nguyen, Minh Duong, Le, Khanh, Do, Khoi, Tran, Nguyen H., Nguyen, Duc, Trinh, Chien, Yang, Zhaohui
In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergence, especially when gradients from different users form an obtuse angle
Externí odkaz:
http://arxiv.org/abs/2410.02845
This paper is devoted to studying the well-posedness, (conditional) conservation of magnetic helicity, inviscid limit and asymptotic stability of the generalized Navier-Stokes-Maxwell equations (NSM) under the Hall effect in two and three dimensions.
Externí odkaz:
http://arxiv.org/abs/2409.07802
Autor:
Vu, Thai-Hoc, Jagatheesaperumal, Senthil Kumar, Nguyen, Minh-Duong, Van Huynh, Nguyen, Kim, Sunghwan, Pham, Quoc-Viet
The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet-of-Things (IoT) era. Nevertheless, most
Externí odkaz:
http://arxiv.org/abs/2405.20024
Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms as well as tailoring the production processes. Analysis of microscopy images that reveal defects typicall
Externí odkaz:
http://arxiv.org/abs/2402.13353
This paper is devoted to studying the well-posedness, conservation of magnetic helicity, inviscid limit and asymptotic stability of the generalized Navier-Stokes-Maxwell (NSM) equations with the standard Ohm's law in $\mathbb{R}^d$ for $d \in \{2,3\}
Externí odkaz:
http://arxiv.org/abs/2401.14839
Autor:
Luu, Minh Ngoc, Nguyen, Minh-Duong, Bedeer, Ebrahim, Nguyen, Van Duc, Hoang, Dinh Thai, Nguyen, Diep N., Pham, Quoc-Viet
In the domain of Federated Learning (FL) systems, recent cutting-edge methods heavily rely on ideal conditions convergence analysis. Specifically, these approaches assume that the training datasets on IoT devices possess similar attributes to the glo
Externí odkaz:
http://arxiv.org/abs/2310.07497