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Effects of heat treatment on microstructures and properties of a heterostructured alloy with dissimilar components fabricated by WAAM

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Rare Metals 2024年第9期43卷 4441-4461页

作者： Ming-Cai Pan Jun-Qiang Xu Yong Peng Ning-Ning Liang Si Lan Qi Zhou Ke-Hong Wang School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of Controlled Arc Intelligent Additive Manufacturing Technology Nanjing 210094China

Heat treatment significantly influences homogeneous material microstructures and mechanical properties,which can be improved by an optimal heat treatment ***,heat treatment application to heterogeneous materials presents significant challenges due to compositional and microstructural ***,a laminated heterostructured alloy comprising alternating316L stainless steel(SS)and 18Ni300 maraging steel(MS)layers fabricated using wire and arc additive manufacturing was investigated.A solution treatment was applied at900℃for 0.5 ***,the solution-treated and asfabricated(AF)samples were aged at 500℃for 4 h;these samples were denoted SA and AT,*** AT phase compositions and orientations were similar to those of *** SA 316L SS layer resembled that of AF,but the SA 18Ni300 MS layers exhibited a reduced austenite phase fraction and refined grain size,attributable to solid-state *** the AT sample,aging induced the formation of nanoscale acicularωphase and ellipsoidal Ni_(3)Ti,Fe_(2)Mo,and X precipitates in the 18Ni300 MS ***,the SA precipitates contained acicular Fe Ni_(2)and ellipsoidalω,Ni_(3)Ti,and X precipitates,and their fractions were lower than those in AT ***18Ni300 MS layer microhardness in the heat-treated samples increased due to nanoprecipitation,but the 316L SS layer microhardness resembled that of *** AT and SA ultimate tensile strengths increased to(1360±50)and(1473±41)MPa,respectively,attributable to precipitation *** SA 316L SS layer exhibited a high stress-induced martensite fraction,enhancing the ductility of heated samples.

关键词： Laminated heterostructured alloy Heat treatment Wire and arc additive manufacturing Microstructure Mechanical property

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Research on simulation of gun muzzle flow field empowered by artificial intelligence

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Defence technology（防务技术） 2024年第2期32卷 196-208页

作者： Mengdi Zhou Linfang Qian Congyong Cao Guangsong Chen Jin Kong Ming-hao Tong School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing210094China Northwest Institute of Mechanical and Electrical Engineering Xianyang712099ShaanxiChina School of Automation Nanjing University of Science and TechnologyNanjing210094China

Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is ***,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal *** physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation *** two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and *** proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is *** is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous *** with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation *** proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.

关键词： Muzzle flow field Artificial intelligence Deep learning Data-physical fusion driven Shock wave

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Stress relief and crystal face transition process contribute to the stability of zinc anodes

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Journal of Energy Chemistry 2024年第12期99卷 593-603页

作者： Yuqian Li Chunhui Peng Xiaotong Gao Huanrong Liu Wenju Wang School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094JiangsuChina

Zn electrodes are suffering the dendrite growth owing to the enrichment of local space charge, distinct exposed face and residual stress. In this work, we investigated the crystal face properties and stress state of Zn foil through static energy calculations, dynamic crystal growth analysis and finite element simulation of stress states. Then thermal driven is deployed to modify the exposure face and residual stress of Zn foil, aiming for a dendrite-free electrode. The calculation of surface energies and simulation of crystal growth models for different crystal faces indicate that the(0 0 1) face can maintain stability during deposition. Inspired by this mechanism, the(1 0 1) exposed commercial Zn foil is modified by thermal processing. Firstly, the exposure level of the(0 0 1) face increases, though only the peak corresponding to the(0 0 2) crystal face is observed, due to the extinction effect of the densely packed plane(0 0 1) ***, the surface morphology becomes smooth and the stress is released with the progresses *** stress relief and crystal face transition process strengthen the uniformity of ion distribution, and increase the interface stability during the crystal growth, which reduce the defect sites in the *** a result, the Zn electrode exhibits tiny voltage hysteresis and outstanding cycle stability, which reveals improved electrochemical performance. Additionally, Li and Na can also be improved in exposed crystal faces and release strain energy through similar methods to enhance cycling stability.

关键词： Zn metal anode Dendrite free Exposure of crystal-face Stress relief Stable deposition

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Creep-induced redistribution of alloying elements in CZ1 zirconium alloys

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Journal of Materials science & technology 2024年第6期173卷 31-44页

作者： W.T.Lin Q.Y.Lv D.Jiao L.B.Zhang J.Tan G.Sha J.Hu China Nuclear Power Technology Research Institute Shenzhen 518026China School of Material Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Introducing minor alloying elements is an effective strategy to improve the corrosion and mechanical properties of zirconium alloys for nuclear *** in-reactor service,external environment and stress can affect the distribution of alloying elements,substantially changing the degradation process of zirconium *** date,there is a lack of in-depth understanding of the interaction between creep and microchemistry ***,we conducted systematic transmission electron microscopy(TEM)and atom probe tomography(APT)investigations to address creep-induced redistribution of alloying elements in CZ1(Zr-Sn-Nb-Fe-Cr-Cu)zirconium alloy with different initial ***,Fe,Sn,and Cu are found to co-segregate at grain *** higher the intermediate annealing temperature,the larger the Gibbsian interfacial excesses of solute elements *** further demonstrate that creep can reduce the excess value of Fe at grain boundaries due to the coarsening of Zr-Fe-Cr second phase particles via grain boundary and dislocation pipe *** the same time,the excess value of Sn is increased by diffusing from the matrix to grain ***,Cu as a minor element in the concentration range of 0.05-0.3 wt.%is found to segregate at dislocations to form the Cottrell atmosphere and develop Cu-rich nanoclusters for suppressing dislocation *** new understanding of the segregation and clustering of minor alloying elements provides guidance for developing zirconium alloys with enhanced creep resistance.

关键词： Zirconium alloy Thermal creep Grain boundary segregation Dislocation segregation Cu-rich nanoclusters

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High-speed encrypted traffic classification by using payload features

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Digital Communications and Networks 2025年第2期11卷 412-423页

作者： Xinge Yan Liukun He Yifan Xu Jiuxin Cao Liangmin Wang Guyang Xie School of Cyber Science and Engineering Southeast UniversityNanjing 211189China Nanjing Huafei Data Technology Co. Ltd.Nanjing 210019China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China

Traffic encryption techniques facilitate cyberattackers to hide their presence and *** classification is an important method to prevent network ***,due to the tremendous traffic volume and limitations of computing,most existing traffic classification techniques are inapplicable to the high-speed network *** this paper,we propose a High-speed Encrypted Traffic Classification(HETC)method containing two ***,to efficiently detect whether traffic is encrypted,HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted ***,HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest *** experimental results show that HETC can achieve a 94%F-measure in detecting encrypted flows and a 85%–93%F-measure in classifying fine-grained flows for a 1-KB flow-length dataset,outperforming the state-of-the-art comparison ***,HETC does not need to wait for the end of the flow and can extract mass computing *** average time for HETC to process each flow is only 2 or 16 ms,which is lower than the flow duration in most cases,making it a good candidate for high-speed traffic classification.

关键词： Traffic classification Flow analysis Information entropy Machine learning Randomness test

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VG-DOCoT:a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition

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Frontiers of Information technology & Electronic Engineering 2024年第11期25卷 1497-1514页

作者： Yanping ZHU Lei HUANG Jixin CHEN Shenyun WANG Fayu WAN Jianan CHEN School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China

Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical *** solve this problem,a novel electroencephalogram(EEG)emotion recognition network named VG-DOCoT is proposed,which is based on depthwise over-parameterized convolutional(DO-Conv),transformer,and variational automatic encoder-generative adversarial network(VAE-GAN)***,the differential entropy(DE)can be extracted from EEG signals to create mappings into the temporal,spatial,and frequency information in *** enhance the training data,VAE-GAN is employed for data augmentation.A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network.A transformer structure is introduced into the network framework to reveal the global dependencies from EEG *** the proposed model,a binary classification on the DEAP dataset is carried out,which achieves an accuracy of 92.52%for arousal and 92.27%for ***,a ternary classification is conducted on SEED,which classifies neutral,positive,and negative emotions;an impressive average prediction accuracy of 93.77%is *** proposed method significantly improves the accuracy for EEG-based emotion recognition.

关键词： Emotion recognition Electroencephalogram(EEG) Depthwise over-parameterized convolutional(DO-Conv) Transformer Variational automatic encoder-generative adversarial network(VAE-GAN)

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Dual-AutoEncoder & Bipartite Graph Embedding for article recommendation

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Soft Computing 2024年第13-14期28卷 7791-7806页

作者： Xi, Liang He, Dong Meng, Xianglong Hu, Qiaodan School of Computer Science and Technology Harbin University of Science and Technology Harbin China

Existing methods in article recommendation fail to fully use the article information, or pay less attention to the correlations among articles and "User-Article"s, resulting in inaccurate recommendation performance and weak personalized recommendation ability. Therefore, we design an article recommendation model based on Dual-AutoEncoder & Bipartite Graph (DAEBG): we build a Dual-AutoEncoder, consisting of an Attention-based AutoEncoder and a Graph AutoEncoder, for article basic features of "Title and Abstract" and the correlation features among articles by the "Tag and Citation" information, which are the input of DAEBG. Second, we design a correlation extraction network with Resource Allocation-based Bipartite Graph to extract the correlation feature embeddings of "User-Article"s. Finally, we employ the Probabilistic Matrix Factorization to fuse and update these feature embeddings, consider all kinds of features comprehensively, and avoid the sparse matrix and cold start problems. The output of DAEBG is a rating matrix, which is used for article recommendation. Experimental results show that DAEBG outperforms the other recommended methods in all experiments with different data characteristics (sparse or dense), which could help people choose the targets that better match their interests from a huge amount of articles. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Matrix algebra

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Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation

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Machine Intelligence Research 2025年第2期22卷 304-323页

作者： Yuanxun Lu Xinya Ji Hao Zhu Xun Cao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China School of Intelligence Science and Technology Nanjing UniversitySuzhou 215163China

Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit *** works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the *** recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes *** this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape ***,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query *** addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher *** formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high *** qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.

关键词： Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling

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Periodic solution of parabolic equations and stochastic process

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Applied Mathematics(A Journal of Chinese Universities) 2025年第1期40卷 78-84页

作者： WANG Xiao-huan School of Mathematics and Statistics Nanjing University Information Science and TechnologyNanjing 210044China

In this short paper, we first establish the existence of periodic solutions to parabolic equation in the whole space by using the probability method. Then, the periodicity of some function of stochastic process is als... 详细信息

关键词： periodic solutions Ito's formula stochastic process

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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

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