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Dynamical nonlinear excitations induced by interaction quench in a two-dimensional box-trapped Bose-Einstein condensate

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Chinese Physics B 2024年第2期33卷 209-215页

作者：牛真霞高超 department of Physics Zhejiang Normal UniversityJinhua 321004China Key Laboratory of Optical information Detection and Display technology of Zhejiang Zhejiang Normal UniversityJinhua 321004China

Manipulating nonlinear excitations,including solitons and vortices,is an essential topic in quantum many-body physics.A new progress in this direction is a protocol proposed in[***.2043256(2020)]to produce dark solitons in a one-dimensional atomic Bose–Einstein condensate(BEC)by quenching inter-atomic *** by this work,we generalize the protocol to a two-dimensional BEC and investigate the generic scenario of its post-quench *** an isotropic disk trap with a hard-wall boundary,we find that successive inward-moving ring dark solitons(RDSs)can be induced from the edge,and the number of RDSs can be controlled by tuning the ratio of the after-and before-quench interaction strength across different critical *** role of the quench played on the profiles of the density,phase,and sound velocity is also *** to the snake instability,the RDSs then become vortex–antivortex pairs with peculiar dynamics managed by the initial density and the after-quench *** tuning the geometry of the box traps,demonstrated as polygonal ones,more subtle dynamics of solitons and vortices are *** proposed protocol and the discovered rich dynamical effects on nonlinear excitations can be realized in near future cold-atom experiments.

关键词： Bose-Einstein condensate quench interaction soliton vortex

MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic information School Wuhan UniversityWuhan 430072China School of Computer Science and technology Harbin Institute of TechnologyHarbin 150001China department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

Cavitation research with computational fluid dynamics:From Euler-Euler to Euler-Lagrange approach

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Journal of Hydrodynamics 2024年第1期36卷 1-23页

作者： Bin Ji Zi-yang Wang Huai-yu Cheng Rickard E.Bensow State Key Laboratory of Water Resources Engineering and management Wuhan UniversityWuhan430072China department of Mechanics and Maritime Sciences Chalmers University of TechnologyGothenburgSweden

Unsteady cavitating flow often contains vapor structures with a wide range of different length scales,from micro-bubbles to large cavities,which issues a big challenge to precisely investigate its evolution mechanism by computational fluid dynamics(CFD)*** present work reviews the development of simulation methods for cavitation,especially the emerging Euler-Lagrange ***,the progress of the numerical investigation of hot and vital issues is discussed,including cavitation inception,cloud cavitation inner structure and its formation mechanism,cavitation erosion,and cavitation *** is indicated that the Euler-Lagrange method can determine cavitation inception point *** cloud cavitation,the Euler-Lagrange method can reveal the source of microbubbles and their distribution law inside the shedding *** method also has advantages and great potential in assessing cloud cavitation-induced erosion and *** the ever-growing demands of cavitation simulation accuracy in basic research and engineering applications,how to improve the Euler-Lagrange method’s stability and applicability is still an open *** further promote the application of this advanced CFD simulation technology in cavitation research,some key issues are to be solved and feasible suggestions are put forward for further work.

关键词： numerical simulation Euler-Euler method Euler-Lagrange method bubble dynamicsgy,Gothenburg,Sweden

Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization

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Tsinghua Science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

Peer-to-Peer Trading in Distribution Systems with the Utility's Operation

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 629-638页

作者： Xuanyi Xiao Feng Wang Mohammad Shahidehpour Yujia Zhai Quan Zhou department of Electrical and information Engineering Hunan UniversityChangshaChina Robert W.Galvin Center for Electricity Innovation Ilinois Institute of TechnologyChicagoIL60616USA

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

关键词： Distribution system peer-to-peer trading electricitymarket distributed energyresources(DERs) analytical target cascading(ATC)

Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication

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Chinese Optics Letters 2024年第6期22卷 116-122页

作者： Haoyu Zhang Li Yao Chaoxu Chen Yuan Wei Chao Shen Jianyang Shi Junwen Zhang Ziwei Li Nan Chi Key Laboratory for information Science of Electromagnetic Waves(MoE) Department of Communication Science and EngineeringSchool of Information Science and TechnologyFudan UniversityShanghai 200433China Shanghai CIC of LEO Satellite Communication technology Fudan UniversityShanghai 200433China Shanghai ERC of LEO Satellite Communication and Application Fudan UniversityShanghai 200433China

This Letter proposes a post-equalizer for underwater visible light communication(UVLC) systems that combines channel estimation and joint time-frequency analysis, named channel-estimation-based bandpass variable-order time-frequency network(CBV-TFNet). By utilizing a bandpass variable-order loss function with communication prior knowledge, CBVTFNet enhances communication performance and training stability. It enables lightweight implementation and faster convergence through a channel estimation-based mask. The superior performance of the proposed equalization method over Volterra and deep neural network(DNN)-based methods has been studied experimentally. Using bit-power loading discrete multitone (DMT) modulation, the proposed method achieves a transmission bitrate of 4.956 Gbps through a 1.2 m underwater channel utilizing only 38.15% of real multiplication calculations compared to the DNN equalizer and achieving a bitrate gain of440 Mbps and a significantly larger dynamic range over the LMS-Volterra equalizer. Results highlight CBV-TFNet's potential for future post-equalization in UVLC systems.

关键词： underwater visible light communication channel estimation post-equalizer neural network

Metallic Sb-stabilized porous silicon with stable SEI and high electron/ion conductivity boosting lithium-ion storage performance

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Rare Metals 2024年第9期43卷 4234-4242页

作者： Jia-Guo Deng Hao-Qin Feng Yu-Long Xu Si-Guang Guo Jian-Ping Li Kai-Fu Huo Ji-Jiang Fu Biao Gao Pual-K.Chu The State Key Laboratory of Refractories and Metallurgy and Institute of Advanced Materials and Nanotechnology Wuhan University of Science and Technology Wuhan National Laboratory for Optoelectronics (WNLO) School of Optical and Electronic InformationHuazhong University of Science and Technology department of Physics Department of Materials Science & Engineeringand Department of Biomedical EngineeringCity University of Hong Kong

Silicon （Si） has mild discharge potential and high theoretical capacity,making it a highly desirable material for lithium-ion batteries （LIBs）.Nevertheless,the excessive volume expansion,poor ion/electron conductiv ity and unstable solid electrolyte interface （SEI） hinde practical application to ***,the metallic antimony（Sb） stabilized porous Si （SiDSb） composite was prepared by magnesiothermic reduction of Sb2O3and Mg2Si and chemical etching to remove the by-product of *** highly conductive Sb nanodots embedded in the Si liga ments promote not only the formation of conductive and stable LiF-rich SEI,but also the electron/ion transpor ability of *** to the outstanding bulk/interface stability,excellent conductivity,as well as ideal porous structure,the SiDSb electrode demonstrates a capacity of820 mAh·g-1after undergoing 320 turns at 1000 mA·***,it exhibits a stable capacity of 675 m Ah·g-1when tested at a higher current density of 5000 m A·*** results reveal a viable solution to solve three problems at the same time,namely the poor conductivity,inferior SEI and excessive volume expansion of Si,boding well for the design of Si-based materials for high-energy LIBs.

关键词： Anodes Antimony Lithium-ion batteries Silicon Solid electrolyte interphase

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

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Computers, Materials & Continua 2024年第6期79卷 3783-3803页

作者： Yi-Chun Lai Shu-Yin Chiang Yao-Chiang Kan Hsueh-Chun Lin department of Public health China Medical UniversityTaichung406040Taiwan department of information and Telecommunications Engineering Ming Chuan UniversityGui-ShanTaoyuan333Taiwan department of Electrical Engineering Yuan Ze UniversityChung-Li32003Taiwan department and Institute of health Service Administrations China Medical UniversityTaichung406040Taiwan

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation *** study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in *** analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the ***,the Mhealth database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and *** ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data *** contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature ***,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model *** best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,*** analysis using the Mhealth dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,***,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research *** conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

关键词： Human activity recognition artificial intelligence support vector machine random forest adaptive neuro-fuzzy inference system convolution neural network recursive feature elimination

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

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Proceedings of Business and Economic Studies 2024年第4期7卷 269-274页

作者： Mi Liu Jia Yu Yan Li Jun Zheng department of Business management of Shanghai information technology College Shanghai 20033China School of information Science and technology Shanghai Shanda UniversityShanghai 20033China

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)***,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were *** a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

关键词： Mobile social e-commerce Little Red Book Female community platform AI User experience UGC