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Jia-Ling Xie, Wei-Feng Shi, Ting Xue and Yu-Hang Liu
The fault detection and diagnosis of a ship?s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by ...
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Haiping Si, Mingchun Li, Weixia Li, Guipei Zhang, Ming Wang, Feitao Li and Yanling Li
Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food ...
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Norbert Fischer, Alexander Hartelt and Frank Puppe
Digitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical c...
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Riad Ibadulla, Thomas M. Chen and Constantino Carlos Reyes-Aldasoro
This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels. When using the free-space 4f system to accelerate the inference sp...
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Artemiy Belousov, Ivan Kisel, Robin Lakos and Akhil Mithran
Algorithms optimized for high-performance computing, which ensure both speed and accuracy, are crucial for real-time data analysis in heavy-ion physics experiments. The application of neural networks and other machine learning methodologies, which are fa...
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Aleksandr Cariow, Janusz P. Paplinski and Marta Makowska
The paper introduces a range of efficient algorithmic solutions for implementing the fundamental filtering operation in convolutional layers of convolutional neural networks on fully parallel hardware. Specifically, these operations involve computing M i...
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Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attenti...
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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then ap...
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Chin-Yi Chen and Jih-Jeng Huang
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking al...
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Wen-Chang Cheng, Hung-Chou Hsiao, Yung-Fa Huang and Li-Hua Li
This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV...
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