This paper investigates a multi-agent system with quantized event-triggered communication mechanisms to reduce the communication expenditure. The event-triggered communication mechanism is proposed to reduce the utili...
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This paper investigates a multi-agent system with quantized event-triggered communication mechanisms to reduce the communication expenditure. The event-triggered communication mechanism is proposed to reduce the utilization of communication bandwidth, which lowers the communication expenditure in communication frequency. The quantized communication mechanism quantizes the communication information in the multi-agent system with limited communication capacity. First, a quantized periodic communication mechanism system is proposed, which provides a lower bound of the communication interval for the quantized event-triggered communication system to avoid the Zeno behavior. Then the system with quantized event-triggered communication is proved to be convergent to an optimal solution of distributed constraint optimization. With the quantized communication mechanism, the system can reduce the communication cost in the frequency of communication and the amount of transmission data. The proposed method has a tradeoff between energy saving and precision. Finally, the simulation results with comparisons verify the convergence of the system and exhibit the accuracy with different quantizer densities. (c) 2021 Elsevier Inc. All rights reserved.
This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem ca...
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This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem can be solved without the information exchange of coupled constraints. The convergence analysis of the proposed algorithm is presented with the convergence condition through which a diminishing step-size with an upper bound can be determined. The convergence rate can be achieved at O(lnT/root T). Moreover, the equilibrium of this algorithm is proved to converge at the optimal solution of the global optimization problem. The effectiveness and the practicability of the proposed algorithm is demonstrated by the parameter optimization problem in smart building.
Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of t...
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In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence ***,an inertial fast-slow dynamical system with ...
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In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence ***,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is *** dual variables are updated in two time scales,i.e.,the fast manifold and the slow *** the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus *** the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial ***-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource *** simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.
Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in a...
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ISBN:
(纸本)9798350342734
Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss L-EC is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plugand-play nature of the EAE module in our method.
A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. ...
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The most commonly used implementation of handwritten digit recognition based on convolutional neural networks requires equipment with high computing power, which is not suitable for edge devices. Recently, spiking neu...
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Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physi...
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Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.
This article proposes a scheme aiming at solving the reconfiguration problem of distribution power network (DPN) with high wind power penetrations. The virtue of the presented scheme lies in balancing the voltage stab...
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This article proposes a scheme aiming at solving the reconfiguration problem of distribution power network (DPN) with high wind power penetrations. The virtue of the presented scheme lies in balancing the voltage stability and the absorption rate of wind energy. First, the DPN reconfiguration is formulated as a multi-objective optimization problem, where a curtailment strategy is introduced with the assistance of the secure operations of DPN. Thereby, the absorption rate of the generated wind power is maximized and voltage stability level is improved as well. Meanwhile, a modified multi-objective Bayesian learning-based evolutionary algorithm is applied to yield a Pareto front, which is a tradeoff between absorption rate and voltage stability. Afterwards, A technique for order preference by similarity to an ideal solution (TOPSIS) is adopted to determine the dispatching solution by similarity to an ideal solution. Finally, numerical case studies are conducted on a modified IEEE-33 bus system to verify the effectiveness of the proposed scheme.
We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is character...
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