Surface-mount technology (SMT) is the technology used in the production of printed circuit boards (PCB) plays a vital role in PCB manufacturing for applications ranging from communication devices to medical systems. A...
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With the increase in the amount of tasks offtoaded to the network edge, the energy supply of edge devices has become a challenge worthy of attention. It is a feasible way to use renewable energy to supply energy for e...
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With the increase in the amount of tasks offtoaded to the network edge, the energy supply of edge devices has become a challenge worthy of attention. It is a feasible way to use renewable energy to supply energy for edge devices, but production of renewable energy has certain uncertainty and stochasticity. In order to provide sufficient energy to ensure stable operation of edge devices, energy Internet (EI) provides an idea, that is, different edge devices are connected with distributed small energy supply and storage systems. As the core equipment of energy Internet, energy router (ER) plays an important role in information transmission, energy transmission and system control. In this paper, the concept of edge energy router is proposed, which has the ability of task computing and scheduling similar to edge computing server, as well as the ability of energy transmission and system control of energy router. Each edge energy router is connected with loads, photovoltaic panel (PV), micro turbine (MT) and battery energy storage (BES) to form a self-sufficient microgrid (MG) system. However, there exists a delay in energy transmission and task scheduling between different ERs. Moreover, the DC bus voltage stability of each edge energy router system is negatively affected by internal uncertainty, stochasticity and external interference. Therefore, the system is modeled by Markov jump ODEs with time delay, and robust control of DC bus voltage deviation is discussed in this paper. The linear matrix inequality (LMI) method is used to solve this Markov jump control problem. Finally, numerical simulations show the effectiveness of the proposed method.
This research work thoroughly describes the process of obtaining and characterizing iron oxide (Fe3O4) nanoparticles as well as emphasizes how different pH levels, reaction environments and synthesis methods can respe...
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In industrial automation and intelligence, fault tolerance mechanisms have always been an attractive topic. To develop soft sensors with fault tolerance for different types of faults and unforeseen new faults, this ar...
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Autonomous decision-making is crucial for aircraft to achieve quick victories in diverse scenarios. Based on a 6-degree-of-freedom aircraft model, this paper proposes a decoupled guidance and control theory for autono...
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Autonomous decision-making is crucial for aircraft to achieve quick victories in diverse scenarios. Based on a 6-degree-of-freedom aircraft model, this paper proposes a decoupled guidance and control theory for autonomous aircraft maneuvering, distinguishing between close and long-range engagements. We introduce a method for heading attitude control to enhance stability during close-range interactions and a speed-based adaptive grid model for precise waypoint control in mid-to-long-range engagements. The paper transforms dynamic aircraft interactions into a Markov decision process and presents a hybrid discrete and continuous action reinforcement learning approach. This unified learning framework offers enhanced generalization and learning speed for dynamic aircraft adversarial processes. Experimental results indicate that in a symmetric environment, our approach rapidly achieves Nash equilibrium, securing over a 10% advantage. In unmanned aerial aircraft game control with higher maneuverability, the probability of gaining a situational advantage increases by more than 40%. Compared to similar methods, our approach demonstrates superior effectiveness in decision optimization and adversarial success ***, we validate the algorithm's robustness and adaptability in an asymmetric environment, showcasing its promising application potential in collaborative control of aircraft clusters.
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision *** address the problem of Integrated Evasion and Impact(IEI)decision under m...
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Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision *** address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was ***,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision ***,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed *** results show that the proposed model has good performance and low computational resource *** minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
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.
In rotor manufacturing for Roots pumps, precision and production time are critical. When a rotor shaft has two stages with claw-type and Roots profiles, traditional forming methods are unsuitable for the Roots rotor (...
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The sintering process is crucial for the preparation of ternary cathode materials (TCMs), which need to be precisely monitored to ensure the production of high-quality products. Nevertheless, the sintering process of ...
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Hydrogen has attracted growing research interest due to its exceptionally high energy per mass content and being a clean energy carrier, unlike the widely used hydrocarbon fuels. With the possibility of long-term ener...
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Hydrogen has attracted growing research interest due to its exceptionally high energy per mass content and being a clean energy carrier, unlike the widely used hydrocarbon fuels. With the possibility of long-term energy storage and re-electrification, hydrogen promises to promote the effective utilization of renewable and sustainable energy resources. Clean hydrogen can be produced through a renewable-powered water electrolysis process. Although alkaline water electrolysis is currently the mature and commercially available electrolysis technology for hydrogen production, it has several shortcomings that hinder its integration with intermittent and fluctuating renewable energy sources. The proton exchange membrane water electrolysis (PEMWE) technology has been developed to offer high voltage efficiencies at high current densities. Besides, PEMWE cells are characterized by a fast system response to fluctuating renewable power, enabling operations at broader partial power load ranges while consistently delivering high-purity hydrogen with low ohmic losses. Recently, much effort has been devoted to improving the efficiency, performance, durability, and economy of PEMWE cells. The research activities in this context include investigations of different cell component materials, protective coatings, and material characterizations, as well as the synthesis and analysis of new electrocatalysts for enhanced electrochemical activity and stability with minimized use of noble metals. Further, many modeling studies have been reported to analyze cell performance considering cell electrochemistry, overvoltage, and thermodynamics. Thus, it is imperative to review and compile recent research studies covering multiple aspects of PEMWE cells in one literature to present advancements and limitations of this field. This article offers a comprehensive review of the state-of-the-art of PEMWE cells. It compiles recent research on each PEMWE cell component and discusses how the characteristi
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