Communication atmosphere in Human-Robot Interaction(HRI) is estimated by integrating emotional states of humans and robots based on the concept of Fuzzy Atmosfleld(FA),where human emotion is estimated from bimodal...
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ISBN:
(纸本)9781538629185
Communication atmosphere in Human-Robot Interaction(HRI) is estimated by integrating emotional states of humans and robots based on the concept of Fuzzy Atmosfleld(FA),where human emotion is estimated from bimodal communication cues(i.e.,speech and facial expression) and robot emotion is generated by emotional expression *** Analytical Hierarchy Process(FAHP) is used for dynamic weight calculation of emotional states of humans and robots in the FA model,in which a three-level hierarchy is adopted for the analysis of communication atmosphere and four acoustic cues(i.e.,volume,rate,pitch,and duration) are utilized to calculate the dynamic *** is conducted in a multi-modal emotional communication based humans-robots interaction(MEC-HRI) system,by which experimental results demonstrate the validity of proposed FA *** the communication atmosphere can help to facilitate smooth communication between humans and robots by adjusting robot behavior for emotion *** proposal is being planned for an atmosphere representation system for realizing causal communication in HRI and background music is being considered for the FA model.
In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for trac...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking obj *** solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well,we propose a Siamese network-based tracker with position attention network(SiamPA).First,we use Siamese backbone network to extract template and search region ***,we adopt the boxguided object feature selection strategy to avoid similarity calculations for background *** addition,we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region ***,the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the *** contribution,one is to propose a box-guided method for refining object features,and the other is to introduce a position attention network for information *** on three challenging benchmarks including GOT-10 k,UAV123 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.
This paper concerns the stability problem of singular systems with time-varying delay. According to different delaypartition intervals, an augmented Lyapunov-Krasovskii functional with delay cross terms is constructed...
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ISBN:
(纸本)9781538629185
This paper concerns the stability problem of singular systems with time-varying delay. According to different delaypartition intervals, an augmented Lyapunov-Krasovskii functional with delay cross terms is constructed. Based on it, using the relaxed integral inequality technique is to obtain a less conservative stability criterion. As a result, two numerical examples are provided to demonstrate the effectiveness of the proposed method.
This paper present a prediction model for three different objection(the airflow rate,the carbonaceous biochemical oxygen demand(CBOD) of the effluent,and the total suspend solids(TSS) of the *** model is built by the ...
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ISBN:
(纸本)9781538629185
This paper present a prediction model for three different objection(the airflow rate,the carbonaceous biochemical oxygen demand(CBOD) of the effluent,and the total suspend solids(TSS) of the *** model is built by the MLP neural *** accurancy of the prediction result of MLP neural network is compared with the accurancy of the result of trational stational autoregressive model(AR).The conclution is that the percentage error prediction model of the MLP neural network(PE),the fractional deviation(FB),normalized mean square error(NMSE),the mean absolute error(MAE) and mean square error(MSE) prediction model of evaluation index is better than the AR *** other words,the prediction model based on MLP neural network provides a reliable basis for reducing the energy consumption in the activated sludge process of industrial waste water treatment and further improving its effect on the treatment of industrial waste water.
A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and *** the dynamics of physical agents are so complex that the environment of learning is indeed stocha...
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ISBN:
(纸本)9781538629185
A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and *** the dynamics of physical agents are so complex that the environment of learning is indeed stochastic,the paper introduces the decentralized multi-agent reinforcement learning(MARL) algorithm,named as Decentralized Concurrent Learning with Cooperative Policy Exploration(DCL-CPE),in order to solve cooperative learning within stochastic *** investigate its feasibility in practical multi-agent systems,the box-pushing test with DCL-CPE is designed with a group of two-wheel driven robots acting as learning *** to physical properties,such as nonholonomic dynamics,rolling and sliding frictions,unreliable sense,rigid body collision,etc.,the cooperative learning is a high stochastic learning *** simulation test in Webots shows that DCL-CPE is good at exploring best cooperative policy in a decentralized way,even as state transition and rewards are all stochastic.
This study aims to investigate the problem of attitude control for a spacecraft with inertial uncertainties, external disturbances, and communication restrictions. An event-triggered active disturbance rejection contr...
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This study aims to investigate the problem of attitude control for a spacecraft with inertial uncertainties, external disturbances, and communication restrictions. An event-triggered active disturbance rejection control approach is proposed for attitude tracking of the spacecraft. An event-triggered mechanism is introduced together with an extended state observer to jointly monitor the system states and total disturbances. The observation error is proved to be uniformly bounded. Based on the proposed control scheme,the integrated tracking system is shown to be asymptotically stable, implying successful attitude tracking of the spacecraft for the desired motion. Numerical results illustrate the effectiveness of the control strategy in achieving satisfactory tracking performance with a reduced data-transmission cost.
This article studies the almost-sure and the mean-square consensus control problems of second-order stochastic discrete-time multi-agent systems with multiplicative ***,a control law based on the absolute velocity and...
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This article studies the almost-sure and the mean-square consensus control problems of second-order stochastic discrete-time multi-agent systems with multiplicative ***,a control law based on the absolute velocity and relative position information is ***,considering the existence of multiplicative noises and nonlinear terms with Lipschitz constants,the consensus control problem is solved through the use of a degenerated Lyapunov ***,for the linear second-order multi-agent systems,some explicit consensus conditions are ***,two sets of numerical simulations are performed.
We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remot...
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We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.
A novel adaptive robust control (ARC) is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximat...
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A novel adaptive robust control (ARC) is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.
Since computer system functions are becoming increasingly complex, the user has to spend much more time on the process of seeking information, instead of utilizing the required infor- mation. Information intelligent p...
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Since computer system functions are becoming increasingly complex, the user has to spend much more time on the process of seeking information, instead of utilizing the required infor- mation. Information intelligent push technology could replace the traditional method to speed up the information retrieval process. The fuzzy cognitive map has strong knowledge representation ability and reasoning capability. Information intelligent push with the basis on fuzzy cognitive map could ab- stract the computer user' s operations to a fuzzy cognitive map, and infer the user' s operating inten- tions. The reasoning results will be translated into operational events, and drive the computer system to push appropriate information to the user.
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