Search alternatives:
we decrease » _ decrease (Expand Search), teer decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), gy decreased (Expand Search), b1 decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
we decrease » _ decrease (Expand Search), teer decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), gy decreased (Expand Search), b1 decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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SEM morphology of power: (a)17−4PH; (b)TiC.
Published 2025“…Compared to the commercial blade, the wear of the laser-cladded blade was decreased by 67%. This study successfully applied wear-resistant laser cladding coatings on the surface of harvester blades with small substrate thickness, significantly extending their service life.…”
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5085
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5086
SlABCG9 Functioning as a Jasmonic Acid Transporter Influences Tomato Resistance to Botrytis cinerea
Published 2025“…Assays using Xenopus oocytes, yeast cell sensitivity, and JA-inhibited primary root growth confirmed that SlABCG9 functions as a JA influx transporter. The knockout mutant lines of <i>SlABCG9</i> showed decreased JA contents, suppressed defense gene <i>PDF1.2</i>’s expression, reduced antioxidant enzyme activity, and severe disease symptoms compared to wild-type controls. …”
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5087
The overall framework of CARAFE.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5088
KPD-YOLOv7-GD network structure diagram.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5089
Comparison experiment of accuracy improvement.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5090
Comparison of different pruning rates.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5091
Comparison of experimental results at ablation.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5092
Result of comparison of different lightweight.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5093
DyHead Structure.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5094
The parameters of the training phase.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5095
Structure of GSConv network.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5096
Comparison experiment of accuracy improvement.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5097
Improved model distillation structure.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. …”
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5098
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5100