|
|
|
|
|
Pin-Hung Juan and Ja-Ling Wu
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yu Yao and Quan Qian
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zifeng Zhao, Xuesong Yang, Ding Ding, Qiangyong Wang, Feiran Zhang, Zhicheng Hu, Kaikai Xu and Xuelin Wang
Physics-informed DeepONet (PI_DeepONet) is utilized for the reconstruction task of structural displacement based on measured strain. For beam and plate structures, the PI_DeepONet is built by regularizing the strain?displacement relation and boundary con...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chenglin Wang, Qiyu Han, Jianian Li, Chunjiang Li and Xiangjun Zou
Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest ope...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yu Zhang, Jiajun Niu, Zezhong Huang, Chunlei Pan, Yueju Xue and Fengxiao Tan
An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood tre...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yong Liu, Xiaohui Yan, Wenying Du, Tianqi Zhang, Xiaopeng Bai and Ruichuan Nan
The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yin Tang, Lizhuo Zhang, Dan Huang, Sha Yang and Yingchun Kuang
In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a S...
ver más
|
|
|
|
|
|
|
|
|
|
|
Juyao Wei, Zhenggang Lu, Zheng Yin and Zhipeng Jing
This paper presents a novel data-driven multiagent reinforcement learning (MARL) controller for enhancing the running stability of independently rotating wheels (IRW) and reducing wheel?rail wear. We base our active guidance controller on the multiagent ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mirko Dinulovic, Aleksandar Benign and Bo?ko Ra?uo
In the present work, the potential application of machine learning techniques in the flutter prediction of composite materials missile fins is investigated. The flutter velocity data set required for different fin aerodynamic geometries and materials is ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaomei Zhong, Yongsheng Wu, Jie Yu, Lei Liu and Haibo Niu
The formation of oil?mineral aggregates (OMAs) is essential for understanding the behavior of oil spills in estuaries and coastal waters. We utilized statistical methods (screening design) to identify the most influential variables (seven factors in tota...
ver más
|
|
|
|
|
|