Many technological systems consist of similar or identical subsystems, which are directly interconnected with their nearest neighbors. Communication networks enable an information exchange among subsystems, when a coo...
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This paper deals with the synchronization problem for a class of linear heterogeneous multi-agent systems. The agents are assumed to be similar in the sense that a part of the dynamics is common and the remaining part...
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Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a nove...
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A new approach to the synthesis of linear virtual actuators for the purpose of reconfigurable control after actuator faults is described. A synthesis approach is provided that recovers the nominal closed-loop tracking...
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This paper investigates the synchronization problem of multi-agent systems with eventbased communication. A communication between the agents is invoked only after an event condition has been fulfilled, which can be ch...
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The paper presents the tuning and testing of an LQ optimal minimax tracking controller which is capable of attenuating low frequency deterministic, and all frequency stochastic disturbances. The controller - based on ...
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Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient t...
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Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
Simulated Moving Bed (SMB) chromatographic separation processes show typical hybrid phenomena: discrete state jumps and switching dynamics. The SMB principle and two modelling approaches based on hybrid automaton are ...
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Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framew...
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Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.
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