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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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Valentin Ogrean and Remus Brad
Since their inception, deep-learning architectures have shown promising results for automatic segmentation. However, despite the technical advances introduced by fully convolutional networks, generative adversarial networks or recurrent neural networks, ...
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Alexander Hartelt and Frank Puppe
This paper deals with the effect of exploiting background knowledge for improving an OMR (Optical Music Recognition) deep learning pipeline for transcribing medieval, monophonic, handwritten music from the 12th?14th century, whose usage has been neglecte...
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Burak Ekim, Elif Sertel and M. Erdem Kabadayi
Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distr...
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Yuexing Han, Xiaolong Li, Bing Wang and Lu Wang
Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and ...
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Zemin Han, Yuanyong Dian, Hao Xia, Jingjing Zhou, Yongfeng Jian, Chonghuai Yao, Xiong Wang and Yuan Li
Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensin...
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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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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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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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Sergei Strijhak, Daniil Ryazanov, Konstantin Koshelev and Aleksandr Ivanov
In this article the procedure and method for the ice accretion prediction for different airfoils using artificial neural networks (ANNs) are discussed. A dataset for the neural network is based on the numerical experiment results?obtained through iceFoam...
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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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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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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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Roei Grimberg, Meir Teitel, Shay Ozer, Asher Levi and Avi Levy
Since leaf temperature (LT) is not a trivial measurement, deep-neural networks (DNN) and machine learning (ML) models were evaluated in this study as tools for estimating foliage temperature. Two DNN methods were used. The first DNN used convolutional la...
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Xiao Du, Jiajie Chen, Haibo Zhang and Jiqiang Wang
The aero-engine system is complex, and the working environment is harsh. As the fundamental component of the aero-engine control system, the sensor must monitor its health status. Traditional sensor fault detection algorithms often have many parameters, ...
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Chongjiao Wang, Changrong Yao, Siguang Zhao, Shida Zhao and Yadong Li
The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge main...
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Xin Tian, Ina Vertommen, Lydia Tsiami, Peter van Thienen and Sotirios Paraskevopoulos
Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customer...
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Ke Zhao, Lan Huang, Rui Song, Qiang Shen and Hao Xu
Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and most ...
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Yaping Hao and Qiang Gao
In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact ...
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