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Theoretical Exploration of Alloying Effects on Stabilities and Mechanical Properties ofγʹPhase in Novel Co–Al–Nb-Base Superalloys

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Acta Metallurgica Sinica(English Letters) 2024年第7期37卷 1238-1248页

作者： Guan-Cheng Gu Zhao-Jing Han Ze-Yu Chen Zhao-Xuan Li Sheng-Bao Xia Zheng-Ning Li Hua Jin Wei-Wei Xu Xing-Jun Liu shenzhen research institute of Xiamen University School of Aerospace EngineeringXiamen UniversityXiamen361005China institute of Materials Genome and big data Harbin Institute of TechnologyShenzhen518055China

The new developedγ/γʹCo–Al–Nb-base alloys show great potentials as high-temperature ***,finding appropriate compositions to improve performance of alloys still poses a great challenge to the development of Co–Al–Nb-base *** by the lack of alloying effects on fundamental properties of criticalγʹphase,we systematically performed a theoretical investigation on the effect of alloying elements TM(TM:Ti,V,Cr,Zr,Mo,Ta,W,Re,and Ru)on phase stabilities and mechanical properties of L1_(2)-typeγʹ(Co,Ni)_(3)(Al,Nb).By analyzing the stability ofγʹphase with respect to its competitive B2 and D0_(19) phases,the results shown that Ti,V,and Cr enhance the L1_(2) stability and widen the L1_(2)–D0_(19) energy barrier,in which V yields the maximum *** analysis of electronic structure indicated that the alternation of valence electrons at fermi level would be the atomic origin for doping TM inγʹ*** calculated results of mechanical properties shown that V and Cr are expected to be optimal dopant for enhancing the strength and the ductility ofγʹ*** addition of Ta is also beneficial for enhancing the strength at the slight expense of ductility ofγʹ*** drawing the mechanical maps,the preferred composition range for the phases with desired properties is roughly demarcated in theory for the multi-addition of V/Cr and V/Ta inγʹ*** findings would be useful for optimizing the performance of novelγ/γʹCo–Al–Nb-base superalloys.

关键词： Co-Al-Nb-base superalloys γ/γʹdual-phase Phase stability Mechanical properties First-principle methods

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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big data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan shenzhen institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering research Center for Health big data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of data Science and shenzhen research institute of big data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of big data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer *** data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth ***,data augmentation is crucial for this *** data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge *** this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth *** method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge *** validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation ***,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed *** results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data *** the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to *** the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina shenzhen institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of big data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Time-sensitive prediction of NO_(2) concentration in China using an ensemble machine learning model from multi-source data

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Journal of Environmental Sciences 2024年第3期137卷 30-40页

作者： Chenliang Tao Man Jia Guoqiang Wang Yuqiang Zhang Qingzhu Zhang Xianfeng Wang Qiao Wang Wenxing Wang big data research Center for Ecology and Environment Environment Research InstituteShandong UniversityQingdao 266237China Shandong Provincial Eco-environment Monitoring Center Jinan 250101China

Nitrogen dioxide(NO_(2))poses a critical potential risk to environmental quality and public health.A reliable machine learning(ML)forecasting framework will be useful to provide valuable information to support government *** on the data from1609 air quality monitors across China from 2014-2020,this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide *** ensemble ML model incorporates a residual connection to the gated recurrent unit(GRU)network and adopts the advantage of Transformer,extreme gradient boosting(XGBoost)and GRU with residual connection network,resulting in a 4.1%±1.0%lower root mean square error over XGBoost for the test *** ensemble model shows great prediction performance,with coefficient of determination of 0.91,0.86,and 0.77 for 1-hr,3-hr,and 24-hr averages for the test results,*** particular,this model has achieved excellent performance with low spatial uncertainty in Central,East,and North China,the major site-dense *** the interpretability analysis based on the Shapley value for different temporal resolutions,we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions,while the impact of meteorological conditions would be ever-prominent for the *** with existing models for different spatiotemporal scales,the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO_(2),which will help developing effective control policies.

关键词： Air quality prediction Deep learning Ensemble method Nitrogen dioxide Spatiotemporal covariates

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Graphene effectively activating "dead" water molecules between manganese dioxide layers in potassium-ion battery

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Journal of Energy Chemistry 2024年第6期93卷 306-315,I0008页

作者： Xinhai Wang Wensheng Yang Shengshang Lu Shangshu Peng Tong Guo Quan Xie Qingquan Xiao Yunjun Ruan institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025GuizhouChina

Aqueous potassium-ion batteries(APIBs),recognized as safe and reliable new energy devices,are considered as one of the alternatives to traditional *** MnO_(2),serving as the main cathode,exhibits a lower specific capacity in aqueous electrolytes compared to organic systems and operates through a different reaction *** application of highly conductive graphene may effectively enhance the capacity of APIBs but could complicate the potassium storage *** this study,a MnO_(2) cathode pre-intercalated with K~+ions and grown on graphene(KMO@rGO) was developed using the microwave hydrothermal method for ***@rGO achieved a specific capacity of 90 mA h g^(-1) at a current density of 0.1 A g^(-1),maintaining a capacity retention rate of>90% after 5000 cycles at 5 A g^(-1).In-situ and exsitu characterization techniques revealed the energy-storage mechanism of KMO@rGO:layered MnO_(2)traps a large amount of "dead" water molecules during K~+ions ***,the introduction of graphene enables these water molecules to escape during K~+ ions insertion at the *** galvanostatic intermittent titration technique and density functional theory confirmed that KMO@rGO has a higher K~+ions migration rate than MnO_(2).Therefore,the capacity of this cathode depends on the interaction between dead water and K~+ions during the energy-storage *** optimal structural alignment between layered MnO_(2) and graphene allows electrons to easily flow into the external *** charge compensation forces numerous low-solvent K~+ions to displace interlayer dead water,enhancing the *** unique reaction mechanism is unprecedented in other aqueous battery studies.

关键词： Graphene K-ion batteries Mn-based layered oxide Water molecules Density functional theory

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A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

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Journal of Safety Science and Resilience 2024年第2期5卷 167-178页

作者： Gangqiao Wang Han Xing Yongqiang Chen Yi Liu institute of big data Inteligent Management and Decision-making College of ManagementShenzhen UniversityShenzhen 518060China School of Safety Science Tsinghua UniversityBejing 100084China Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBejing 100871China

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep *** to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of *** this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep *** the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future ***-time data is then incorporated into diverse forecasts to dynamically adjust the simulation *** ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually *** approach has been applied to a human-involved car-following system simulation under complex traffic *** results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

关键词： Disaster modeling and analysis Deepuncertainty Hybrid modeling Adaptivesimulation

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A CELL-CENTERED GODUNOV METHOD BASED ON STAGGERED data DISTRIBUTION,PART Ⅰ:ONE-DIMENSIONAL CASE

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Journal of Computational Mathematics 2024年第4期42卷 1172-1196页

作者： Jiayin Zhai Xiao Li Zhijun Shen institute of Applied Physics and Computational Mathematics LCPP.O.Box 8009-26Beijing 100088China College of big data and Internet Shenzhen Technology UniversityShenzhen 518118China Center for Applied Physics and Technology HEDPSPeking UniversityChina

This paper presents a cell-centered Godunov method based on staggered data distribu-tion in Eulerian *** motivation is to reduce the intrinsic entropy dissipation of classical Godunov methods in the calculation of an isentropic or rarefaction *** the same time,the property of accurate shock capturing is also *** analyzing the factors that cause nonphysical entropy in the conventional Godunov methods,we introduce two velocities rather than a single velocity in a cell to reduce kinetic energy dissipation.A series of redistribution strategies are adopted to update subcell quantities in order to improve *** examples validate that the present method can dramatically reduce nonphysical entropy *** subject classification:35Q35,76N15,76M12.

关键词： Godunov method Riemann solver Rarefaction wave Nonphysical entropy increase.

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