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Superior stability of Li5Mg@Cu anodes for lithium metal batteries:Investigating the suppression effects of magnesium on lithium dendrite growth

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材料科学技术（英文版） 2025年第8期211卷 288-302页

作者： Ruijun Yao Zhuoyu Li Longke Bao Rui Deng Kai Zheng Yiming Hu Jiahui Li Hao Zhang Shaobo Tu Rongpei Shi Junwei Wu Changming Li Xingjun Liu College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Process Engineering Graduate School of EngineeringNagoya UniversityFuro-choChikusa-kuNagoya464-8603Japan School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China 21C Innovation Laboratory(21C LAB) Contemporary Amperex Technology Ltd.Ningde 352100China Institute for Materials Science and Devices Suzhou University of Science and TechnologySuzhou 215011China

Li metal is widely recognized as the desired anode for next-generation energy storage,Li metal batteries,due to its highest theoretical capacity and lowest ***,it suffers from unstable elec-trochemical behaviors like dendrite growth and side reactions in practical ***,we report a highly stable anode with collector,Li5Mg@Cu,realized by the melting-rolling *** Li5Mg@Cu anode delivers ultrahigh cycle stability for 2000 and 1000 h at the current densities of 1 and 2 mA cm-2,respectively in symmetric ***,the Li5Mg@Cu|LFP cell exhibits a high-capacity retention of 91.8％for 1000 cycles and 78.8％for 2000 cycles at 1 ***,we investigate the suppression effects of Mg on the dendrite growth by studying the performance of LixMg@Cu electrodes with different Mg contents(2.0-16.7 at％).The exchange current density,surface energy,Li+diffusion coefficient,and chem-ical stability of LixMg@Cu concretely reveal this improving suppression effect when Mg content becomes *** addition,a Mg-rich phase with"hollow brick"morphology forming in the high Mg content LixMg@Cu guides the uniform deposition of *** study reveals the suppression effects of Mg on Li dendrites growth and offers a perspective for finding the optimal component of Li-Mg alloys.

关键词： Lithium dendrite Lithium metal anode Lithium-magnesium alloy Cycle performance Suppression effect Stability

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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HFANet: Hierarchical Feature-Enhanced Aggregation Network for Camouflaged Object Detection

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Zhang, Xinchao Zhu, Hengliang Mao, Guojun Department of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

Camouflaged object detection (COD) aims to identify target objects in complex scenes with extremely high similarity to their surroundings, and has significant applications in military, medical, and other fields. This paper proposes a hierarchical feature-enhanced aggregation network (HFANet) for COD, aiming to address the situations that the target object is highly similar to the background. First, we adopt the pyramid vision Transformer model as the backbone for feature extraction. On top of it, the object-region amplification module and deep interaction guidance module are stacked to enhance the perception of camouflaged objects in complex scenes. Second, an enhanced receptive field module is designed to improve edge perception of camouflaged objects. At last, a multi-scale interactive fusion module is designed by cross-scale connection through adjacent layers, effectively improving the accuracy of COD. The proposed method is evaluated on three challenging datasets: CAMO, CHAMELEON, and COD10K. Evaluation results demonstrate superior performance compared to the state-of-the-art methods. © Shanghai Jiao Tong University 2024.

关键词： Object detection

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Knowledge Probabilization in Ensemble Distillation: Improving Accuracy and Uncertainty Quantification for Object Detectors

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 221-233页

作者： Yang, Yang Wang, Chao Gong, Lei Wu, Min Chen, Zhenghua Li, Xiang Chen, Xianglan Zhou, Xuehai University of Science and Technology of China School of Computer Science and Technology Hefei230026 China 138632 Singapore East China Normal University School of Data Science and Engineering Shanghai200062 China

Ensemble object detectors have demonstrated remarkable effectiveness in enhancing prediction accuracy and uncertainty quantification. However, their widespread adoption is hindered by significant computational and storage demands, limiting their feasibility in resource-constrained settings. To overcome this, researchers have focused on distilling the knowledge from ensemble object detectors into a single model. In this article, we introduce probabilization based ensemble distillation (ProbED), an innovative ensemble distillation framework that consolidates knowledge from multiple object detectors into a single, resource-efficient model. Unlike traditional ensemble distillation methods that average the outputs of subteachers, ProbED captures comprehensive outcome distributions from all subteachers, providing a more nuanced approach to knowledge transfer. ProbED employs knowledge probabilization to achieve a sophisticated and refined aggregation of teacher knowledge, including feature knowledge, semantic knowledge, and localization knowledge, resulting in dual improvements in prediction accuracy and uncertainty quantification for the student model. In particular, ProED's novel knowledge probabilization-based approach to aggregating teacher knowledge is inspired by our empirical observations, which demonstrate that knowledge probabilization excels in effectively representing uncertainty, improving prediction, and facilitating robust knowledge transfer. Furthermore, we introduce a random smoothing perturbation technique to modify inputs within ProbED, further enhancing the distillation process. Extensive experiments highlight ProbED's ability to significantly enhance the prediction accuracy and uncertainty quantification of various object detectors, demonstrating its superior performance compared to other state-of-the-art techniques. © 2024 IEEE.

关键词： Teaching

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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A momental bound algorithm based on precise stepsize adjustment for training deep neural networks 43

A momental bound algorithm based on precise stepsize adjustm...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Haixia Li, Dequan Qian, Zhen Anhui University of Science and Technology School of Mathematics and Big Data Huainan232000 China Anhui University of Science and Technology School of Artificial Intelligence Huainan232000 China

ISBN: (纸本)9789887581581

Adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence properties. Adam is the most common adaptive algorithm among them. However, it has revealed that Adam has a poor generalization ability. AdaBelief is an algorithm based on Adam with exact stepsize adjustment. It introduces a new second-order momentum that corrects Adam's second-order moments. These algorithms still fail to converge during training due to the presence of instability and extreme learning rates. In this paper, we propose a new adaptive and momental bounded algorithm, called AdaBMod, which can effectively mitigate the sudden large learning rate problem and is especially suitable for training deep neural networks. By setting an adaptive finite learning rate in AdaBelief algorithm, the obtained AdaBMod can effectively eliminate the problem of high learning rate in the late training of neural networks, so as to make the training process more stable. Numerical simulation experiments on deep neural network tasks also show that our proposed AdaBMod algorithm eliminates the large learning rate during the training process. The results are also better than other current state-of-the-art optimizers. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adaptive Algorithms Adaptive and Momental Bound Deep Neural Networks Image Classification Robustness

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis techniques: image processing Sun: fundamental parameters

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Research on Heterogeneous Redundancy Emergency Decision Strategy based on Hierarchical State Machine 7

Research on Heterogeneous Redundancy Emergency Decision Stra...

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7th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Xu, Xiaolin Wang, Shenbo School of Big Data and Arificial Interlligence Zhengzhou University of Science and Technology Zhengzhou450064 China

ISBN: (纸本)9798350334197

In order to meet the functional safety requirements of unmanned driving technology at critical moments under decision failure, a heterogeneous redundant emergency decision-making strategy based on hierarchical state machine is proposed. According to the centralized architecture of the whole vehicle domain, the overall redundancy hierarchical state machine model is constructed from the data end, the decision-making end to the execution end, the two-heartbeat communication detection mechanism based on the time wheel is used as the boundary condition for the main redundant decision state transfer, and the AEBS control model of redundant decision is established using RSS theory, and finally the overall hierarchical decision state machine architecture is processed recursively. Taking a pure electric bus as the test object, from the main decision start operation to the redundant decision intervention after failure, the hierarchical decomposition verification is carried out according to different trigger conditions. The results show that the overall time efficiency of redundant decision-making from trigger initiation to policy control execution is only 0.79% compared with the main decision, and the double-heartbeat timeout threshold can effectively trigger the precise intervention of redundant decision-making at 160ms, so that the decision security is further improved and computing resources are rationally utilized. At the same time, the hierarchical state machine theory can realize the circulation of various working modes of the vehicle after the failure of the main decision, so that the sub-states can be reasonably transferred in the redundancy decision, so as to meet the design feasibility and effectiveness requirements of the redundancy function. © 2023 IEEE.

关键词： Redundancy

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A topology based on nuclei and upsets in residuated lattices

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Soft Computing 2024年第20期28卷 11743-11756页

作者： Wu, Supeng Liu, Hui Yang, Jiang School of Mathematics and Data Science Shaanxi University of Science and Technology Xi’an710021 China School of Mathematics Northwest University Xi’an710127 China

Firstly, a topology is constructed by nuclei and upsets in residuated lattices and it is shown that a residuated lattice endowed with this topology becomes a topological space. Furthermore, some properties of the topological space are investigated such as compactness and connectedness. Moreover, we study continuities of all the operations with respect to the topology in a residuated lattice. Finally, the relationships are revealed between the two topologies on quotient residuated lattices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Topology

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KubeSPT: Stateful Pod Teleportation for Service Resilience with Live Migration

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IEEE Transactions on Services Computing 2025年第3期18卷 1500-1514页

作者： Zhang, Hansheng Wu, Song Fan, Hao Huang, Zhuo Xue, Weibin Yu, Chen Ibrahim, Shadi Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong China

Container orchestration systems, such as Kubernetes, streamline containerized application deployment. As more and more applications are being deployed in Kubernetes, there is an increasing need for rescheduling - relocating a running pod to different nodes - due to system upgrades, node failures, and load-balancing optimizations. Live migration, which transfers services from source nodes to target nodes with minimal downtime, is the ideal support for rescheduling. However, implementing live migration for pods that run stateful services is challenging, because Kubernetes manages pods as stateless. First, the current pod's network namespace initialization process causes a mismatch in the network state between the migrated pod and internal containers. Second, migrating the memory state results in extended downtime. Third, Kubernetes operations on pods do not consider preserving the state of the pods. Therefore, we propose KubeSPT to achieve live migration of stateful pods in rescheduling scenarios. Firstly, we synchronize the network state of pods and internal containers by controlling packet flow and implement fast service redirection. Secondly, we introduce a Hot data and Lazy-Restore method for memory restoration to reduce migration downtime. Finally, we decouple pod migration operations from other Kubernetes operations to ensure compatibility with live migration. Experimental results show that KubeSPT reduces downtime by 86%-93% compared to current rescheduling methods. © 2008-2012 IEEE.

关键词： Restoration

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