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Development of the ReaxFF Reactive Force Field for Li/Mn/O Battery Technology with Application to Design a Self-Healing Cathode Electrolyte Interphase

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The Journal of Physical Chemistry A 2024年第16期128卷 6538-6550页

作者： Guiling He Qinqin Wei Siyu Gan Sizhao Huang Xihao Chen Jia Fu Ning Wang College of Science Key Laboratory of High-Performance Scientific Computation Xihua University Chengdu 610039 China School of Science Harbin University of Science and Technology Harbin 150006 China School of Materials Science and Engineering Chongqing University of Arts and Sciences Chongqing 402160 China

Cluster substitution is a novel strategy for optimizing the physical and chemical properties of functional materials, but it is not fully understood in related fields, especially in the fields of thermoelectricity and engineering of thermal management. The monolayer 1T-Ag6X2(X = S, Se, Te) is obtained by substituting the transition metal atoms in traditional two-dimensional transition metal sulfides (TMDs) with octahedral cluster Ag6. The phonon dispersion curves and ab initio molecular dynamics (AIMD) results show that monolayers 1T-Ag6S2and 1T-Ag6Se2have reliable stability. Therefore, the thermal transport performance of monolayers 1T-Ag6S2and 1T-Ag6Se2is studied systematically based on first principles. The results show that monolayers 1T-Ag6S2and 1T-Ag6Se2both have extremely low lattice thermal conductivity κl, 0.27 and 0.30 W/mK at room temperature, respectively, which are much lower than those of two-dimensional 1T-MX2and other two-dimensional materials. This is because, after the introduction of Ag6clusters, monolayers 1T-Ag6S2and 1T-Ag6Se2have more complex structures than traditional TMDs, with more tortuous phonon paths and greater phonon anharmonicity. It is also interesting to note that the contribution of optical and acoustic phonon modes to the total κlof monolayers 1T-Ag6S2and 1T-Ag6Se2is similar (usually, the κlof most two-dimensional materials is dominated by the acoustic component), which makes a reasonable explanation for the extremely low κlof these two materials. In this study, we further understand the important role of cluster substitution in regulating the thermal transport properties of two-dimensional materials and provide research ideas for designing new two-dimensional materials with low κl.

关键词： Heat transfer Monolayers Phonons Scattering Two dimensional materials

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A Point Cloud Optimization Algorithm based on Nonadjacent Low-altitude Remote Sensing Images 6

A Point Cloud Optimization Algorithm based on Nonadjacent Lo...

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6th International Conference on Computer Information science and Application Technology, CISAT 2023

作者： Yang, Nan Huang, Lei Chang, Haonan Key laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources 69 Xinwen Road Futian District Shenzhen518034 China Harbin Institute of Technology School of Transportation Science and Engineering 73 Huanghe Road Harbin150090 China

ISBN: (纸本)9781510668546

This paper presents a robust point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images. The proposed algorithm is designed to optimize the performance of point cloud generation in accuracy and efficiency. In order to accelerate the process of stereo matching and compute the coordinate of object point accurately, a pair of nonadjacent images is employed to lengthen the epipolar line of the image pair. Then a patch based Least Square Matching (LSM) method is utilized to search the optimal matching pixels and compute the coordinates of corresponding object points in 3D space. Comparison studies and experimental results in point cloud generation about low altitude remote sensing images have proved the effectiveness of the proposed algorithm. © 2023 SPIE.

关键词： Optimization

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Federated Learning With Client Clustering Selection and Quality-Aware Model Aggregation(2024)

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IEEE Internet of Things Journal 2025年

作者： Peng, Y. Wang, C. Shi, H. Ma, R. Guan, H. Yang, Hanbo Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

The Industrial Internet of Things (IIoT) is revolutionizing industries through device interconnectivity, enabling real-time data collection and transmission for enhanced monitoring, control, and automation. This has led to improvements in predictive maintenance, production optimization, and supply chain management. The integration of machine learning (ML) and deep learning (DL) within IIoT has been accelerated by increased data availability, computational advancements, and algorithmic innovations, with applications ranging from image processing to autonomous vehicles. Foundation models, such as ChatGPT, are becoming prevalent in IIoT for their capabilities in natural language processing and computer vision. However, the growth of IIoT and foundation models presents challenges, including data volume, real-time processing requirements, computational costs, and security vulnerabilities. Federated learning (FL) addresses these issues by allowing distributed model training without raw data transfer, enhancing privacy and security. FL is particularly beneficial for IIoT’s decentralized architecture and real-time decision-making needs. Despite the advantages, FL faces challenges such as data heterogeneity and communication overhead. To overcome these, we propose a federated learning framework, Fed-CCSQMA, which includes client selection and model aggregation modules to mitigate data heterogeneity’s impact. The client selection module uses Principal Component Analysis (PCA) and clustering to select clients with diverse yet representative data, while the model aggregation module assigns weights based on model accuracy to ensure faster global model convergence. We test our proposed framework on FMNIST and CIFAR-10 datasets, with accuracy surpassing baseline methods and faster convergence, demonstrating an improvement in overall generalizability and learning efficiency. © 2014 IEEE.

关键词： Decision making

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Regular Posit ion-Oriented Method for Mesh Smoothing

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Acta Mechanica Solida Sinica 2021年第3期34卷 437-448页

作者： Yongqing Hai Yufei Guo Siyuan Cheng Yunpeng Hai Department of Mechanics and Engineering Science Peking UniversityBeijing 100871China X Lab Second Academy of China Aerospace Science and Industry CorporationBeijing 100854China State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300072China A College of Electromechanical Engineering Qingdao University of Science and TechnologyQingdao 266061China

Mesh smoothing is an essential technique for the improvement of mesh quality in finite element analysis,due to the fact that mesh quality has a large impact on the convergence of the computational scheme and the accuracy of the numerical results.A novel mesh smoothing method based on regular-position-guided operations is presented in this *** method introduced here contains two main stages:The first stage computes the regular position of each vertex based on the shape of the element and conducts regular-position-oriented-based element transformations independently;the second stage determines the finial position of each vertex according to its surrounding elements with an assembly *** method is not limited to planar triangular mesh,but applicable to surface polygonal *** experiments on various mesh models demonstrate the effectiveness and potential of this method.

关键词： Mesh smoothing Element transformation Finite element analysis Mesh quality

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A CONSERVATIVE RELAXATION CRANK-NICOLSON FINITE ELEMENT METHOD FOR THE SCHRÖDINGER-POISSON EQUATION

arXiv

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arXiv 2024年

作者： Liu, Huini Yi, Nianyu Yin, Peimeng School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China Department of Mathematical Sciences The University of Texas at El Paso El PasoTX79968 United States

In this paper, we propose a novel mass and energy conservative relaxation Crank-Nicolson finite element method for the Schrödinger-Poisson equation. Utilizing only a single auxiliary variable, we simultaneously reformulate the distinct nonlinear terms present in both the Schrödinger equation and the Poisson equation into their equivalent expressions, constructing an equivalent system to the original Schrödinger-Poisson equation. Our proposed scheme, derived from this new system, operates linearly and bypasses the need to solve the nonlinear coupled equation, thus eliminating the requirement for iterative techniques. We in turn rigorously derive error estimates for the proposed scheme, demonstrating second-order accuracy in time and (k+1)th order accuracy in space when employing polynomials of degree up to k. Numerical experiments validate the accuracy and effectiveness of our method and emphasize its conservation properties over long-time *** Codes 35Q55, 65M15, 65M60 © 2024, CC BY-NC-ND.

关键词： Poisson equation

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Dynamic ModeⅡfracture behavior of rocks under hydrostatic pressure using the short core in compression(SCC)method

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International Journal of Mining science and Technology 2021年第5期31卷 927-937页

作者： Wei Yao Ying XuChonglang Wang Kaiwen Xia Mikko Hokka State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil EngineeringTianjin UniversityTianjin 300072China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China Engineering Materials Science Faculty of Engineering and Natural SciencesTampere University(TAU)POB 589FI-33014 TampereFinland Department of Civil&Mineral Engineering University of TorontoTorontoON M5S 1A4Canada

The shear failure of rocks under both a static triaxial stress and a dynamic disturbance is common in deep underground engineering and it is therefore essential for the design of underground engineering to quantitively estimate the dynamic ModeⅡfracture toughness KⅡCof rocks under a triaxial stress ***,the method for determining the dynamic KⅡCof rocks under a triaxial stress has not been developed *** an optimal sample preparation,the short core in compression(SCC)method was designed and verified in this study to measure the dynamic KⅡCof Fangshan marble(FM)subjected to different hydrostatic pressures through a triaxial dynamic testing *** formula for calculating the dynamic KⅡCof the rock SCC specimen under hydrostatic pressures was obtained by using the finite element method in combination with secondary *** experimental results indicate that the failure mode of the rock SCC specimen under a hydrostatic pressure is the shear fracture and the KⅡCof FM increases as the loading *** addition,at a given loading rate the dynamic rock KⅡCis barely affected by hydrostatic *** important observation is that the dynamic fracture energy of FM enhances with loading rates and hydrostatic pressures.

关键词： Loading rate Finite element method ModeⅡfracture toughness Fangshan marble Hydrostatic pressure Short core in compression

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arXiv

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arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Critical Laplace Pressure and Dewetting Tansition of Aluminum-Based Superhydrophobic Surfaces Textured with a Nanosecond Laser

SSRN

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SSRN 2024年

作者： Wang, Guoyong Xu, Zhengzheng Li, Danqing Jiang, Yue Jiang, Zhonghao Liu, Yan Yang, Chun Cheng Key Laboratory of Automobile Materials Department of Materials Science and Engineering Jilin University Changchun130025 China Key Laboratory of Bionic Engineering Ministry of Education College of Biological and Agricultural Engineering Jilin University Changchun130025 China Key Laboratory of Bionic Engineering Ministry of Education State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China

In critical areas such as aerospace, where the formation of frost can pose a serious threat to safety, delaying and preventing its formation has become extremely important. Although it has been shown that superhydrophobic surfaces (SHs) are capable of delaying ice formation, these surfaces have challenges in maintaining the stability of the Cassie-Baxter (CB) state, especially in high humidity and freezing environments, which limits their application in anti-icing and de-icing. To deal this issue, in this study, a low-cost near-infrared nanosecond laser technique was used instead of the more expensive ultrafast femtosecond laser by inducing the formation of three different micro/nanostructured superhydrophobic surfaces on the aluminum surface. The critical Laplace pressure of these surfaces was tested by evaporation experiments. Further icing/melting cycle experiments observed the ability of these superhydrophobic surfaces to recover from the CB state. The experimental results showed that the surfaces with higher critical Laplace pressures were more likely to recover the CB state under the same conditions, implying that they were better able to maintain their superhydrophobic properties after undergoing the icing/melting process. The results not only deepen our understanding of icing and melting phenomena, but also provide important scientific guidance for the design of superhydrophobic anti-icing surfaces with high CB state stability. These findings are of great practical application value and scientific significance for the development of new anti-icing materials, especially in aerospace and other fields that require high safety. © 2024, The Authors. All rights reserved.

关键词： Recovery

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Joint-Motion Mutual Learning for Pose Estimation in Video 24

Joint-Motion Mutual Learning for Pose Estimation in Video

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A STAR Singapore Singapore Georgia Institute of Technology AtlantaGA United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798400706868

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. © 2024 ACM.

关键词： Motion estimation

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Utilization of Energy Storage and Hydrogen in Power and Energy Systems:Viewpoints from Five Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 1-7页

作者： Yonghua Song Mohammad Shahidehpour Saifur Rahman Nigel Brandon Kai Strunz Jin Lin Yuxuan Zhao State Key Laboratory of Internet of Things for Smart City University of MacaoTaipaMacao 999078China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Galvin Center for Electricity Innovation Illinois Institute of TechnologyChicagoIL 60616USA Department of Advanced Research Institute Virginia Polytechnic Institute and State UniversityArlingtonVA 22203USA Department of Earth Science and Engineering Imperial College LondonLondon SW72AZU.K Department of Sustainable Electric Networks and Sources of Energy Technische Universitt Berlin10587 BerlinGermany State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon *** renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization ***,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power *** storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of *** the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage ***,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were *** experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.

关键词： Electrolysis electric vehicle energy storage flow battery fuel cell electric vehicle hydrogen energy

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