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Journal of Magnesium and Alloys 2024年第1期12卷 251-266页

作者： Fei-er Shangguan Wei-li Cheng Yu-hang Chen Ze-qin Cui Hui Yu Hong-xia Wang Li-fei Wang Hang Li Hua Hou school of Materials science and Engineering Taiyuan University of TechnologyTaiyuan 030024China school of Materials science and Engineering Hebei University of TechnologyTianjin 300132China school of Materials science and Engineering North University of ChinaTaiyuan 030051China

The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are *** grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing *** that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing *** electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical *** In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca *** micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly *** In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.

关键词： Mg-air batteries Mg-Bi-Sn based alloys Electrochemical behaviors Discharge properties

A Dual Discriminator Method for Generalized Zero-Shot Learning

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Computers, Materials & Continua 2024年第4期79卷 1599-1612页

作者： Tianshu Wei Jinjie Huang school of Computer science and technology Harbin University of Science and TechnologyHarbin150006China school of Automation Harbin University of Science and TechnologyHarbin150006China

Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen *** the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

关键词： Generalized zero-shot learning modality consistent discriminator domain shift problem feature fusion

Composite Braking Control Strategy for Electromechanical-Hydraulic Vehicles

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1722-1732页

作者： Zewen Meng Jichao Hong Hongxin Zhang Tiezhu Zhang Xiaoming Xu Qinghai Zhao Jian Yang College of Mechanical and Electrical Engineering Qingdao UniversityQingdao 260071China Power Integration and Energy Storage Systems Engineering technology Center(Qingdao) Qingdao 260071China school of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China Shunde graduate school of university of science and technology Beijing Foshan 528000China

Short driving ranges and low braking energy recovery efficiencies are two recognized technical bottlenecks to be overcome in electric vehicles. In this paper, a novel electromechanical-hydraulic coupling system is proposed and integrated as a powertrain for electric vehicles, which can assist the electric vehicle to fully utilize its braking energy. The hydraulic regenerative braking force and electric regenerative braking force can provide all the braking needs using the medium and small braking intensities. Furthermore, an improved compound brake control strategy based on the braking force distribution is proposed and simulated. The results show that under the premise of ensuring braking stability, the electromechanical-hydraulic coupling driving electric vehicle can adapt to various working conditions with excellent energy-saving results. The hydraulic accumulator recovery efficiency is above 99%, and the state of charge consumption rate of the battery pack can be reduced by more than 9%. More importantly, the proposed hybrid power system can significantly improve the driving range and energy efficiency, as well as reduce the consumers' mileage anxiety in electric vehicles.

关键词： Braking control strategy electric vehicle electromechanical-hydraulic coupling energy recovery state of charge

A new centrality measure based on neighbor loop structure for network dismantling

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Digital Communications and Networks 2024年第2期10卷 472-480页

作者： Qingxia Liu Bang Wang Jiming Qi Xianjun Deng school of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan430074China school of Cyber science and Engineering Huazhong University of Science and TechnologyWuhan430074China

Nearly all real-world networks are complex networks and usually are in danger of ***,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network *** dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive *** overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation *** addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and ***,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation *** results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack ***,the insights gained from this study may be of assistance to future practical research into real-world networks.

关键词： Complex networks Network dismantling Centrality measure

Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua science and technology 2024年第2期29卷 469-480页

作者： Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang school of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China school of Computer science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

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Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng school of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China school of Computer science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

An in-situ polymerized interphase engineering for high-voltage allsolid- state lithium-metal batteries

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Nano Research 2024年第4期17卷 2687-2692页

作者： Lu Nie Shaojie Chen Mengtian Zhang Tianyi Gao Yuyao Zhang Ran Wei Yining Zhang Wei Liu school of Physical science and technology ShanghaiTech UniversityShanghai 201210China Tsinghua-Berkeley Shenzhen Institute&Tsinghua Shenzhen International graduate school Tsinghua UniversityShenzhen 518055China

All-solid-state lithium batteries(ASSLBs)have attracted great interest due to their promising energy density and strong ***,the interface issues,including large interfacial resistance between electrode and electrolyte and low electrochemical stability of solid-state electrolytes against high-voltage cathodes,have restricted the development of high-voltage ***,we report an ASSLB with stable cycling by adopting a conformal polymer interlayer in-situ formed at the Li_(6.4)La_(3)Zr_(1.4)Ta_(0.6)O_(12)(LLZTO)–cathode *** polymer can perfectlyﬁll the voids and create a stable interface contact between LLZTO and *** addition,the electric field across the polymer interlayer is reduced compared with pure solid polymer electrolyte(SPE),which facilitates the electrochemical stability with high-voltage *** all-solid-state Li|LLZTO-SPE|LiFe_(0.4)Mn_(0.6)PO_(4)(LMFP)cells achieve a low interface impedance,high specific capacity,and excellent cycling *** work presents an effective and practical strategy to rationally design the electrode–electrolyte interface for the application of high-voltage ASSLBs.

关键词： all-solid-state lithium batteries interface contact electric field electrochemical stability

Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations

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Chinese Physics B 2024年第7期33卷 126-135页

作者：邵晓光张捷鲁延娟 school of Automation Nanjing University of Science and TechnologyNanjing 210094China school of Electronics and Information Engineering Changchun University of Science and TechnologyChangchun 130022China

This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.

关键词： memristor-based neural networks proportional delays dynamic event-triggered mechanism sensor saturations

Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma school of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina school of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

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Chinese Physics B 2024年第3期33卷 677-689页

作者：霍良安武兵杰 Business school University of Shanghai for Science and TechnologyShanghai 200093China school of Intelligent Emergency Management University of Shanghai for Science and TechnologyShanghai 200093China

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex *** fact,the disease transmission process is very *** this information,there will often be individual behavioral measures and other factors to *** of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,*** carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease *** is achieved by considering four influential relationships between adjacent layers in multilayer *** the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are *** the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease *** the disease layer,individual susceptibility is considered to be influenced by vaccination *** state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are *** is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local d

关键词： information diffusion vaccination behavior disease transmission multilayer networks local and global effect