The design of thermal conductivity enhancers(TCE) is quite critical to overcoming the disadvantage of the poor thermal conductivity of phase change materials(PCM).The main contribution of this study is firstly to disc...
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The design of thermal conductivity enhancers(TCE) is quite critical to overcoming the disadvantage of the poor thermal conductivity of phase change materials(PCM).The main contribution of this study is firstly to discuss how to actively enhance natural convection of the melted PCM in cellular structure by the fin formed in the structure under the condition of the same metal mass,apart from simultaneously improving heat conduction,which can boost the heat transfer ***,a tailored hybrid fin-lattice structure(HFS) as TCE is designed and fabricated by additive manufacturing(AM).A two-equation numerical method is applied to study the heat transfer of the PCM,and its feasibility is validated with the experimental *** numerical results indicate that enhanced natural convection and improved heat conduction can be obtained simultaneously when a well-designed fin is embedded into a lattice *** enhanced natural convection results in the improved melting rate and the decreased wall temperature;e.g.,the complete melting time and the wall temperature are reduced by 11.6% and 19.7%,respectively,because of the fin for metal ***,the parameters of HFS including the porosity,pore density,and fin dimension have a great impact on the heat *** enhancement effect of the fin for HFS on the melting rate of the PCM increases as the thermal conductivity of the base material *** example,when the fin is introduced into the lattice structure,the complete melting time is reduced by 24.1% for metal *** summary,this study enables us to obtain a good understanding of the mechanism of the heat transfer and provides necessary experimental data for the structural design of HFS fabricated by AM.
Flourishing rare earth superhydrides are a class of recently discovered materials that exhibit near-room-temperature superconductivity at high pressures,ushering in a new era of superconductivity research at high *** ...
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Flourishing rare earth superhydrides are a class of recently discovered materials that exhibit near-room-temperature superconductivity at high pressures,ushering in a new era of superconductivity research at high *** superhydrides drew the most attention among these superhydrides due to their abundance of stoichiometries and excellent ***,we carried out a comprehensive study of yttrium superhydrides in a wide pressure range of 140 GPa-300 *** successfully synthesized a series of superhydrides with the compositions of YH_(4),YH_(6),YH_(7),and YH_(9),and reported superconducting transition temperatures of 82 K at 167 GPa,218 K at 165 GPa,29 K at 162 GPa,and230 K at 300 GPa,respectively,as evidenced by sharp drops in *** structure and superconductivity of YH_(4) were taken as a representative example and were also examined using x-ray diffraction measurements and the superconductivity suppression under external magnetic fields,*** YH_(10),a candidate for room-temperature superconductor,was not synthesized within the study pressure and temperature ranges of up to 300 GPa and 2000 *** current study established a detailed foundation for future research into room-temperature superconductors in polynary yttrium-based superhydrides.
Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training ...
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Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.
Model-based reinforcement learning is a promising direction to improve the sample efficiency of reinforcement learning with learning a model of the *** model learning methods aim at fitting the transition data,and com...
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Model-based reinforcement learning is a promising direction to improve the sample efficiency of reinforcement learning with learning a model of the *** model learning methods aim at fitting the transition data,and commonly employ a supervised learning approach to minimize the distance between the predicted state and the real *** supervised model learning methods,however,diverge from the ultimate goal of model learning,i.e.,optimizing the learned-in-the-model *** this work,we investigate how model learning and policy learning can share the same objective of maximizing the expected return in the real *** find model learning towards this objective can result in a target of enhancing the similarity between the gradient on generated data and the gradient on the real *** thus derive the gradient of the model from this target and propose the Model Gradient algorithm(MG)to integrate this novel model learning approach with policy-gradient-based policy *** conduct experiments on multiple locomotion control tasks and find that MG can not only achieve high sample efficiency but also lead to better convergence performance compared to traditional model-based reinforcement learning approaches.
Machine learning algorithms provide new ways to solve the problem of stock prediction. Among them, the decision tree algorithm has been widely used in financial market prediction because of its strong interpretability...
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The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance st...
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The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance structural ***,topology results often contain microstructures(several multicellular scales)similar to the characteristic length of local macrostructures,leading to errors in structural performance analysis based on classical ***,it is necessary to consider the size effect in topology *** this paper,we establish a novel topology optimization model utilizing the integral nonlocal theory to account for the size *** approach consists of an integral constitutive model that incorporates a kernel function,enabling the description of stress at a specific point in relation to strain in a distant *** optimization structures based on nonlocal theory are presented for some benchmark examples,and the results are compared with those based on classical medium *** material layout exhibits significant differences between the two approaches,highlighting the necessity of topology optimization based on nonlocal theory and the effectiveness of the proposed method.
Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in a complex environment. However, the increase in the population size...
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Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature...
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With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a ...
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With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.
Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning(MARL).Nowadays,existing works mainly focus on improving the communication efficiency of agents,neglecting that real-world commun...
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Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning(MARL).Nowadays,existing works mainly focus on improving the communication efficiency of agents,neglecting that real-world communication is much more challenging as there may exist noise or potential *** the robustness of the communication-based policies becomes an emergent and severe issue that needs more *** this paper,we posit that the ego system trained with auxiliary adversaries may handle this limitation and propose an adaptable method of Multi-Agent Auxiliary Adversaries Generation for robust Communication,dubbed MA3C,to obtain a robust communication-based *** specific,we introduce a novel message-attacking approach that models the learning of the auxiliary attacker as a cooperative problem under a shared goal to minimize the coordination ability of the ego system,with which every information channel may suffer from distinct message ***,as naive adversarial training may impede the generalization ability of the ego system,we design an attacker population generation approach based on evolutionary ***,the ego system is paired with an attacker population and then alternatively trained against the continuously evolving attackers to improve its robustness,meaning that both the ego system and the attackers are *** experiments on multiple benchmarks indicate that our proposed MA3C provides comparable or better robustness and generalization ability than other baselines.
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