In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of pos...
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In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of possible *** studies on decentralized robust diagnosis can only deal with different types of sensor failures separately,e.g.,all sensors suffer from the same type of sensor failures such as intermittent sensor failures or permanent sensor ***,since sensors of different local diagnosers may face different external environments and have different internal characteristics,sensors corresponding to different local diagnosers may also have their own fault *** this paper,we propose a flexible framework of decentralized diagnosis for DES subject to unreliable sensors such that sensors of different local diagnosers are permitted to have different types of sensor *** this end,we extend the existing decentralized diagnosis framework to the case where there exist common sensors broadcasting their observations to all local *** apply linear temporal logic(LTL) to constrain infinite behaviors of private sensors of local diagnosers and common ***,a new notion of φ-codiagnosability is proposed as the necessary and sufficient condition for the existence of a decentralized diagnoser that works correctly under sensors,satisfying LTL-based sensor ***,we provide an effective approach to verify the φ-codiagnosability.
In this paper, a distributed adaptive dynamic programming(ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting,players have no access to the information of other...
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In this paper, a distributed adaptive dynamic programming(ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting,players have no access to the information of others' system parameters or control laws. Each player adopts an on-policy value iteration algorithm as the basic learning framework. To deal with the incomplete information structure, players collect a period of system trajectory data to compensate for the lack of information. The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy. Theoretical analysis shows that by adopting proximal policy searching rules, the approximated policies can converge to a neighborhood of equilibrium policies. The efficacy of our method is illustrated by three examples, which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.
Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate bot...
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Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators(ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally,instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100), and improves the area under curve(AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.
This paper studies the connectivity-maintaining consensus of multi-agent *** the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy i...
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This paper studies the connectivity-maintaining consensus of multi-agent *** the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be *** this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching *** proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving *** the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving *** results indicate the effectiveness and advantages of our scheme.
Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian ...
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Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm(BADE)was *** first,we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling ***,the BADE algorithm was proposed to optimize the improved spread mechanism *** optimization algorithm is based on a novel adaptive differential evolution algorithm,which can effectively achieve the global optimal ***,the prediction performances of five machine learning algorithms were compared in *** results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms,which proves the effectiveness of the proposed method.
This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence *** players are divided into two groups in the learnin...
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This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence *** players are divided into two groups in the learning process and adapt their policies *** method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based ***,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control *** efficacy of our method is illustrated by three examples.
Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors (SPMSMs) due to its strong robustness against measurement and system noise. However, conventional meth...
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Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)*** this scheme,each legal voter performs unitary operation on corresponding position of particle seque...
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Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)*** this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting *** voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting *** improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also *** with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and ***,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.
We propose a new protocol for quantum teleportation(QT)which adopts the Brown state as the quantum *** work focuses on the teleportation of a single unknown two-qubit state via a Brown state channel in an ideal *** va...
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We propose a new protocol for quantum teleportation(QT)which adopts the Brown state as the quantum *** work focuses on the teleportation of a single unknown two-qubit state via a Brown state channel in an ideal *** validate the effectiveness of our proposed scheme,we conduct experiments by using the quantum circuit simulator ***,we investigate the effects of four noisy channels,namely,the phase damping noise,the bit-flip noise,the amplitude damping noise,and the phase-flip ***,we employ Monte Carlo simulation to elucidate the fidelity density under various noise *** analysis demonstrates that the fidelity of the protocol in a noisy environment is influenced significantly by the amplitude of the initial state and the noise factor.
Orbital angular momentum (OAM)-based dense space-division multiplexing technology has emerged as a promising candidate to break through the Shannon limit of single-mode fibers. While optical coordinate transformation ...
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