|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Surgery for atrial fibrillation in patients with mitral valve disease: Results at five years from the International Registry of Atrial Fibrillation Surgery显示文摘 | Joao Melo Teresa Santiago Carlos Aguiar Eva Berglin Michael Knaut Ottavio Alfieri Stefano Benussi Haw Sie Mathew Williams Fernando Hornero Giuseppi Marinelli Paul Ridley Enrique Fulquet-Carreras António Ferreira | 2008 | The Journal of Thoracic and Cardiovascular Surgery2008，，4： | 1 |
| 2 | On dissipated energy density in compression for concrete显示文摘 | Giuseppi F | 2006 | Engineering Fracture Mechanics2006，73，8： | 1 |
| 3 | Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection显示文摘This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing.In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches. | Emanuele De Santis Alessandro Giuseppi Antonio Pietrabissa Michael Capponi Francesco Delli Priscoli | 2022 | Machine Intelligence Research2022，19，2： | 0 |
| 4 | A Weighted Average Consensus Approach for Decentralized Federated Learning显示文摘Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training process.To remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new tasks.Of the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests. | Alessandro Giuseppi Sabato Manfredi Antonio Pietrabissa | 2022 | Machine Intelligence Research2022，19，4： | 0 |
| 5 | Robust and fault-tolerant spacecraft attitude control based on an extended-observer design显示文摘The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and external disturbances.Thanks to the use of an extended observer design,the proposed control law is robust against all the uncertainties that affect the high-frequency gain matrix,which is shown to capture a broad spectrum of modelling issues,some of which are often neglected by traditional approaches.The proposed controller then provides robustness against parametric uncertainties,as moment of inertia estimation,payload deformations,actuator faults and external disturbances,while maintaining its asymptotic properties. | Alessandro Giuseppi Francesco Delli Priscoli Antonio Pietrabissa | 2022 | Control Theory and Technology2022，20，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员