With an increase in the amount of waste electrical and electronic equipment (WEEE), the waste of resources and environmental hazards caused by WEEE cannot be ignored. Meanwhile, the lack of environmental awareness amo...
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With an increase in the amount of waste electrical and electronic equipment (WEEE), the waste of resources and environmental hazards caused by WEEE cannot be ignored. Meanwhile, the lack of environmental awareness among consumers and the existence of informal recyclers pose a great challenge to the government in governing the WEEE recycling industry. This study constructs a tripartite evolutionary game model consisting of the government and formal and informal recyclers. Then, the payoff matrix, replicator dynamic equations and all the equilibrium points are obtained, and a stability analysis of the equilibrium points is performed to derive the evolutionary stability strategies (ESSs) and their formation conditions. Finally, the influence of important parameters on the WEEE recycling industry is examined through numerical analysis. The results suggest that the government cannot ignore the existence of informal recyclers but should take governance measures to intervene in informal recycling and guide such recyclers to upgrade their processing technology. Moreover, the willingness of informal recyclers to invest in processing technology increases with the increase in environmental damage taxes. Second, the government should provide formal recyclers with appropriate promotional subsidies. Third, the government should control its own cost of governance and reduce its financial burden. Fourth, with government subsidies, formal recyclers should decide whether to make promotional investments based on the investment cost and the sum of the benefits from the investment and government subsidy. Finally, under government tax pressure and the influence of formal recyclers' promotional investments, informal recyclers should actively invest in processing technology.
Amidst the escalating challenges of global warming and energy crises, the rapid development of distributed renewable energy resources has emerged as a critical strategy. Regional integrated energy systems (RIESs) have...
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Amidst the escalating challenges of global warming and energy crises, the rapid development of distributed renewable energy resources has emerged as a critical strategy. Regional integrated energy systems (RIESs) have garnered significant attention for their potential to integrate and optimize both distributed renewable energy resources and conventional energy facilities. This article presents a bilevel optimization framework for the electricity-storage coupling market in multi-RIES, considering the integration of 6G network slicing technology and battery energy storage (BES) capacity sharing. The upper-level model maximizes the profit of generation units by optimizing their bidding strategies, while the lower-level model aims to maximize social welfare through market clearing. The proposed line search-based global Levenberg-Marquardt algorithm addresses the limitations of existing algorithms with necessary and innovative improvements to tackle the challenge of global convergence in nonsmooth optimization problems. Numerical case studies validate the effectiveness of the proposed framework, demonstrating enhanced BES utilization, increased renewable energy generation, and improved social welfare. The results also highlight the sensitivity of social welfare to communication costs, underscoring the importance of careful cost calibration.
This study addresses the complexities of orchestrating multi-target transportation tasks within multi-agent systems, constrained by load capacity. The primary objective is to engineer an advanced path planning framewo...
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The deep learning based methods have improved the visual tracking precision significantly. However, the background distraction and the high precise localization remain challenging problems. Despite that some methods h...
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The deep learning based methods have improved the visual tracking precision significantly. However, the background distraction and the high precise localization remain challenging problems. Despite that some methods have fused the deep and shallow layer features to solve these problems, the existing fusion methods, like simply concatenating or adding the features from the different layers, cannot take the advantage of both the deep and shallow layer features fully. In this paper, we propose a new adaptive feature fusion method, called the instance-based feature pyramid (IBFP) to obtain the discriminative high-resolution feature, which not only inherits the discriminative information from the deep layer feature, but also keeps the high precision localization information of the shallow layer feature. For utilizing the deep and shallow features effectively, we design an instance-based upsampling (IBU) module to fuse them, and a compressed space channel selection (CSCS) module to re-weight the feature channels adaptively. We insert the IBU and CSCS modules in the Siamese tracker for end-to-end training and testing. By using the proposed IBU and CSCS modules, we fuse the deep and shallow features in a series manner. Experiments on large-scale benchmark datasets demonstrate that the proposed modules boost the capabilities of distinguishing the targets and the similar distractors and perform favorably against the state-of-the-art.
storage as the cost of high-throughput DNA synthesis and DNA sequencing drops rapidly. In previous DNA data storage methods, the overall storage density is compromised as a specific portion of DNA bases are used as in...
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ISBN:
(纸本)9789819722716;9789819722723
storage as the cost of high-throughput DNA synthesis and DNA sequencing drops rapidly. In previous DNA data storage methods, the overall storage density is compromised as a specific portion of DNA bases are used as index segments to facilitate data recovery. In this work, a novel DNA storage strategy termed as Index-Free High-Density Convolutional Codes (IFHDCC) is introduced. Due to the serial coding nature of the convolutional code in the IFHDCC strategy, the correlation of data segment information between groups is fully exploited, the same organisational efficiency as index segment method is achived without sacrificing storage density. IFHDCC strategy generates DNA sequences compatible with current synthesis and sequencing technologies by superimposing pseudo-random sequences and employing row and column interleaving methods. To evaluate IFHDCC, we have constructed a dataset containing videos, images, audios, and documents. The computer simulation experiments conducted on the dataset show that the information density of our strategy is 1.75 bits/nt, which is a great improvement over the DNA Fountain strategy (1.57 bits/nt). Furthermore, the convolution matrix used in IFHDCC adds an additional layer of security, DNA sequences can only be decoded accurately with the correct convolution matrix, enhancing the security of information transmission.
作者:
Qiu, QianSu, HoushengHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Henan Univ
Sch Artificial Intelligence Zhengzhou 450046 Peoples R China
In this paper, the leader-follower robust synchronization issue is mainly addressed for reaction-diffusion neural networks (RDNNs) with multiple leaders and external disturbances under directed graphs. Based on the cr...
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In this paper, the leader-follower robust synchronization issue is mainly addressed for reaction-diffusion neural networks (RDNNs) with multiple leaders and external disturbances under directed graphs. Based on the cr modification approach, we propose a novel distributed adaptive controller by adding a term - 0 , ( c , - 1) 2 to avoid the phenomenon of parameter drift, that is, the adaptive parameters grow to infinity. Meanwhile, different from the adaptive control algorithm proposed in the undirected graph, we introduce a new function x , ( t ) to provide additional freedom for the design to achieve robust containment when confronted with external disturbances. Further, the robustness of tracking synchronization with one leader is guaranteed by the proposed adaptive controller when the external disturbances concerning L 2 norm are bounded. Finally, relevant numerical simulation graphics are displayed separately to verify the correctness of the related theoretical results.
作者:
Qiu, QianSu, HoushengHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ
Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Minist China
Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China
In this article, the exponential synchronization control issue of reaction-diffusion neural networks (RDNNs) is mainly resolved by the sampling-based event-triggered scheme under Dirichlet boundary conditions. Based o...
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In this article, the exponential synchronization control issue of reaction-diffusion neural networks (RDNNs) is mainly resolved by the sampling-based event-triggered scheme under Dirichlet boundary conditions. Based on the sampled state information, the event-triggered control protocol is updated only when the triggering condition is met, which effectively reduces the communication burden and saves energy. In addition, the proposed control algorithm is combined with sampled-data control, which can effectively avoid the Zeno phenomenon. By thinking of the proper Lyapunov-Krasovskii functional and using some momentous inequalities, a sufficient condition is obtained for RDNNs to achieve exponential synchronization. Finally, some simulation results are shown to demonstrate the validity of the algorithm.
In this article, the coordination control problem of discrete-time multiagent systems (MASs) affected by uncertainties, namely unknown initial states and external disturbances, is considered. Inspired by the interval ...
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In this article, the coordination control problem of discrete-time multiagent systems (MASs) affected by uncertainties, namely unknown initial states and external disturbances, is considered. Inspired by the interval observer constructed by the single system, the definition of distributed interval observer for discrete-time MASs is given, in which the control protocol of each agent obtained by solving a modified algebraic Riccati equation depends on the bounded information of the interval observer connected to itself and its neighbors. By the cooperativity theory and Lyapunov stability theory, it is established that the distributed interval observer can not only access some information about MASs at any instant, that is, the upper and lower bounds of each component of the agent state, but also realize the cooperative behavior of MASs under some essential conditions involving network synchronization and the unstable eigenvalue of the system matrix. In addition, with the help of a new time-varying transformation matrix, the new interval observer is constructed to eliminate the non-negative constraint. Finally, two numerical simulations are provided to confirm the validity of the derived results.
This article proposes a new cooperation framework of energy storage sharing that comprises prosumers, energy storage providers (ESPs), and a middle agent to achieve social energy optimality. In this framework, the pro...
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This article proposes a new cooperation framework of energy storage sharing that comprises prosumers, energy storage providers (ESPs), and a middle agent to achieve social energy optimality. In this framework, the prosumers share multiple energy storages of the ESPs via the agent. An energy sharing optimization problem minimizing the total energy cost is formulated involving the energy storage operation, the shiftable load schedule, and the energy trading with the utility. To solve the problem, a fast alternating direction multiplier method (ADMM) with restart which converges faster than traditional ADMM is adopted. Furthermore, a clearing scheme, named as an ideal profit realization ratio distribution model is introduced to distribute the participants' net profits fairly. Simulation results show that the energy costs can be substantially reduced, and the net profit distribution is relatively fairer compared with the widely used Nash bargaining scheme, which ensures the feasibility of our framework in real applications.
DC microgrid as a microscale power system has drawn growing attention for its various applications, which calls for good power quality, proper power sharing, and fast response property. However, the existing researche...
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DC microgrid as a microscale power system has drawn growing attention for its various applications, which calls for good power quality, proper power sharing, and fast response property. However, the existing researches are not yet able to achieve the purpose in a predefined time. In this article, a new predefined-time secondary controller is proposed for DC microgrid to realize the objectives of both voltage regulation and current sharing among DGs within a predefined time. The controller is designed by employing a sign function and a K-1 function and by defining a new composite error, which does not need to sample the voltage of DC bus or the current of neighbors, but only the local current and voltage. The upper bound of the convergence time of both the voltage and current in the DC microgrid is just related to one parameter which can be predefined and is independent of initial error. The effectiveness of the proposed controller is verified by multiple simulations and experiments built on MATlab/Simulink and a hardware-in-the-loop experimental platform. The conducted simulations and experiments include several typical scenarios together with a comparison, which illustrate the advantages of the proposed controller such as fast convergence rate and small overshoot.
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