In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better priva...
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This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and...
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Enabling aerial robots to handle dynamic contacts happening at non-vanishing speeds can enlarge the range of their applications. In this work, we propose an impactaware strategy to allow aerial multirotor robots to re...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
Enabling aerial robots to handle dynamic contacts happening at non-vanishing speeds can enlarge the range of their applications. In this work, we propose an impactaware strategy to allow aerial multirotor robots to recover from impacts. The method leverages a reactive strategy not requiring low-level changes to the motion controller commonly implemented onboard quadrotors, which might be not viable or not desirable for most users. Extensive simulation tests show that the proposed strategy considerably increases the tolerated velocity at impact in tasks in which the robot either picks an object up or collides against an object to clear its way. Preliminary experimental results using Crazyflie UAVs are also presented.
Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mi...
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This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one han...
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In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs ...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.
Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have de...
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Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.
The problem of non-collocated vibration absorption by a delayed resonator is addressed with emphasis on system fatigue resistance and energy efficiency of control actions. The analysis is performed for a system consis...
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Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing...
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Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.
One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, curren...
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