A Friction-Driven Strategy for Agile Steering Wheel Manipulation by Humanoid Robots

Cai, Zhaoyang; Zhu, Xin; Gergondet, Pierre; Chen, Xuechao; Yu, Zhangguo

doi:10.34133/cbsystems.0064

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…VC from large scale dataset is great challenging in computer task. In this paper, different large-scale datasets, namely Stanford car, Vehicle rear, MVVTR, Indian Vehicle, TRANSCOS, Thai VC, 2023 Car Model, UFPR-ALPR, Vehicle x and CompCar datasets are utilized for VC [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Application of an ensemble CatBoost model over complex dataset for vehicle classification

M.,

R. K.,

et al. 2024

PLoS ONE

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The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

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Section: Introductionmentioning

confidence: 99%

Application of an ensemble CatBoost model over complex dataset for vehicle classification

M.,

R. K.,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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Antibacterial and Biofilm Removal Strategies Based on Micro/Nanomotors in the Biomedical Field

Wu,

Wang,

Yang

et al. 2024

ChemMedChem

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Bacterial infection, which can trigger varieties of diseases and tens of thousands of deaths each year, poses serious threats to human health. Particularly, the new dilemma caused by biofilms is gradually becoming a severe and tough problem in the biomedical field. Thus, the strategies to address these problems are considered an urgent task at present. Micro/nanomotors (MNMs), also named micro/nanoscale robots, are mostly driven by chemical energy or external field, exhibiting strong diffusion and self‐propulsion in the liquid media, which has the potential for antibacterial applications. In particular, when MNMs are assembled in swarms, they become robust and efficient for biofilm removal. However, there is a lack of comprehensive review discussing the progress in this aspect. Bearing it in mind and based on our own research experience in this regard, the studies on MNMs driven by different mechanisms orchestrated for antibacterial activity and biofilm removal are timely and concisely summarized and discussed in this work, aiming to show the advantages of MNMs brought to this field. In addition, an outlook was proposed, hoping to provide the fundamental guidance for future development in this area.

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Research on Optimizing 6G Data-Driven Decision-Making Using Wireless Cyber Enterprise Innovation Process Using Decision Tree Algorithm

Hu,

Zhao

2024

Wireless Pers Commun

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A Friction-Driven Strategy for Agile Steering Wheel Manipulation by Humanoid Robots

Cited by 8 publications

References 22 publications

Application of an ensemble CatBoost model over complex dataset for vehicle classification

Application of an ensemble CatBoost model over complex dataset for vehicle classification

Antibacterial and Biofilm Removal Strategies Based on Micro/Nanomotors in the Biomedical Field

Research on Optimizing 6G Data-Driven Decision-Making Using Wireless Cyber Enterprise Innovation Process Using Decision Tree Algorithm

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