Controllabilty of a two-body crawling system on an inclined plane

Болотник, Н. Н.; Figurina, Tatiana

doi:10.1007/s11012-021-01466-5

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…A reported 1.9 billion people worldwide are affected by skin diseases, with the shortage of dermatologists leading to many seeking dermatologic care from general practitioners, resulting in lower diagnostic accuracy [45]. Additionally, the utilization of heuristic/evolutionary search optimization AI-based algorithms, such as the Gravitational Search Algorithm [47][48][49] and Inclined Planes System Optimizations [50,51], to tackle hyperparameter optimization during network training is a promising research area. This paper introduces supervised loss, AdaBoost loss, and distillation loss, suggesting that enhancing the performance of dermatological diagnostic models could be achieved by carefully selecting optimal values for loss term weights and the temperature coefficient.…”

Section: Dermatologic Diagnostic Systems In Clinical Applicationsmentioning

confidence: 99%

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Yu,

Xiong,

et al. 2024

Complex Intell. Syst.

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Knowledge Distillation has shown promising results for classifying skin lesions in dermatology images. Traditional knowledge distillation typically involves the student model passively mimicking the teacher model's knowledge. We propose utilizing AdaBoost to enable the student to actively mine the teacher's learning representation for skin lesion classification. This paradigm allows the student to determine the “granularity” in mining the teacher's knowledge. As the student's learning process progresses, it can become challenging to pinpoint specific learning difficulties, especially with potential interference from the teacher. To address this issue, we introduce a variational difficulty mining strategy to reduce the impact of such interference. This strategy involves the distillation module capturing more nuanced classification difficulties by extracting information from the node's $$l$$ l th hops. By maximizing the mutual information between the teacher and student, we effectively filter out noise interference from these nuanced difficulties. Our proposed framework, Variational AdaBoost Knowledge Distillation (VAdaKD), allows the student to actively mine and leverage the teacher's knowledge for improved skin lesion classification. Our proposed method performs satisfactorily on three benchmark datasets: the Dermnet dataset, ISIC 2019 dataset, and HAM10000 dataset, respectively. Specifically, our method shows an improvement of 2–3% over the baseline on the Dermnet dataset and outperforms the best results of the other compared methods by 1%. Experimental results and visualization performance indicate that our proposed method effectively captures the learning difficulties and achieves better visualized t-distributed stochastic neighbor embedding classification results. Our code is available at https://github.com/25brilliant/VAdaKD.

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Section: Dermatologic Diagnostic Systems In Clinical Applicationsmentioning

confidence: 99%

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Yu,

Xiong,

et al. 2024

Complex Intell. Syst.

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Motion of a system of interacting bodies in a medium with quadratic resistance

Figurina,

Knyazkov

2023

Nonlinear Dyn

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Spatial reorientation of a solid body using a moving mass in the presence of external forces specified as the functions of time

Shmatkov

2024

Doklady Rossijskoj akademii nauk. Fizika, tehničeskie nauki

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The spatial motion of a mechanical system consisting of a rigid body and a moving point mass, interacting with each other by means of unspecified internal forces, has been studied. The task is to construct such a trajectory for a point mass, when moving along which a rigid body, under the influence of the force of interaction with this mass, changes its orientation in space according to a known program. It is assumed that there are external forces acting on both objects, specified as functions of time. A system of three first-order ordinary differential equations, resolved with respect to derivatives, is obtained, which allows solving the problem. These relationships can be used to control spacecraft and robotic systems.

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Controllabilty of a two-body crawling system on an inclined plane

Cited by 3 publications

References 28 publications

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Motion of a system of interacting bodies in a medium with quadratic resistance

Spatial reorientation of a solid body using a moving mass in the presence of external forces specified as the functions of time

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