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arXiv 2025年

作者： Yang, Yang Zhang, Tong Wu, Jian Su, Lijie National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China China Criminal Police University Shenyang China Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA United States Beijing Institute for General Artificial Intelligence Beijing China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University ChinaShenyang Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic analysis framework that incorporates convex optimization to improve topic consistency, sparsity, and interpretability. In Stage 1, a two-layer non-negative matrix factorization (NMF) model is employed to extract annual topics and identify key terms. In Stage 2, a convex optimization algorithm refines the dynamic topic structure using the convex NMF (cNMF) model, further enhancing topic integration and stability. Applying the proposed method to IEEE journal abstracts from 2004 to 2022 effectively identifies and quantifies emerging research topics, such as COVID-19 and digital twins. By optimizing sparsity differences in the clustering feature space between traditional and emerging research topics, the framework provides deeper insights into topic evolution and ranking analysis. Moreover, the NMF-cNMF model demonstrates superior stability in topic consistency. At sparsity levels of 0.4, 0.6, and 0.9, the proposed approach improves topic ranking stability by 24.51%, 56.60%, and 36.93%, respectively. The source code is available at https://***/meetyangyang/CDNMF. © 2025, CC BY.

关键词： Non-negative matrix factorization

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Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

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Chinese Physics C 2024年第12期48卷 230-242页

作者： Yu-Xin Wang Shang-Jie Jin Tian-Yang Sun Jing-Fei Zhang Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)*** rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of *** this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)*** simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector *** found that the GW events in the first and second observation runs could all be clearly and rapidly *** the third observing run,approximately 80% of the GW events could be *** contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive *** conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

关键词： gravitational waves deep learning binary black holes time-frequency domain signals U-Net algorithm

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Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

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Chinese Physics C 2024年第4期48卷 240-251页

作者：孙天阳熊春雨金上捷王钰鑫张敬飞张鑫 Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW *** inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such *** this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency ***,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling ***,our approach exhibits a greater efficiency,boasting processing times on the order of *** conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

关键词： gravitational wave glitch non-Gaussian and transient noise normalizing flow machine learning likelihood-free parameter inference

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A search for sterile neutrinos in interacting dark energy models using DESI baryon acoustic oscillations and DES supernovae data

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Physics of the Dark Universe 2025年 48卷

作者： Feng, Lu Li, Tian-Nuo Du, Guo-Hong Zhang, Jing-Fei Zhang, Xin College of Physical Science and Technology Shenyang Normal University Shenyang 110034 China Key Laboratory of Cosmology and Astrophysics (Liaoning) & College of Sciences Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China

Sterile neutrinos can influence the evolution of the universe, and thus cosmological observations can be used to search for sterile neutrinos. In this study, we utilized the latest baryon acoustic oscillations data from DESI, combined with the cosmic microwave background data from Planck and the five-year supernova data from DES, to constrain the interacting dark energy (IDE) models involving both cases of massless and massive sterile neutrinos. We consider four typical forms of the interaction term Q=βHρ_de, Q=βHρ_c, Q=βH₀ρ_de, and Q=βH₀ρ_c, respectively. Our analysis indicates that the current data provide only a hint of the existence of massless sterile neutrinos (as dark radiation) at about the 1σ level. In contrast, no evidence supports the existence of massive sterile neutrinos. Furthermore, in IDE models, the inclusion of (massless/massive) sterile neutrinos has a negligible impact on the constraint of the coupling parameter β. The IDE model of Q=βHρ_c with sterile neutrinos does not favor an interaction. However, the other three IDE models with sterile neutrinos support an interaction in which dark energy decays into dark matter. © 2025 Elsevier B.V.

关键词： Cosmological observations Cosmological parameters Interacting dark energy Sterile neutrinos

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Joint constraints on cosmological parameters using future multi-band gravitational wave standard siren observations

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Chinese Physics C 2023年第6期47卷 188-198页

作者：金上捷邢双双邵悦张敬飞张鑫 Key Laboratory of Cosmology and Astrophysics(Liaoning Province)&Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Gravitational waves(GWs)from compact binary coalescences can be used as standard sirens to explore the cosmic expansion *** the next decades,it is anticipated that we could obtain the multi-band GW standard siren data(from nanohertz to a few hundred hertz),which are expected to play an important role in cosmological parameter *** this work,we provide,for the first time to the best of our knowledge,joint constraints on cosmological parameters using the future multi-band GW standard siren *** simulate the multi-band GW standard sirens based on the SKA-era pulsar timing array(PTA),Taiji observatory,and Cosmic Explorer(CE)to perform cosmological *** theΛCDM model,we find that the joint PTA+Taiji+CE data could provide a tight constraint on the Hubble constant with a 0.5%***,PTA+Taiji+CE could break the cosmological parameter degeneracies generated by CMB,especially in the dynamical dark energy *** combining the PTA+Taiji+CE data with the CMB data,the constraint precisions of?_(m)and H_(0)are 1.0%and 0.3%,respectively,meeting the standard of precision *** joint CMB+PTA+Taiji+CE data giveσ(_(w))=0.028 in the wCDM model andσ(w_(0))=0.11 andσ(w_(a))=0.32 in the w_(0)w_(a)CDM model,which are comparable with or close to the latest constraint results by CMB+BAO+*** conclusion,the future multi-band GW observations are expected to be used for exploring the nature of dark energy and measuring the Hubble constant.

关键词： gravitational wave standard siren multi-band observations cosmological parameters dark energy

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Null test for cosmic curvature using Gaussian process

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Chinese Physics C 2023年第5期47卷 259-270页

作者：武鹏举祁景钊张鑫 Key Laboratory ofCosmology and Astrophysics(Liaoning Province)&Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

The cosmic curvature Ω_(K,0),which determines the spatial geometry of the universe,is an important parameter in modern *** deviation from Ω_(K,0)=0 would have a profound impact on the primordial inflation paradigm and fundamental *** this work,we adopt a cosmological model-independent method to test whether Ω_(K,0) deviates from *** use the Gaussian process to reconstruct the reduced Hubble parameter E(z)and the derivative of the distance D'(z)from observational data and then determine Ω_(K,0) with a null test *** cosmic chronometer(CC)Hubble data,baryon acoustic oscillation(BAO)Hubble data,and supernovae Pantheon sample are *** result is consistent with a spatially flat universe within the domain of reconstruction 01,it tends to favor a closed *** this sense,there is still a possibility for a closed *** also carry out the null test of the cosmic curvature at 0

关键词： cosmic curvature Gaussian process cosmological observations cosmological model-independent method null test

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Solid state recycling of used aluminum alloy beverage cans by thermomechanical consolidation

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Heat Treatment and Surface engineering 2022年第1期4卷 90-98页

作者： Wang, Ziyi Yu, Yuanping Jin, Yu Li, Zhuolun Sun, Song Xu, Haoran Wu, Chengxun Zhang, Deliang School of Material Science and Engineering Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang China

Solid-state recycling of used aluminum alloy beverage cans was accomplished by thermomechanical consolidation of the sheet fragments produced by shredding the cans. Different samples were obtained using two thermomechanical consolidation routes, one involving direct hot extrusion of a sheet fragment compact and the other involving upset hot-forging of a sheet fragment compact followed by hot extrusion of the forged ingot. The microstructures and mechanical properties of the samples were investigated. It was found that the samples contained less pores and exhibited better mechanical properties with a higher extrusion temperature and involving upset forging than with only hot extrusion, the highest tensile strength is 209.5 MPa and the largest elongation to fracture is 29.1%. The surface layers of the samples exhibited a clearly higher strength and tensile ductility than the interior. They also showed significantly different fracture behaviors, with the former being like fully ductile fracture and the latter involving delamination of the sheet fragments, suggesting that the bonding between the fragments is significantly stronger in the surface layer, possibly due to a higher amount of plastic deformation in it. Overall, the best quality solid state recycled material has tensile properties comparable with those of wrought aluminum alloy with the same composition. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： mechanical properties microstructure solid state recycling thermomechanical consolidation of sheet fragments Used aluminum beverage cans

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A real-time temperature prediction method based on CNN-LSTM with MODE in steelmaking process

A real-time temperature prediction method based on CNN-LSTM ...

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IISE Annual Conference and Expo 2024

作者： Wang, Dan Liu, Chang Tang, Lixin Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Ministry of Education Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

ISBN: (纸本)9781713877851

Prediction of molten steel quality is a key issue in the converter steelmaking process. Due to high temperature and physical-chemical reaction, it is difficult to predict the temperature of molten steel in real time through mechanism analysis. This is a challenging problem with black box characteristics. Here we propose a Convolutional neural networks (CNN) and long short-term memory (LSTM) networks model based on multi-objective differential evolution algorithm (MODE) for the real-time prediction problem in the steelmaking process. Specifically, the CNN is used to extract feature between multiple variables that affect the temperature of molten steel, and the LSTM is suitable for the time series model based on the irregular trends information. The multi-step output CNN-LSTM method is used to construct the temperature prediction model of molten steel, and a knee solution strategy is applied to MODE, which can balance the model accuracy and generalization ability. The experiments were conducted using real data collected from the converter steelmaking process. The results verify the effectiveness of the proposed model on multiple steel grades and outperform other artificial intelligence methods in multi-task prediction. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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WO Nanosheets/FeCoO Nanoparticles Heterostructures for Highly Sensitive and Selective Ammonia Sensors

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IEEE Sensors Journal 2021年第23期21卷 26515-26525页

作者： Yuan, Zhenyu Lei, Yanfeng Li, Xiaozhi Meng, Fanli Gao, Hongliang State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

The gas - sensitive properties of metal oxide nanocomposites mainly depend on their structure and composition. In this paper, a highly sensitive ammonia sensor based on FeCo2O4 nanoparticles modified WO3 nanosheet nanocomposite is prepared. The structure and composition of the sensor material are characterized by the systematic spectral and structural characterization. The results show that FeCo2O4 nanoparticles grow on the surface of WO3 nanosheet and the interfacial contact is close, which leads to the P-N heterojunction and the increase of the relative specific surface area. The gas sensitivity test results show that the WO3/FeCo2O4 gas sensor has the highest sensitivity to NH3 (33.5 at 100 ppm) at 300 °C, with the characteristics of satisfactory selectivity, short response/recovery time, high repeatability and long stability. Compared with the intrinsic properties of WO3 and FeCo2O4, the sensitivity of WO3/FeCo2O4 nanocomposites is improved by tens of times. These excellent properties are attributed to the P-N heterojunction between WO3 nanosheets and FeCo2O4 nanoparticles, as well as the good catalytic activity of FeCo2O4, which make it become a practical gas sensitive material for ammonia detection. © 2001-2012 IEEE.

关键词： Tungsten compounds

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NSMD-NAS: Retinal Image Segmentation with Neural Architecture Search and Non-Subsampled Multiscale Decomposition 13

NSMD-NAS: Retinal Image Segmentation with Neural Architectur...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Hanyu Tang, Lixin Song, Xiangman Xu, Te Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shen Yang110819 China Northeastern University Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shen Yang110819 China

ISBN: (纸本)9798350308365

Accurate segmentation of the microvascular component of the retinal fundus image is of great significance for the clinical diagnosis. Currently, deep learning-based methods are often employed for retinal fundus image segmentation. However, most deep learning models manually designed by experts are not suitable for task-specific applications. Therefore, we propose a novel retinal image segmentation method that integrates a neural architecture search framework with a non-subsampled multiscale decomposition (NSMD-NAS) technique. Specifically, the feature extraction layer includes the convolutional layer and the transformer layer. In addition, the non-subsampling filter bank is embedded into the convolutional and the transformer modules respectively to generate a new network module for exploring multiscale, multi-frequency, and multi-directional information. Unlike previous wavelet-based network modules, non-subsampling network modules have translation invariance. Subsequently, the components of the network are specified by the elaborated encoding strategy, and then the network with the highest adaptation is searched by a genetic evolutionary algorithm (GA) to finally obtain the best network structure. Experimental results demonstrate the effectiveness of each component in the proposed model on the public benchmark datasets. © 2024 IEEE.

关键词： Convolution

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