In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t...Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.展开更多
This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading d...This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.展开更多
Continuous Fiber-reinforced Metal Matrix Composites(CFMMCs),such as Si C fiberreinforced TC17 matrix composites(SiC_(f)/TC17),are renowned for their exceptional mechanical properties.However,their heterogeneous compos...Continuous Fiber-reinforced Metal Matrix Composites(CFMMCs),such as Si C fiberreinforced TC17 matrix composites(SiC_(f)/TC17),are renowned for their exceptional mechanical properties.However,their heterogeneous compositions present significant machining challenges,including fiber pullout,matrix cracking,and accelerated tool wear.Ultrasonic Vibration-Assisted Grinding(UVAG)has proven to be an effective technique for overcoming these challenges.The material removal mechanisms in UVAG,especially in composites with both ductile and brittle phases,remain poorly understood.To explore these issues,UVAG and Conventional Grinding(CG)experiments were conducted on SiC_(f)/TC17 along two grinding directions:fiber's transverse direction(FT)and fiber's longitudinal direction(FL).This paper aims to provide a new dynamic mechanical model and shed light on the complex removal mechanisms in CFMMCs,which are characterized by a near one-to-one alternation of ductile and brittle phases.The findings reveal that UVAG reduces fiber damage and surface roughness compared to CG,especially when grinding along FT.UVAG lowers normal(F_(n))and tangential grinding forces(F_(t))by 15.3%and 12.3%,respectively.This highlights UVAG's potential for improving the machinability of complex materials like CFMMCs.The proposed grinding force model closely matches the experimental results.This paper hopes to support the precision abrasive machining of CFMMCs,a kind of complex and highly anisotropic composite material,and promote their application in the fields such as aerospace.展开更多
Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,t...Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Three different degrees of heterogeneous fault models are simulated by using 2-D random dynamic cellular automata models for analyzing macroscopic behaviors of seismic activity evolution influenced by heterogeneity of...Three different degrees of heterogeneous fault models are simulated by using 2-D random dynamic cellular automata models for analyzing macroscopic behaviors of seismic activity evolution influenced by heterogeneity of fault structures. The results show that the heterogeneities of fault structures can influence evolution properties of the foreshock activity and rupture process, such as the mediate heterogeneous and less heterogeneous structures, which show relatively higher ASR rates and more significant seismic gaps before main shocks. Besides, stress drop distribution ranges of the foreshock events when approaching a main shock show more homogenous (narrower) than that of the foreshock events far from a main shock. So the heterogeneity of fault structures plays an important role in strong earthquake preparation processes.展开更多
Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development...Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.展开更多
In heterogeneous natural gas reservoirs, gas is generally present as small patchlike pockets embedded in the water-saturated host matrix. This type of heterogeneity, also called "patchy saturation", causes s...In heterogeneous natural gas reservoirs, gas is generally present as small patchlike pockets embedded in the water-saturated host matrix. This type of heterogeneity, also called "patchy saturation", causes significant seismic velocity dispersion and attenuation. To establish the relation between seismic response and type of fluids, we designed a rock physics model for carbonates. First, we performed CT scanning and analysis of the fluid distribution in the partially saturated rocks. Then, we predicted the quantitative relation between the wave response at different frequency ranges and the basic lithological properties and pore fluids. A rock physics template was constructed based on thin section analysis of pore structures and seismic inversion. This approach was applied to the limestone gas reservoirs of the right bank block of the Amu Darya River. Based on poststack wave impedance and prestack elastic parameter inversions, the seismic data were used to estimate rock porosity and gas saturation. The model results were in good agreement with the production regime of the wells.展开更多
In this paper we present a model with spatial heterogeneity based on cellular automata （CA）. In the model we consider the relevant heterogeneity of host （susceptible） mixing and the natural birth rate. We divide t...In this paper we present a model with spatial heterogeneity based on cellular automata （CA）. In the model we consider the relevant heterogeneity of host （susceptible） mixing and the natural birth rate. We divide the susceptible population into three groups according to the immunity of each individual based on the classical susceptible-infectedremoved （SIR） epidemic models, and consider the spread of an infectious disease transmitted by direct contact among humans and vectors that have not an incubation period to become infectious. We test the local stability and instability of the disease-free equilibrium by the spectrum radii of Jacobian. The simulation shows that the structure of the nearest neighbour size of the cell （or the degree of the scale-free networks） plays a very important role in the spread properties of infectious disease. The positive equilibrium of the infections versus the neighbour size follows the third power law if an endemic equilibrium point exists. Finally, we analyse the feature of the infection waves for the homogeneity and heterogeneous cases respectively.展开更多
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for...For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.展开更多
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ...Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.展开更多
The grain density,Nv,in the solid state after solidification of AZ91/SiC composite is a function of maximum undercooling,ΔT,of a liquid alloy.This type of function depends on the characteristics of heterogeneous nucl...The grain density,Nv,in the solid state after solidification of AZ91/SiC composite is a function of maximum undercooling,ΔT,of a liquid alloy.This type of function depends on the characteristics of heterogeneous nucleation sites and number of SiC present in the alloy.The aim of this paper was selection of parameters for the model describing the relationship between the grain density of primary phase and undercooling.This model in connection with model of crystallisation,which is based on chemical elements diffusion and grain interface kinetics,can be used to predict casting quality and its microstructure.Nucleation models have parameters,which exact values are usually not known and sometimes even their physical meaning is under discussion.Those parameters can be obtained after mathematical analysis of the experimental data.The composites with 0,1,2,3 and 4wt.% of SiC particles were prepared.The AZ91 alloy was a matrix of the composite reinforcement SiC particles.This composite was cast to prepare four different thickness plates.They were taken from the region near to the thermocouple,to analyze the undercooling for different composites and thickness plates and its influence on the grain size.The microstructure and thermal analysis gave set of values that connect mass fraction of SiC particles,and undercooling with grain size.These values were used to approximate nucleation model adjustment parameters.Obtained model can be very useful in modelling composites microstructure.展开更多
The heterogeneity of coal was studied by mechanical tests. Probability plots of experimental data show that the mechanical parameters of heterogeneous coal follow a Weibull distribution. Based on elasto-plastic mechan...The heterogeneity of coal was studied by mechanical tests. Probability plots of experimental data show that the mechanical parameters of heterogeneous coal follow a Weibull distribution. Based on elasto-plastic mechanics and gas dynamics, the model of coupled gas flow＇ and deformation process of heterogeneous coal was presented and the effects of heterogeneity of coal on gas flow and failure of coal wcrc investigated. Major findings include： The effect of the heterogeneity of coal on gas flow and mechanical thilure of coal can be considered by the model in this paper. Failure of coal has a great effect on gas flow.展开更多
Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban gr...Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.展开更多
Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this ana...Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.展开更多
With the increasing maturity of automatic driving technology,the homogeneous traffic flow will gradually evolve into the heterogeneous traffic flow,which consists of human-driving and autonomous vehicles.To better stu...With the increasing maturity of automatic driving technology,the homogeneous traffic flow will gradually evolve into the heterogeneous traffic flow,which consists of human-driving and autonomous vehicles.To better study the characteristics of the heterogeneous traffic system,this paper proposes a new car-following model for autonomous vehicles and heterogeneous traffic flow,which considers the self-stabilizing effect of vehicles.Through linear and nonlinear methods,this paper deduces and analyzes the stability of such a car-following model with the self-stabilizing effect.Finally,the model is verified by numerical simulation.Numerical results show that the self-stabilizing effect can make the heterogeneous traffic flow more stable,and that increasing the self-stabilizing coefficient or historical time length can strengthen the stability of heterogeneous traffic flow and alleviate traffic congestion effectively.In addition,the heterogeneous traffic flow can also be stabilized with a higher proportion of autonomous vehicles.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金supported by the Natural Science Foundation of Jiangsu province,China(BK20240937)the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention(2022491411,2021491811)the Basal Research Fund of Central Public Welfare Scientific Institution of Nanjing Hydraulic Research Institute(Y223006).
文摘Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.
基金Project supported the Natural Science Foundation of Zhejiang Province, China (Grant No. LQN25F030011)the Fundamental Research Project of Hangzhou Dianzi University (Grant No. KYS065624391)+1 种基金the National Natural Science Foundation of China (Grant No. 61573148)the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2019A050520001)。
文摘This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.
基金financially supported by the National Natural Science Foundation of China(Nos.92160301,52175415,and 52205475)the Science Center for Gas Turbine Project(No.P2023-B-Ⅳ-003-001)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK20210295)the Fundamental Research Funds for the Central Universities(Nos.NG2024015 and NS2023028)the State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(No.MCAS-S-0423G02)。
文摘Continuous Fiber-reinforced Metal Matrix Composites(CFMMCs),such as Si C fiberreinforced TC17 matrix composites(SiC_(f)/TC17),are renowned for their exceptional mechanical properties.However,their heterogeneous compositions present significant machining challenges,including fiber pullout,matrix cracking,and accelerated tool wear.Ultrasonic Vibration-Assisted Grinding(UVAG)has proven to be an effective technique for overcoming these challenges.The material removal mechanisms in UVAG,especially in composites with both ductile and brittle phases,remain poorly understood.To explore these issues,UVAG and Conventional Grinding(CG)experiments were conducted on SiC_(f)/TC17 along two grinding directions:fiber's transverse direction(FT)and fiber's longitudinal direction(FL).This paper aims to provide a new dynamic mechanical model and shed light on the complex removal mechanisms in CFMMCs,which are characterized by a near one-to-one alternation of ductile and brittle phases.The findings reveal that UVAG reduces fiber damage and surface roughness compared to CG,especially when grinding along FT.UVAG lowers normal(F_(n))and tangential grinding forces(F_(t))by 15.3%and 12.3%,respectively.This highlights UVAG's potential for improving the machinability of complex materials like CFMMCs.The proposed grinding force model closely matches the experimental results.This paper hopes to support the precision abrasive machining of CFMMCs,a kind of complex and highly anisotropic composite material,and promote their application in the fields such as aerospace.
基金Research Committee of University of Macao under Research Grant No.MYRG081(Y1-L2)-FST13-YKVthe Science and Technology Development Fund of the Macao SAR government under Grant No.012/2013/A1
文摘Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
文摘Three different degrees of heterogeneous fault models are simulated by using 2-D random dynamic cellular automata models for analyzing macroscopic behaviors of seismic activity evolution influenced by heterogeneity of fault structures. The results show that the heterogeneities of fault structures can influence evolution properties of the foreshock activity and rupture process, such as the mediate heterogeneous and less heterogeneous structures, which show relatively higher ASR rates and more significant seismic gaps before main shocks. Besides, stress drop distribution ranges of the foreshock events when approaching a main shock show more homogenous (narrower) than that of the foreshock events far from a main shock. So the heterogeneity of fault structures plays an important role in strong earthquake preparation processes.
基金National Key Research and Development Program of China(No.2023YFB3907103).
文摘Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.
基金sponsored by the NSFC(41104066)973 Program of China(No.2014CB239006)+1 种基金NSTMP of China(Nos.2011ZX05004-003 and 2011ZX05029-003)12th 5-Year Basic Research Program of CNPC(No.2011A-3601)
文摘In heterogeneous natural gas reservoirs, gas is generally present as small patchlike pockets embedded in the water-saturated host matrix. This type of heterogeneity, also called "patchy saturation", causes significant seismic velocity dispersion and attenuation. To establish the relation between seismic response and type of fluids, we designed a rock physics model for carbonates. First, we performed CT scanning and analysis of the fluid distribution in the partially saturated rocks. Then, we predicted the quantitative relation between the wave response at different frequency ranges and the basic lithological properties and pore fluids. A rock physics template was constructed based on thin section analysis of pore structures and seismic inversion. This approach was applied to the limestone gas reservoirs of the right bank block of the Amu Darya River. Based on poststack wave impedance and prestack elastic parameter inversions, the seismic data were used to estimate rock porosity and gas saturation. The model results were in good agreement with the production regime of the wells.
基金Project supported by the National Natural Science Foundation of China （Grant No 10471040）.
文摘In this paper we present a model with spatial heterogeneity based on cellular automata （CA）. In the model we consider the relevant heterogeneity of host （susceptible） mixing and the natural birth rate. We divide the susceptible population into three groups according to the immunity of each individual based on the classical susceptible-infectedremoved （SIR） epidemic models, and consider the spread of an infectious disease transmitted by direct contact among humans and vectors that have not an incubation period to become infectious. We test the local stability and instability of the disease-free equilibrium by the spectrum radii of Jacobian. The simulation shows that the structure of the nearest neighbour size of the cell （or the degree of the scale-free networks） plays a very important role in the spread properties of infectious disease. The positive equilibrium of the infections versus the neighbour size follows the third power law if an endemic equilibrium point exists. Finally, we analyse the feature of the infection waves for the homogeneity and heterogeneous cases respectively.
基金supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335the Shandong Provincial Natural Science Foundation under Grant JQ201808+5 种基金The Fundamental Research Funds for the Central Universities under Grant 18CX02097Athe Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006111 Project under Grant B08028Sinopec Science and Technology Project under Grant P20050-1
文摘For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金supported by the National Nature Science Foundation of China(Grant No.71401052)the National Social Science Foundation of China(Grant No.17BGL156)the Key Project of the National Social Science Foundation of China(Grant No.14AZD024)
文摘Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.
基金supported financially by the European Community under Marie Curie Transfer of Knowledge grant No. MTKD-CT-2006-042468 (AGH No.27.27.170.304)Polish Ministry of Science and Higher Education for financial support under grant No. N507-44-66-34 (AGH No.18.18.170.325)
文摘The grain density,Nv,in the solid state after solidification of AZ91/SiC composite is a function of maximum undercooling,ΔT,of a liquid alloy.This type of function depends on the characteristics of heterogeneous nucleation sites and number of SiC present in the alloy.The aim of this paper was selection of parameters for the model describing the relationship between the grain density of primary phase and undercooling.This model in connection with model of crystallisation,which is based on chemical elements diffusion and grain interface kinetics,can be used to predict casting quality and its microstructure.Nucleation models have parameters,which exact values are usually not known and sometimes even their physical meaning is under discussion.Those parameters can be obtained after mathematical analysis of the experimental data.The composites with 0,1,2,3 and 4wt.% of SiC particles were prepared.The AZ91 alloy was a matrix of the composite reinforcement SiC particles.This composite was cast to prepare four different thickness plates.They were taken from the region near to the thermocouple,to analyze the undercooling for different composites and thickness plates and its influence on the grain size.The microstructure and thermal analysis gave set of values that connect mass fraction of SiC particles,and undercooling with grain size.These values were used to approximate nucleation model adjustment parameters.Obtained model can be very useful in modelling composites microstructure.
基金Supported by the Key National Natural Science Foundation of China （50434020） the Natural Science Foundation of Hebei Province, China （E2010000872, Z2009315）
文摘The heterogeneity of coal was studied by mechanical tests. Probability plots of experimental data show that the mechanical parameters of heterogeneous coal follow a Weibull distribution. Based on elasto-plastic mechanics and gas dynamics, the model of coupled gas flow＇ and deformation process of heterogeneous coal was presented and the effects of heterogeneity of coal on gas flow and failure of coal wcrc investigated. Major findings include： The effect of the heterogeneity of coal on gas flow and mechanical thilure of coal can be considered by the model in this paper. Failure of coal has a great effect on gas flow.
文摘Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.
基金Supported by the National Natural Science Foundation of China(No.51379006 and No.51009106)the Program for New Century Excellent Talents in University of Ministry of Education of China(No.NCET-12-0404)the National Basic Research Program of China("973"Program,No.2013CB035903)
文摘Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.
基金supported by the National Natural Science Foundation of China(Grant No.61773243)the Major Technology Innovation Project of Shandong Province,China(Grant No.2019TSLH0203)the National Key Research and Development Program of China(Grant No.2020YFB1600501)。
文摘With the increasing maturity of automatic driving technology,the homogeneous traffic flow will gradually evolve into the heterogeneous traffic flow,which consists of human-driving and autonomous vehicles.To better study the characteristics of the heterogeneous traffic system,this paper proposes a new car-following model for autonomous vehicles and heterogeneous traffic flow,which considers the self-stabilizing effect of vehicles.Through linear and nonlinear methods,this paper deduces and analyzes the stability of such a car-following model with the self-stabilizing effect.Finally,the model is verified by numerical simulation.Numerical results show that the self-stabilizing effect can make the heterogeneous traffic flow more stable,and that increasing the self-stabilizing coefficient or historical time length can strengthen the stability of heterogeneous traffic flow and alleviate traffic congestion effectively.In addition,the heterogeneous traffic flow can also be stabilized with a higher proportion of autonomous vehicles.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
