Gotowe bibliografie tematyczne / Factorized cross entropy

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Gotowa bibliografia na temat „Factorized cross entropy”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Factorized cross entropy"

1

C. Patil, Pawankumar, and Shashidhar Sonnad. "Factorized cross entropy integrated hyperspectral CNN (HSCNet-FACE) for hyperspectral image classification." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1890–900. https://doi.org/10.11591/eei.v14i3.8829.

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The use of hyperspectral image classification algorithms has garnered increasing interest from the scientific community in recent years, especially in the field of geosciences for pattern recognition applications. In order to extract full spectral-spatial characteristics, this study presents feature extraction with hyperspectral CNN (HSCNet), a unique hierarchical neural network architecture. HSCNet can handle computational complexity issues and capture extensive spectral-spatial information with ease. We use factorized cross entropy (FACE) to address the common problem of class imbalance in b
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Pan, Rui, Wei Gao, Yunbo Zuo, Guoxin Wu, and Yuda Chen. "Investigation into defect image segmentation algorithms for galvanised steel sheets under texture backgrounds." Insight - Non-Destructive Testing and Condition Monitoring 65, no. 9 (2023): 492–500. http://dx.doi.org/10.1784/insi.2023.65.9.492.

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Image segmentation is a significant step in image analysis and computer vision. Many entropy-based approaches have been presented on this topic. Among them, Tsallis entropy is one of the best-performing methods. In this paper, the surface defect images of galvanised steel sheets were studied. A two-dimensional asymmetric Tsallis cross-entropy image segmentation algorithm based on chaotic bee colony algorithm optimisation was used to investigate the segmentation of surface defects under complex texture backgrounds. On the basis of Tsallis entropy threshold segmentation, a more concise expressio
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Kaupp, Lukas, Bernhard Humm, Kawa Nazemi, and Stephan Simons. "Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis." Sensors 22, no. 21 (2022): 8259. http://dx.doi.org/10.3390/s22218259.

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Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select produc
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Pawankumar, C. Patil, and Sonnad Shashidhar. "Factorized cross entropy integrated hyperspectral CNN (HSCNet-FACE) for hyperspectral image classification." May 15, 2025. https://doi.org/10.11591/eei.v14i3.8829.

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The use of hyperspectral image classification algorithms has garnered increasing interest from the scientific community in recent years, especially in the field of geosciences for pattern recognition applications. In order to extract full spectral-spatial characteristics, this study presents feature extraction with hyperspectral CNN (HSCNet), a unique hierarchical neural network architecture. HSCNet can handle computational complexity issues and capture extensive spectral-spatial information with ease. We use factorized cross entropy (FACE) to address the common problem of class imbalance in b
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Chen, Xinyu, Zhuzhen He, Qihao Ma, Yan Ren, and Tong Cui. "A lightweight efficient semantic segmentation with encoder-decoder for arc interference in robotic arc welding." Measurement Science and Technology, November 8, 2023. http://dx.doi.org/10.1088/1361-6501/ad0ad8.

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Abstract Recognizing laser stripes under conditions of arc interference is a persistent challenge faced by linear structured light sensors due to their similarity with noise in morphological characteristics. In this paper, a symmetrical encoder-decoder semantic segmentation model named LSRNet (lightweight stripe recognition network) is proposed that enables real-time detection of laser stripes in environments with strong arc interference. It analyzes the difference between arc and laser stripe from the semantic point of view. Firstly, to facilitate quick and accurate recognition of laser strip
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Berrehal, Hamza, Roshanak Karami, Saeed Dinarvand, Ioan Pop, and Ali Chamkha. "Entropy generation analysis for convective flow of aqua Ag-CuO hybrid nanofluid adjacent to a warmed down-pointing rotating vertical cone." International Journal of Numerical Methods for Heat & Fluid Flow, December 26, 2023. http://dx.doi.org/10.1108/hff-05-2023-0236.

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Purpose This paper aims to study numerically the flow, heat transfer, and entropy generation of aqueous copper oxide-silver hybrid nanofluid over a down-pointing rotating vertical cone, with linear surface temperature (LST) and linear surface heat flux (LSHF), in the presence of a cross-magnetic field. In industrial applications, such as oil and gas plants, food industries, steel factories and nuclear packages, the real bodies may contain nonorthogonal walls and variable cross-section three-dimensional forms which this issue can clarify the importance of selective geometry in the present resea
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