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A Cloud Detection Network Based on Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net.
Int J Neural Syst
December 2024
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, P.R. China.
Cloud cover experiences rapid fluctuations, significantly impacting the irradiance reaching the ground and causing frequent variations in photovoltaic power output. Accurate detection of thin and fragmented clouds is crucial for reliable photovoltaic power generation forecasting. In this paper, we introduce a novel cloud detection method, termed Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net (ALCU-Net).
View Article and Find Full Text PDFLT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments.
Front Plant Sci
October 2024
College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Article Synopsis
- - Zanthoxylum bungeanum Maxim is an important crop in Asia, but diseases can significantly reduce its yield, prompting research into deep learning methods for disease recognition in agriculture.
- - The paper introduces LT-DeepLab, a new deep learning model that enhances the existing DeepLabV3+ by improving feature extraction and reducing complexity, allowing for better segmentation of various leaf and trunk diseases.
- - LT-DeepLab shows improvements in disease segmentation accuracy metrics and reduces the model's computational requirements and parameter count, making it a potential tool for more efficient crop disease management.
Objective: To determine the effectiveness of Kinesio taping along with routine physical therapy on improving gross motor function in sitting and standing among spastic diplegic Cerebral Palsy children.
Design: Randomized controlled trial.
Settings: University Teaching Hospital University of Lahore, Lahore.
Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.
Bioinformatics
May 2024
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Motivation: Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.
Results: To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer.
Lightweight Deep Exemplar Colorization via Semantic Attention-Guided Laplacian Pyramid.
IEEE Trans Vis Comput Graph
May 2024
Exemplar-based colorization aims to generate plausible colors for a grayscale image with the guidance of a color reference image. The main challenging problem is finding the correct semantic correspondence between the target image and the reference image. However, the colors of the object and background are often confused in the existing methods.
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