Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic...Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.展开更多
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.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
This study aims to investigate the influence of rapid economic development on pollution at the municipal level in China.It constructs a Stochastic Impacts by Regression on Population,Affluence and Technology model(STI...This study aims to investigate the influence of rapid economic development on pollution at the municipal level in China.It constructs a Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT model) and uses comprehensive municipal data on industrial pollution and economic performance.The dataset contains 290 cities from2003 to 2016 as a sample for the panel data analysis.The study further separates the cities into two groups by their levels of economic development for heterogeneity analysis.It reveals that a low level of economic development would aggravate environmental pollution,and when the economy reaches a high level,this economic development will improve environmental quality.We also find that the relationships between foreign direct investment and industrial dust and sulfur dioxide(SO_2) discharge are significant,while the relationship between economic growth and effluent emission is not.The more developed subsample cities present an inverted U-shaped curve between industrial pollutant emission,GDP per capita,and foreign direct investment,while the less developed subsamples show no such relationship.Since the shape of these curves differs among regions,their turning points vary accordingly.Based on this finding,this study suggests that the governments of more developed cities should balance environmental pollution and economic development by enhancing environmental regulations and adjusting industrial structure.展开更多
Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full ...Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.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
A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in ...A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.展开更多
Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce t...Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model parameters.These methods allow for the sharing of only class representatives between heterogeneous clients while maintaining privacy.However,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization capabilities.To address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous environments.Furthermore,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive learning.Comprehensive experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.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
To construct mediators for data integration systems that integrate structured and semi-structured data, and to facilitate the reformulation and decomposition of the query, the presented system uses the XML processing ...To construct mediators for data integration systems that integrate structured and semi-structured data, and to facilitate the reformulation and decomposition of the query, the presented system uses the XML processing language (XPL) for the mediator. With XPL, it is easy to construct mediators for data integration based on XML, and it can accelerate the work in the mediator.展开更多
Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborati...Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborative modeling and training across multiple endpoints without exposing the original data.However,the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios.We propose a Federated Contrast Enhancement(FedCE)method for NER to address the challenges faced by non-large-scale pre-trained models in FL for labelheterogeneous.The method leverages a multi-view encoder structure to capture both global and local semantic information,and employs contrastive learning to enhance the interoperability of global knowledge and local context.We evaluate the performance of the FedCE method on three real-world clinical record datasets.We investigate the impact of factors,such as pooling methods,maximum input text length,and training rounds on FedCE.Additionally,we assess how well FedCE adapts to the base NER models and evaluate its generalization performance.The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models,which is of great theoretical and practical significance for advancing FL in healthcare settings.展开更多
In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay fo...In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow acc...A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.展开更多
The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with th...The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism.The results show that,there is a 'U' type nonlinear relationship between the ERI and GML.The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion.There is a 'U' type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML.The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI,while the CAC plays a significant guiding role in upgrading of the energy consumption structure.There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area,and the CAC is not significantly.Meanwhile,both of the ERI shows no positive effects in the central and western inland region.展开更多
In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with...In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with construction of data gathering trees,channel allocation,power assignment of nodes and link scheduling,is formulated as an optimization problem in this paper.Then,the optimization problem is proved to be NP-hard.To make the problem tractable,firstly,a multi-channel-based low-latency(MCLL)algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes.Secondly,a maximum links scheduling(MLS)algorithm is proposed to further reduce the latency of data gathering,which ensures that the signal to interference plus noise ratio(SINR)of all scheduled links is not less than a certain threshold to guarantee the reliability of links.In addition,considering the interruption problem of data gathering caused by dead nodes or failed links,a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight.A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links,and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.展开更多
Data-driven methods are widely considered for fault diagnosis in complex systems.However,in practice,the between-class imbalance due to limited faulty samples may deteriorate their classification performance.To addres...Data-driven methods are widely considered for fault diagnosis in complex systems.However,in practice,the between-class imbalance due to limited faulty samples may deteriorate their classification performance.To address this issue,synthetic minority methods for enhancing data have been proved to be effective in many applications.Generative adversarial networks(GANs),capable of automatic features extraction,can also be adopted for augmenting the faulty samples.However,the monitoring data of a complex system may include not only continuous signals but also discrete/categorical signals.Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data,a Mixed Dual Discriminator GAN(noted as M-D2GAN)is proposed in this work.In order to render the expanded fault samples more aligned with the real situation and improve the accuracy and robustness of the fault diagnosis model,different types of variables are generated in different ways,including floating-point,integer,categorical,and hierarchical.For effectively considering the class imbalance problem,proper modifications are made to the GAN model,where a normal class discriminator is added.A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework.Compared to the classic GAN,the proposed framework achieves better results with respect to F-measure and G-mean metrics.展开更多
Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint...Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.展开更多
The data nodes with heterogeneous database in early warning system for grain security seriously hampered the effective data collection in this system. In this article,the existing middleware technologies was analyzed,...The data nodes with heterogeneous database in early warning system for grain security seriously hampered the effective data collection in this system. In this article,the existing middleware technologies was analyzed,the problem-solution approach of heterogeneous data sharing was discussed through middleware technologies. Based on this method,and according to the characteristics of early warning system for grain security,the technology of data sharing in this system were researched and explored to solve the issues of collection of heterogeneous data sharing.展开更多
Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to co...Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62371098the National Key Laboratory ofWireless Communications Foundation under Grant IFN20230203the National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
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financially supported by the Major Program of National Social Science Foundation (No.16ZDA006)National Natural Science Foundation of China (Nos.71603193 and 71974151)Teaching and Research Project of Wuhan University (No.1201-413200127)。
文摘This study aims to investigate the influence of rapid economic development on pollution at the municipal level in China.It constructs a Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT model) and uses comprehensive municipal data on industrial pollution and economic performance.The dataset contains 290 cities from2003 to 2016 as a sample for the panel data analysis.The study further separates the cities into two groups by their levels of economic development for heterogeneity analysis.It reveals that a low level of economic development would aggravate environmental pollution,and when the economy reaches a high level,this economic development will improve environmental quality.We also find that the relationships between foreign direct investment and industrial dust and sulfur dioxide(SO_2) discharge are significant,while the relationship between economic growth and effluent emission is not.The more developed subsample cities present an inverted U-shaped curve between industrial pollutant emission,GDP per capita,and foreign direct investment,while the less developed subsamples show no such relationship.Since the shape of these curves differs among regions,their turning points vary accordingly.Based on this finding,this study suggests that the governments of more developed cities should balance environmental pollution and economic development by enhancing environmental regulations and adjusting industrial structure.
基金supported by the National Natural Science Foundation of China(Nos.92152301,12072282)。
文摘Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.
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.
基金supported by National Natural Science Foundation of China(Nos.61304131 and 61402147)Grant of China Scholarship Council(No.201608130174)+2 种基金Natural Science Foundation of Hebei Province(Nos.F2016402054 and F2014402075)the Scientific Research Plan Projects of Hebei Education Department(Nos.BJ2014019,ZD2015087 and QN2015046)the Research Program of Talent Cultivation Project in Hebei Province(No.A2016002023)
文摘A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01B187).
文摘Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model parameters.These methods allow for the sharing of only class representatives between heterogeneous clients while maintaining privacy.However,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization capabilities.To address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous environments.Furthermore,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive learning.Comprehensive experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.
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To construct mediators for data integration systems that integrate structured and semi-structured data, and to facilitate the reformulation and decomposition of the query, the presented system uses the XML processing language (XPL) for the mediator. With XPL, it is easy to construct mediators for data integration based on XML, and it can accelerate the work in the mediator.
基金supported by the National Key Research and Development Program of China(Nos.2023YFC3502604,2022YFC2403902,2020YFC0841600,and 2020YFC0845000-4)the National Natural Science Foundation of China(Nos.82374302,82174533,82204941,and U23B2062)+3 种基金the Natural Science Foundation of Beijing(No.L232033)the Key R&D project of Ningxia Autonomous Region(No.2022BEG02036)the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0505700)the Fundamental Research Funds for the Central Universities(No.2024JBMC007).
文摘Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborative modeling and training across multiple endpoints without exposing the original data.However,the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios.We propose a Federated Contrast Enhancement(FedCE)method for NER to address the challenges faced by non-large-scale pre-trained models in FL for labelheterogeneous.The method leverages a multi-view encoder structure to capture both global and local semantic information,and employs contrastive learning to enhance the interoperability of global knowledge and local context.We evaluate the performance of the FedCE method on three real-world clinical record datasets.We investigate the impact of factors,such as pooling methods,maximum input text length,and training rounds on FedCE.Additionally,we assess how well FedCE adapts to the base NER models and evaluate its generalization performance.The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models,which is of great theoretical and practical significance for advancing FL in healthcare settings.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61203147,61374047,61203126,and 61104092)the Humanities and Social Sciences Youth Funds of the Ministry of Education,China(Grant No.12YJCZH218)
文摘In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
文摘A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.
文摘The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism.The results show that,there is a 'U' type nonlinear relationship between the ERI and GML.The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion.There is a 'U' type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML.The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI,while the CAC plays a significant guiding role in upgrading of the energy consumption structure.There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area,and the CAC is not significantly.Meanwhile,both of the ERI shows no positive effects in the central and western inland region.
基金This work was supported by the Natural Science Foun-dation of China(Nos.U1334210 and 61374059).
文摘In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with construction of data gathering trees,channel allocation,power assignment of nodes and link scheduling,is formulated as an optimization problem in this paper.Then,the optimization problem is proved to be NP-hard.To make the problem tractable,firstly,a multi-channel-based low-latency(MCLL)algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes.Secondly,a maximum links scheduling(MLS)algorithm is proposed to further reduce the latency of data gathering,which ensures that the signal to interference plus noise ratio(SINR)of all scheduled links is not less than a certain threshold to guarantee the reliability of links.In addition,considering the interruption problem of data gathering caused by dead nodes or failed links,a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight.A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links,and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.
文摘Data-driven methods are widely considered for fault diagnosis in complex systems.However,in practice,the between-class imbalance due to limited faulty samples may deteriorate their classification performance.To address this issue,synthetic minority methods for enhancing data have been proved to be effective in many applications.Generative adversarial networks(GANs),capable of automatic features extraction,can also be adopted for augmenting the faulty samples.However,the monitoring data of a complex system may include not only continuous signals but also discrete/categorical signals.Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data,a Mixed Dual Discriminator GAN(noted as M-D2GAN)is proposed in this work.In order to render the expanded fault samples more aligned with the real situation and improve the accuracy and robustness of the fault diagnosis model,different types of variables are generated in different ways,including floating-point,integer,categorical,and hierarchical.For effectively considering the class imbalance problem,proper modifications are made to the GAN model,where a normal class discriminator is added.A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework.Compared to the classic GAN,the proposed framework achieves better results with respect to F-measure and G-mean metrics.
基金partly supported by National Natural Science Foundation of China(No.71771204,72231010)the Fundamental Research Funds for the Central Universities(No.E0E48946X2).
文摘Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.
基金Supported by Monitoring and Early warning System for Grain Security in Henan (0613024000)
文摘The data nodes with heterogeneous database in early warning system for grain security seriously hampered the effective data collection in this system. In this article,the existing middleware technologies was analyzed,the problem-solution approach of heterogeneous data sharing was discussed through middleware technologies. Based on this method,and according to the characteristics of early warning system for grain security,the technology of data sharing in this system were researched and explored to solve the issues of collection of heterogeneous data sharing.
基金financial and data support from NISOC Oil Company.
文摘Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.
