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ANALYSIS THE FORMULA OF STRESS INTENSITY FACTORS FOR THE COMPACT TENSION SHEAR TEST
Published 2021-01-01Get full text
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582
RESEARCH ON SOLUTION METHOD OF CONTACT STIFFNESS OF CURVIC COUPLINGS CONSIDERING TOOTH SURFACE ROUGHNESS
Published 2020-01-01Get full text
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583
RESEARCH ON PLUG-IN FILLET WELD FLAW DETECTING BY USING FLEXIBLE PHASED ARRAY TECHNIQUE
Published 2022-01-01Get full text
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584
Kinematics Modeling and Analysis of Manipulator based on Dual Quaternion
Published 2018-01-01Get full text
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585
Analysis of the Torque Transmission Performance of Double 3-RRS Over-redundant Actuation Flight Simulator
Published 2016-01-01Get full text
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Early Identification of Mpox Clinical Features and Its Significance for Epidemic Control
Published 2025-02-01Get full text
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592
Reversible Manifestations of Hypothyroidism causing Diagnostic Dilemma: A Case Series
Published 2025-01-01Get full text
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593
Local residents’ attitudes towards the impact of tourism development in Cape Verde
Published 2014-01-01Get full text
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594
Microvascular dysfunction in a murine model of Alzheimer’s disease using intravital microscopy
Published 2025-02-01Get full text
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595
Research on vehicle feature recognition algorithm based on optimized convolutional neural network
Published 2023-10-01“…To address the issue of weak identification and low accuracy in recognizing features of target vehicles at different distances in road scene images, a vehicle feature recognition algorithm based on optimized convolutional neural network (CNN) was proposed.Firstly, a multi-scale input based on the PAN model was employed to capture target vehicle features at varying distances.Subsequently, improvements were made to the network model by incorporating multi-pool, batch normalization (BN) layers, and Leaky ReLU activation functions within the CNN architecture.Furthermore, the generalization ability of the network model was enhanced by introducing a hybrid attention mechanism that focuses on important features and regions in the vehicle image.Lastly, a multi-level CNN structure was constructed to achieve feature recognition for vehicles.Simulation experiment results conducted on the BIT-Vehicle database within a single scene show the proposed algorithm’s significant enhancements in single-object and multi-object recognition rates compared to CNN, R-CNN, ABC-CNN, Faster R-CNN, AlexNet, VGG16, and YOLOV8.Specifically, improvements of 16.75%, 10.9%, 4%, 3.7%, 2.46%, 1.3%, and 1% in single-object recognition, as well as 17.8%, 10.5%, 2.5%, 3.8%, 2.7%, 1.1%, and 1.3% in multi-object recognition, have been demonstrated by the proposed algorithm, respectively.Over the more complex UA-DETRAC datasets, more precise results have been also achieved by the proposed algorithm in recognizing target vehicles at various distances compared to other algorithms.…”
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596
The Impact of the Pandemic on Sports Companies: Comparative Rate Analysis of Champions League and Turkish Football Companies
Published 2023-04-01“…It was evaluated that the net working capital of the other three clubs was negative. …”
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597
Design and Analysis of Closed-Linked Eight-Link Foot Robot with Composite Hinges
Published 2022-04-01Get full text
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598
Study on the Load Spectrum Test Method and Experiment of Shaft Parts of Loader
Published 2017-01-01Get full text
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599
Design and Dynamics Simulation of a New Type of Swashplate Engine
Published 2021-08-01Get full text
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600
BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK
Published 2024-08-01Get full text
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