作者：
Wang, YufeiXiao, BaihuaChinese Academy of Sciences
University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences
The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China
Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the...
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The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the ne...
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The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.
WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoin...
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WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.
This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnos...
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This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnosis problem with insufficient *** found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data.A straightforward solution is to use massive amounts of normal data to drive the diagnostic *** introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data.A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal *** uses a generator network as a feature extractor,and uses a discriminator network as a fault probability evaluator,which creates a new use of GAN in the field of fault *** the many learning strategies of GAN,we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the *** also design a fault location method based on binary search,which greatly improves the search efficiency and engineering value of the entire *** have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working *** compared FD-GAN with popular diagnostic *** results show that our method has the highest accuracy and recall rate.
BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan...
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BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.
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.
Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram...
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Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research framework that unifies the explanation and perception of dynamic and complex scenes.
This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforc...
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This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE
Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation *** approaches require traffic signal professionals to...
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Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation *** approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and ***,this process is cumbersome,labor-intensive,and cannot be applied on a large network *** studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and *** a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been *** this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.
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