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GCRA-FWVAE:Anomaly detection for IIoT univariate time series using time-frequency domain analysis 认领 引用 被引量：1
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作者 Xiaoling Tao Haowei Liu +3 位作者 Wenbo Zhao Weikun Li Yaqi Nie Jingqi Fu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期405-416,共12页
With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT ... With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT data are typically scarce in abnormal samples and noisy,making unsupervised learning a common solution.The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods.While Variational Autoencoders(VAEs)excel in noise resilience,they face two critical challenges in IIoT data:difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features.To address these challenges,we propose the Greater Cane Rat Algorithm-enhanced FourierWavelet Conditional Variational Autoencoder(GCRA-FWVAE).Our method introduces a time-frequency dualbranch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization.These complementary representations jointly regulate the Conditional Variational Autoencoder(CVAE)reconstruction process,effectively preserving critical anomaly signatures while suppressing noise interference.The architecture is further optimized through bioinspired Greater Cane Rat Algorithm(GCRA)to improve adaptive learning capabilities.Extensive validation on the Yahoo benchmark indicates state-of-the-art performance,achieving an F1-score of 93.6%(an improvement of 4.5% over baseline VAEs)and a precision of 95.1%.These improvements significantly increase anomaly detection accuracy and robustness,particularly in the AIoT environment,where it effectively handles more complex and dynamic industrial data. 展开更多
关键词 Anomaly detection Time series GCRA CVAE Time-frequency domain
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Domain Switching in Relaxed Tetragonal-Phase Ferroelectric Single Crystals Under Nanoindentation 认领 引用
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作者 Guian Man Changjun Qi +3 位作者 Yujuan Peng Jiawei Wang Yixuan Jiang Xingzhe Wang 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第3期291-297,共7页
Relaxor ferroelectric single-crystal materials have attracted extensive attention because of their extremely high piezoelectric and electromechanical coupling properties,but research on their mechanically induced doma... Relaxor ferroelectric single-crystal materials have attracted extensive attention because of their extremely high piezoelectric and electromechanical coupling properties,but research on their mechanically induced domain switching properties under different strain rates is still lacking.In this paper,the domain switching dynamics exhibited by the PMN-0.36PT relaxor ferroelectric single crystal(with a tetragonal phase structure)under nanoindentation with variable strain rates are investigated via transmission electron microscopy in combination with the phase-field method.The microstructural material changes observed through transmission electron microscopy show that the T phase of the relaxor ferroelectric material undergoes 90°domain switching under nanoindentation.The results of a phase-field simulation involving nanoindentation with different strain rates further show that the T phase of the relaxor ferroelectric material undergoes 90°domain switching,the domain wall moves,and the original domain becomes wider directly under the indenter.Moreover,there is no correlation between the domain switching and loading rates.The phase-field simulation results are consistent with the experimental results,providing new insights into the mechanical force regulation properties of domain switching in relaxor ferroelectric materials. 展开更多
关键词 Domain switching Transmission electron microscopy Nanoindentation Phase-field method Relaxor ferroelectrics
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Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning 认领 引用
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作者 Dingran Song Feng Dai +2 位作者 Yi Liu Hao Tan Mingdong Wei 《Engineering》 SCIE EI CSCD 2026年第1期267-283,共17页
Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe ... Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe noise interference,and data scarcity.To address these issues,this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework(LRE-UDAF)for cross-domain MS signal classification.The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation(UDA)module utilizing a bi-classifier disparity metric and entropy regularization.The feature extractor derives high-level representations from the preprocessed signals,which are subsequently fed into two classifiers to predict class probability.Through three-stage adversarial learning,the feature extractor and classifiers progressively align the distributions of the source and target domains,facilitating knowledge transfer from the labeled source to the unlabeled target domain.Source-domain experiments reveal that the feature extractor achieves high effectiveness,with a classification accuracy of up to 97.7%.Moreover,LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness.Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals.Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules.This framework presents an efficient solution for resource-constrained,noise-prone,and data-scarce environments in deep underground engineering,offering significant promise for practical implementations in early disaster warning. 展开更多
关键词 Deep learning Microseismic classification Lightweight design Noise robustness Unsupervised domain adaptation
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A novel small perturbation analytical model to investigate temperature control characteristics of spacecraft thermal systems in frequency domain 认领 引用
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作者 Yuehang SUN Yunze LI +3 位作者 Yupeng ZHOU Ran WEI Hao DANG Xin ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期100-114,共15页
This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems unde... This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability. 展开更多
关键词 Frequency domain analysis Gravitational prospecting Perturbation techniques Power spectral density Temperature control
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Eco‑Friendly,Multi‑Mode Processable Highly Moldable Wood Enabled by the Reconstruction of Hydrogen‑Bonding Domain 认领 引用
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作者 Rui Yang Linghui Qi +7 位作者 Xiaoli Wu Zhipeng Liu Huiyang Bian Changlei Xia Changtong Mei Shuaicheng Jiang Meng Yao Jianzhang Li 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第8期378-394,共17页
The production of advanced 3D engineering materials relies on energy-intensive moldable materials such as metals and plastics,making it difficult to cope with the increasingly severe global energy crisis.Wood,as a sus... The production of advanced 3D engineering materials relies on energy-intensive moldable materials such as metals and plastics,making it difficult to cope with the increasingly severe global energy crisis.Wood,as a sustainable material,can be molded through hydrothermal treatment,but the limited plasticity hinders its ability to manufacture precision devices.Herein,the process of hydrogen-bond domain reorganization is used in the manufacture of highly moldable wood to enhance the plasticity of wood and ensure the stability of the cellulose structure.The native hydrogen-bond network in the wood cell wall is disrupted and liberated the cellulose fibril matrix through delignification.Subsequent epoxidized soybean oil acrylate(AESO)plasticization enables significantly enhanced plasticity.Hydrogen-bond domains between fibers are reconstructed through moisture variation.Meanwhile,AESO forms a protective layer on the surface of the fibers,preventing excessive moisture from entering and causing the collapse of the fiber framework.This process allows the material to be shaped into complex 3D geometries,including origami cranes or honeycombs,through low-energy hydrothermal processing.This strategy addresses both dimensional stability challenges and environmental instability associated with wood composite materials and offers an eco-friendly alternative to functionalized structures in aviation and transportation. 展开更多
关键词 Wood modification 3D structures Flexible and moldable wood Reconstruction of hydrogen-bonding domains
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Optical simulations of organic light-emitting diodes with non-uniform layers based on two-step domain decomposition method 认领 引用
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作者 Linya Chen Honggang Gu +5 位作者 Xiaoke Guo Ming Xu Ting Shi Jinchuan Li Weiran Cao Shiyuan Liu 《ENGINEERING Mechanical Engineering》 SCIE CAS CSCD 2026年第2期153-167,共15页
Non-uniform layers are a common and unavoidable phenomenon in the fabrication of pixel organic light-emitting diodes(OLEDs),particularly in inkjet printing(IJP),which often exhibits pronounced coffee-ring effects.Howe... Non-uniform layers are a common and unavoidable phenomenon in the fabrication of pixel organic light-emitting diodes(OLEDs),particularly in inkjet printing(IJP),which often exhibits pronounced coffee-ring effects.However,accurately simulating these non-uniform features in pixel OLEDs remains a significant challenge for existing methods.In this work,a two-step domain decomposition method was proposed to accurately and efficiently analyze pixel OLEDs with non-uniform layers.In the first step,the whole pixel was divided into several non-overlapping regions according to the dipole radiation range,and the classical dipole radiation model combined with the scattering-matrix method was applied.In the second step,each radiation region was subdivided into uniform and nonuniform parts(quasi-uniform parts),and a modified physical model was introduced to correct the reflection coefficient,transmission coefficient,and phase difference caused by non-uniform layers.The proposed method was verified through both numerical simulations and experiments on a typical IJP OLED.The results showed excellent agreement between the simulated and experimental data,with computational efficiency improved by a factor of 182 compared with COMSOL Multiphysics®.In addition,the analysis of the Purcell effect of a single dipole in a truncated Gaussian microcavity revealed the influence of non-uniformity on the microcavity effect.It explains the physical mechanism of the optical effect caused by non-uniformity,providing a theoretical fundament for non-uniform OLED optimization and manufacturing.This method breaks through the limitations of the traditional uniform model and facilitates the optical simulation and analysis of large-area pixel OLEDs with non-uniform layers. 展开更多
关键词 organic light emitting diode two-step domain decomposition method non-uniform layer optical simulation
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Motion In-Betweening via Frequency-Domain Diffusion Model 认领 引用
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作者 Qiang Zhang Shuo Feng +2 位作者 Shanxiong Chen Teng Wan Ying Qi 《Computers, Materials & Continua》 SCIE EI 2026年第1期275-296,共22页
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame... Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction. 展开更多
关键词 Motion generation diffusion model frequency domain human motion synthesis self-attention network 3D motion interpolation
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Gearbox Fault Diagnosis under Varying Operating Conditions through Semi-Supervised Masked Contrastive Learning and Domain Adaptation 认领 引用
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作者 Zhixiang Huang Jun Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期448-470,共23页
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis... To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings. 展开更多
关键词 Gearbox variable working conditions fault diagnosis semi-supervised masked contrastive learning domain adaptation
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Coordinated DNA methyltransferase 3A and methyltransferase-like 7A activity reprograms the tumor microenvironment through discoidin domain receptor 1 signaling 认领 引用
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作者 Zhengyang Bai Dan Yang +3 位作者 Jiayi Li Yaobang Liu Bin Lian Jinping Li 《Cancer Biology & Medicine》 SCIE CAS CSCD 2026年第1期107-132,共26页
Objective:Breast cancer is the most common malignancy in women and is characterized by a high recurrence rate that severely impacts patient survival.Regulatory T cells(Tregs)in the tumor microenvironment(TME)promote i... Objective:Breast cancer is the most common malignancy in women and is characterized by a high recurrence rate that severely impacts patient survival.Regulatory T cells(Tregs)in the tumor microenvironment(TME)promote immune evasion and metastasis,increasing recurrence risk.This study determined how the epigenetic regulators,DNMT3A and METTL7A,modulate Treg infiltration via the DDR1/STAT3/CXCL5 axis and influence breast cancer recurrence and prognosis.Methods:RNA sequencing(RNA-seq)was used to identify differentially expressed genes(DEGs),followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),supported vector machine-recursive feature elimination(SVM-RFE)and ElasticNet identified DDR1 as a key gene.Validation included RT-qPCR,western blot,MSP,MeRIP-qPCR,and Co-IP to assess epigenetic regulation.Functional assays(CCK-8,Transwell,and Treg differentiation/chemotaxis)and xenograft models evaluated the role of DDR1 in tumor progression and recurrence.Results:DNMT3A upregulated DDR1 via DNA methylation,while METTL7A enhanced DDR1 mRNA stability via m6A modification.Co-regulation activated the DDR1/STAT3/CXCL5 axis,which boosted cancer cell proliferation,migration,and invasion.CXCL5 secretion increased Treg infiltration and accelerated tumor growth in vivo.DDR1 silencing reversed these effects,confirming that DDR1 has a pivotal role in breast cancer recurrence.Conclusion:DNMT3A and METTL7A were shown to cooperatively regulate DDR1 via DNA/m6A methylation,which drives Tregmediated immune suppression and recurrence.This study provided novel insights and therapeutic targets for breast cancer prognosis and treatment. 展开更多
关键词 Tumor microenvironment DNMT3A METTL7A DDR1/STAT3/CXCL5 axis Discoidin domain receptor 1
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Zigzag domain walls unravel the polarization switching puzzle in wurtzite ferroelectrics 认领 引用
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作者 Hang Zang Zhiming Shi +1 位作者 Xiaojuan Sun Dabing Li 《Journal of Semiconductors》 EI CAS CSCD 2026年第6期33-36,共4页
In the past decade,the discovery of robust ferroelectricity in scandium-doped aluminum nitride(Al1−xScxN)[1]has ignited a new wave of research in the semiconductor community.Unlike traditional perovskite ferroel... In the past decade,the discovery of robust ferroelectricity in scandium-doped aluminum nitride(Al1−xScxN)[1]has ignited a new wave of research in the semiconductor community.Unlike traditional perovskite ferroelectrics(such as PbZrTiO3 or PZT)[2],wurtzite-structured materials are fully compatible with modern CMOS fabrication processes[3]. 展开更多
关键词 cmos fabrication processes zigzag domain walls scandium doped aluminum nitride wurtzite ferroelectrics semiconductor community robust ferroelectricity perovskite ferroelectrics such polarization switching
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A spherical Eshelby inclusion with a Steigmann-Ogden interface in a finite domain 认领 引用
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作者 Xu WANG P.SCHIAVONE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第7期1569-1580,共12页
We derive closed-form solutions to the three-dimensional Eshelby's problem of a spherical Eshelby inclusion undergoing uniform deviatoric eigenstrains concentrically embedded in an isotropic elastic finite spheric... We derive closed-form solutions to the three-dimensional Eshelby's problem of a spherical Eshelby inclusion undergoing uniform deviatoric eigenstrains concentrically embedded in an isotropic elastic finite spherical domain with a traction-free or rigidly clamped boundary.The interface between the inclusion and its surrounding domain is assumed to be of Steigmann-Ogden type.Our solutions indicate that the stresses and strains within the spherical inclusion are generally nonuniform because of the effects of the finite spherical domain and the Steigmann-Ogden imperfect interface.The internal elastic field of stresses and strains is uniform within the spherical inclusion when a condition that relates the single interface parameter to the geometric parameter and Poisson's ratio of the finite domain is satisfied.When the spherical edge is rigidly clamped,a GurtinMurdoch interface is found to be sufficient to achieve this interior uniformity property.In contrast,when the spherical edge is traction-free,a Steigmann-Ogden interface with nonzero and positive bending stiffness parameters must be used to achieve the interior uniformity property. 展开更多
关键词 spherical inclusion spherical domain Steigmann-Ogden interface Gurtin-Murdoch interface Neumann problem Dirichlet problem uniformity property
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FENet:Underwater Image Enhancement via Frequency Domain Enhancement and Edge-Guided Refinement 认领 引用 被引量：1
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作者 Xinwei Zhu Jianxun Zhang Huan Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第2期1942-1966,共25页
Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater imag... Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets. 展开更多
关键词 Detail extraction frequency domain operation edge guidance image enhancement
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Manipulation of Topological Boundaries within a Magnetic Domain Wall 认领 引用
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作者 Zhuyang Nie Ji Chen +6 位作者 Cunlong Dong Bin He Caihua Wan Haifeng Du Xiufeng Han Yizhou Liu Guoqiang Yu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第5期277-292,共16页
Bloch points and transverse walls can serve as topological boundaries within a magnetic domain wall.Here,we investigate the stability and dynamics of these topological boundaries for potential spintronic applications.... Bloch points and transverse walls can serve as topological boundaries within a magnetic domain wall.Here,we investigate the stability and dynamics of these topological boundaries for potential spintronic applications.Using micromagnetic simulations,we reveal the coexistence regimes of Bloch points and transverse walls in thin films with perpendicular magnetic anisotropy.An external in-plane field enables reversible transitions between these states through boundary-mediated Bloch point nucleation and annihilation processes.Under spin-transfer torque,transverse walls exhibit transverse drift and deformation.In contrast,Bloch points move strictly along the domain wall without transverse deflection and feature a Walker breakdown threshold an order of magnitude higher than conventional domain walls.Our findings establish a device concept where binary states correspond to in-plane magnetization orientations separated by mobile topological boundaries,offering new opportunities for spintronic architectures. 展开更多
关键词 bloch points transverse walls magnetic domain wallherewe topological boundaries micromagnetic simulationswe magnetic domain walls thin films stability dynamics
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One-dimensional charged domain walls in fluorite ferroelectrics 认领 引用
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作者 Jiajia Chen Haoji Qian +3 位作者 Xiaoxi Li Yan Liu Chengji Jin Genquan Han 《Journal of Semiconductors》 EI CAS CSCD 2026年第5期23-27,共5页
Ferroelectric domain walls are conventionally regarded as two-dimensional(2D)interfacial objects that separate regions of different polarization within a crystal.This picture has guided decades of research into polari... Ferroelectric domain walls are conventionally regarded as two-dimensional(2D)interfacial objects that separate regions of different polarization within a crystal.This picture has guided decades of research into polarization switching,domain evolution,and ferroic functionality. 展开更多
关键词 domain walls ferroic functionality separate regions different polarization one dimensional polarization switching ferroelectric domain walls charged polarization switchingdomain
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One-dimensional domain walls:A new dimension for ferroelectric nanoelectronics 认领 引用
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作者 Zepeng Li Wenjing Yue Yang Li 《Journal of Semiconductors》 EI CAS CSCD 2026年第6期16-20,共5页
Topological structures in ferroelectric materials,such as vortices,skyrmions,and merons,have garnered significant attention due to their emergent physical properties that are distinct from the bulk parent phase[1−4].T... Topological structures in ferroelectric materials,such as vortices,skyrmions,and merons,have garnered significant attention due to their emergent physical properties that are distinct from the bulk parent phase[1−4].These nanoscale textures hold immense promise for next-generation nanoelectronics,particularly in the realm of high-density non-volatile memory and logic.Among these topological features,ferroelectric domain walls(DWs),which serve as the interfaces separating domains with divergent polarization orientations,have long been viewed as potential active elements for next-generation electronic devices[5,6]. 展开更多
关键词 skyrmions domain walls topological structures ferroelectric materials nanoelectronics vortices nanoscale textures domains divergent polarization
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Viscosity prediction of refining slag based on machine learning with domain knowledge 认领 引用
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作者 Jianhua Chen Yijie Feng +4 位作者 Yixin Zhang Jun Luan Xionggang Lu Zhigang Yu Kuochih Chou 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第2期555-566,共12页
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e... The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al2O3-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al2O3-SiO2,CaO-Al2O3-CaF2,CaO-Al2O3-SiO2-MgO,and CaO-Al2O3-SiO2-MgO-CaF2.By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties. 展开更多
关键词 refining slag viscosity prediction machine learning domain knowledge
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Vacancy oscillating mode in amorphous binary oxide film by terahertz time domain spectroscopy 认领 引用
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作者 Huan Liu Haiyun Huang +12 位作者 Heng Yu Zhi Gong Fei Yu Zheng Zhang Zhiyong Tan Juncheng Cao Haiyun Liu Kan-Hao Xue Xiangshui Miao Yan Liu Yue Hao Genquan Han Qihua Xiong 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2026年第6期70-81,共12页
Unveiling the vacancy oscillation mode in amorphous binary oxides films at nanoscale and its impact on ionic conductivity and conductivity spectra is vital to explore the tightly intertwined connection between reversi... Unveiling the vacancy oscillation mode in amorphous binary oxides films at nanoscale and its impact on ionic conductivity and conductivity spectra is vital to explore the tightly intertwined connection between reversible oxygen migration and stabilizing and controlling the ferroelectric behavior,to complement traditional ferroelectric doped-HfO2materials.Using terahertz time-domain spectroscopy(THz-TDS),infrared reflection spectra,and density functional theory(DFT)calculations,we investigate the optical absorption and reflection spectra of crystalline and amorphous ZrO2thin film by varying oxygen vacancy concentrations.Experimental results show that oxygen vacancy migration rather than intrinsic paraelectric nature in films significantly affect the conductivity and polarization behavior of ZrO2thin film.Notably,except for the phonon modes induce distinct absorption peaks around 11 THz,additional absorption peaks are observed in the 1-2 THz range,which are caused by localized states originated from the oxygen vacancies,supported by DFT calculations.Temperature-dependent ion migration behavior further confirms the role of vacancy oscillation modes in ionic conductivity.DFT calculations additionally reveal how oxygen vacancies alter infrared absorption and optical modes,leading to a redshift in existing absorption peaks or the introduction of new peaks.Our findings unambiguously clarify the oxygen voltammetry characteristics in amorphous ferroelectric binary oxides films.Furthermore,the strategic deployment of amorphous binary oxides films enables the use of lowtemperature atomic layer deposition(ALD)growing process,effectively alleviating routing congestion of ferroelectric oxides and offering additional design flexibility for future memory devices with ultra-low effective oxide thickness(EOT)and low thermal budget,and providing alternative technological routes that are poised to propel the development of next-generation more compact ferroelectric devices for advanced process nodes. 展开更多
关键词 amorphous binary oxide ferroelectric material terahertz time domain spectroscopy memory
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Engineering nonreciprocal damping and polarization of spin waves in ferromagnetic domain wall via Dzyaloshinskii-Moriya interaction and spin-transfer torque 认领 引用
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作者 Xiang Liu Xiguang Wang +2 位作者 Zhixiong Li Xiufeng Han Guanghua Guo 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期29-35,共7页
Manipulation of spin-wave polarization is fundamental for designing novel magnonic devices based on the polarization coding technique.Here,we demonstrate the generation of left-handed polarized spin waves(LPSWs)in a f... Manipulation of spin-wave polarization is fundamental for designing novel magnonic devices based on the polarization coding technique.Here,we demonstrate the generation of left-handed polarized spin waves(LPSWs)in a ferromagnetic domain wall and their polarization modulation through the combined effect of the Dzyaloshinskii-Moriya interaction(DMI)and spin-polarized electric current.A phase diagram delineating the stability regions of left-and right-handed polarized spin waves(RPSWs)is constructed as a function of DMI strength and current density.Our results reveal a pronounced DMI-induced nonreciprocal damping effect,predominantly manifested in RPSWs while leaving LPSWs largely unaffected.This phenomenon enables effective filtering of RPSWs in one direction,allowing the realization of pure LPSW propagation as well as elliptically polarized spin waves with tunable eccentricity.Our work provides a viable method for controlling spin-wave polarization and nonreciprocal propagation in ferromagnetic systems. 展开更多
关键词 spin wave Dzyaloshinskii–Moriya interaction spin-polarized electric current ferromagnetic domain wall
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M~3Net:Meta-Reinforcement Learning-Based Open-Set Domain Generalization of Hyperspectral Image Classification Model 认领 引用
19
作者 Yuhu Cheng Wei Zhang +1 位作者 C.L.Philip Chen Xuesong Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期568-585,共18页
Hyperspectral image(HSI)classification models face dual challenges in open-set domain generalization:limited generalization ability due to unseen-domain shifts,and the need for unknown class recognition that breaks th... Hyperspectral image(HSI)classification models face dual challenges in open-set domain generalization:limited generalization ability due to unseen-domain shifts,and the need for unknown class recognition that breaks the closed-set assumption of traditional models.To address these challenges,we propose the Markov meta-Mamba network(M~3Net),which provides a metareinforcement learning-based solution for open-set domain generalization of HSI classification model.Specifically,a meta-task construction mechanism is proposed,treating source-domain background pixels as virtual unknown classes to simulate open-set HSI classification tasks during training,thereby providing task support for meta-reinforcement learning.Then,the open-set HSI classification task is reconstructed as a Markov decision process.By leveraging reinforcement learning's multi-step temporal credit assignment,non-causal factor sensitivity is suppressed,improving the model's cross-domain generalization performance.Finally,the theoretical linkage between Mamba and meta-learning is established,demonstrating that Mamba inherently operates as a metalearner when processing task sequences.Building on this,a Mamba-based meta-task embedding framework is designed,where shared meta-parameters and task-specific parameters are jointly optimized to achieve cross-task knowledge induction across open-set HSI classification tasks,thereby enhancing the model's generalization capability for unseen open-set tasks.Experiments on three cross-domain hyperspectral image datasets show that M~3Net has achieved the most competitive performance in the open-set domain generalization. 展开更多
关键词 Hyperspectral image classification Markov decision process meta-learning open-set domain generalization reinforcement learning
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EDTM:Efficient Domain Transition for Multi-Source Domain Adaptation 认领 引用
20
作者 Mangyu Lee Jaekyun Jeong +2 位作者 Yun Wook Choo Keejun Han Jungeun Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期955-970,共16页
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ... Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance. 展开更多
关键词 Multi-source domain adaptation imitation learning maximum classifier discrepancy ensemble based classifier EDTM
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