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
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
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
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v...Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science.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
The synthesis method of propargylamines has always been the focus of research in organic synthetic methodology.A method of alkynylation of tertiary aliphatic amines with alkynes in the presence of copper doped zeolite...The synthesis method of propargylamines has always been the focus of research in organic synthetic methodology.A method of alkynylation of tertiary aliphatic amines with alkynes in the presence of copper doped zeolite Y as a catalyst and oxygen in the air as an oxidant has been developed.The most important feature of this reaction is that copper molecular siolite is used as catalyst,which avoids the intermolecular self-coupling of alkynes,and thus realizes the high efficiency propargylization of alkyl tertiary amines.展开更多
Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesiz...Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesized via impregnating a cobalt precursor into polymers(POPs-NVP).Comprehensive characterization revealed that the cobalt species on the catalysts exist as CoO with two distinct sizes:nanoparticles and single sites.The CoO nanoparticles on POPs-NVP exhibited outstanding hydroformylation activity(81.7%yield of aldehyde and alcohol,13.5%yield of alkane),while CoO single sites displayed robust olefin hydrogenation performance(62.6%yield of alkane,27.3% yield of aldehyde and alcohol).These divergent catalytic behaviors were attributed to distinct electron density distributions around surface-exposed cobalt species,which were critically governed by CoO sizes on catalysts.By elucidating the size-dependent effects of CoO/POPs-NVP catalysts,this work provided insights into the complex active species states in heterogeneous cobalt-based catalysts,and established valuable experimental and theoretical foundations for designing highly efficient cobalt-based heterogeneous catalysts for hydroformylation.展开更多
In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to...In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.展开更多
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication.Although various authentication and key agreement proto...The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication.Although various authentication and key agreement protocols have been developed,current approaches are constrained by homogeneous cryptosystem frameworks,namely public key infrastructure(PKI),identity-based cryptography(IBC),or certificateless cryptography(CLC),each presenting limitations in client-server architectures.Specifically,PKI incurs certificate management overhead,IBC introduces key escrow risks,and CLC encounters cross-system interoperability challenges.To overcome these shortcomings,this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client operations(eliminating PKI’s certificate dependency)with CLC for server implementation(mitigating IBC’s key escrow issue while preserving efficiency).Rigorous security analysis under the mBR(modified Bellare-Rogaway)model confirms the protocol’s resistance to adaptive chosen-ciphertext attacks.Quantitative comparisons demonstrate that the proposed protocol achieves 10.08%–71.34%lower communication overhead than existing schemes across multiple security levels(80-,112-,and 128-bit)compared to existing protocols.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Heterogeneous nucleation,characterized by its low nucleation barrier and controllable nucleation sites,has been widely employed to manipulate the microstructures and properties of metallic materials.In recent years,th...Heterogeneous nucleation,characterized by its low nucleation barrier and controllable nucleation sites,has been widely employed to manipulate the microstructures and properties of metallic materials.In recent years,the dispersion of inclusions,carbides,and microstructure refinement in steel have emerged as one of the key research directions in the development of high-quality steel.The current research status regarding the regulation of inclusions,carbides,and microstructures in steel through heterogeneous nucleation are reviewed.The key points and challenges in refining the second phase and microstructure in steel using inclusion particles are highlighted,aiming to provide inspiration and references for future scholars.Deoxidized inclusions,when refined and dispersed,exhibit favorable lattice matching with second phases(e.g.,nitrides,sulfides,carbides)in steel.This characteristic serves as the fundamental mechanism for achieving refinement of the second phase.Concurrently,the solid-solution alloying effect from deoxidizing metals contributes to second-phase refinement,an aspect that requires prioritized investigation.In addition to the single heterogeneous nucleation refinement effect,the two-stage heterogeneous nucleation refinement of the second phase and microstructure offers a new approach for follow-up research.Notably,second-phase particles added as heterogeneous nucleation sites via external addition often require surface modification to ensure their stable retention in steel at high temperatures,which remains a major challenge restricting the widespread application of this method.Currently,the explanation of heterogeneous nucleation phenomena primarily relies on empirical calculations of lattice mismatch between the substrate and the nucleating phase,which cannot fully elucidate the quantitative relationship on the interface between the substrate and the nucleation phase.On this basis,quantifying the electronic structure and nucleation barrier at the interface between the substrate and the nucleation phase is a critical direction worthy of increased attention in the future.展开更多
Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated...Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated metamorphic rocks characterized by significant anisotropy,the dependence of elastic properties on the orientation of foliation complicates the measurement and interpretation of strain data.In this study,a series of wave propagation velocity tests and UCS tests are conducted on cylindrical and prismatic slate specimens to gain a better understanding of how to obtain and process deformability and strength results.Wave propagation velocity results demonstrate an increase with the dip of foliation planes crossed,which is consistent with previous studies.Based on UCS test results,two methodologies are considered for obtaining transversely isotropic deformability parameters:the least-squares method and the recently proposed generalized reduction gradient(GRG)algorithm.Their performance is assessed in the context of potentially variable and limited amounts of data.GRG algorithms provide an enhanced analysis technique for estimating anisotropic elastic properties when dealing with limited or heterogeneous laboratory test data.Different strength models have also been considered,including the classic Jaeger's weakness plane(JPW)and its subsequent modification,i.e.2HBJPW.The 2HBJPW approach has proven to be more consistent with the obtained results and enhances the representation of the strength properties of slates.Additionally,a finite element method(FEM)numerical approach is employed to compare results with analytical and experimental ones,demonstrating a good match,thereby offering calibrated inputs for rock engineering applications.展开更多
Heterogeneous polymerization represents a widely employed method in the polyolefin industry.In recent years,various heterogenization strategies for late transition metal catalysts have been developed,enabling effectiv...Heterogeneous polymerization represents a widely employed method in the polyolefin industry.In recent years,various heterogenization strategies for late transition metal catalysts have been developed,enabling effective control of polymer morphology and optimization of catalytic performance.However,while most studies have focused on designing anchoring groups and advancing support approaches,systematic investigations into how the support influences the catalytic behavior of the late transition metal catalysts.In this work,we fabricated supported α-diimine nickel catalysts by functionalizing the ligand with alkyl alcohol chains of varying lengths and supporting them onto MgCl_(2)supports.The ethylene polymerization behavior of these catalysts was then investigated.By precisely adjusting the alkyl alcohol chain length,the distance between the catalytically active metal center and the support surface was modulated.This approach demonstrates that support-induced steric hindrance effect can be effectively regulated by controlling the separation distance between the metal center and the support surface.展开更多
To obtain protease-and lipase-producing halotolerant/halophilic strains suitable for shrimp paste(SP)fermentation,the microbial community structure and enzyme-producing microbial species were analyzed and predicted us...To obtain protease-and lipase-producing halotolerant/halophilic strains suitable for shrimp paste(SP)fermentation,the microbial community structure and enzyme-producing microbial species were analyzed and predicted using metagenomics in 3 high-salt samples.Based on the linear salt gradient method,128 strains were screened.Eight halotolerant/halophilic strains highly producing 2 types of enzymes were identified and inoculated into lowsalt SP to assess the heterogeneity of SP.Physicochemical properties of SP indicated that Bacillus subtilis XJ-11,Virgibacillus halodenitrificans XJ-229,Piscibacillus halophilus XY-193,and Bacillus vallismortis HT-73 were more suitable for rapid fermentation of SP.Nutritional analysis showed that SP inoculated with V.halodenitrificans XJ-229 had the highest free amino acid content and SP inoculated with P.halophilus XY-193 had the highest unsaturated fatty acid content.The former had prominent umami,sweetness,and meaty aroma,weak bitterness and fishy flavor,and the closest flavor to the control(CP)based on sensory evaluation and E-nose analysis.A total of 61 volatile compounds were detected in all samples by SPME-GC-MS,of which 32,23,40,24,and 28 were detected in the CP and SP inoculated with B.subtilis XJ-11,V.halodenitrificans XJ-229,P.halophilus XY-193,and B.vallismortis HT-73,respectively,with 12,11,12,9,and 9 key flavor compounds.Among several samples,the highest levels of pyrazines,aldehydes,alcohols,and ketones were found in SP inoculated with B.subtilis XJ-11,V.halodenitrificans XJ-229,P.halophilus XY-193,and B.vallismortis HT-73,respectively.These results suggested that inoculation of different enzyme-producing halotolerant/halophilic strains resulted in differences in SP quality and main flavors.This study provides some references for process control and interpretation of heterogeneous mechanisms in low-salt SP fermented by inoculated strains.展开更多
Magnesium alloys usually exhibit poor ductility because of their limited slip systems at room temperature.To overcome this intrinsic limitation,heterostructure design has emerged as an effective strategy for enhancing...Magnesium alloys usually exhibit poor ductility because of their limited slip systems at room temperature.To overcome this intrinsic limitation,heterostructure design has emerged as an effective strategy for enhancing their mechanical performance,yet the development of orientation-based heterogeneous magnesium alloys remains relatively unexplored.In this work,by varying the triaxial cyclic compression(TCC)applied to an extruded Mg-2.9Y(wt.%)alloy,we obtained two materials that possessed comparable bimodal grain-size characteristics but showed notable differences in orientation heterogeneity.The material processed by TCC along three orthogonal directions for five complete cycles exhibited a predominantly hard orientation,with hard refined grains embedded within coarse grains of the same hard orientation.By applying an additional compression to plane A,the other material mainly comprising the soft orientation was obtained,with hard-oriented refined grains embedded in soft-oriented coarse grains.These materials exhibited quite different tensile properties and work hardening abilities.By combining microstructural characterization and crystal plasticity modeling,deformation micromechanism of the materials under tensile loading was explored.In the former,poor deformation coordination between the different domains led to strain localization in the refined grain region.However,the latter experienced a significant orientation transition due to tensile twinning.This promoted non-basalslip and improved deformation compatibility,resulting in the more persistent hetero-deformation induced hardening.These findings provide fundamental insights into the micromechanical behavior of heterostructured alloys and offer a new strategy for designing high-performance hexagonal close-packed materials by introducing heterogeneous orientation distributions.展开更多
With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these i...With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these issues,we propose Federated Dynamic Prototype Learning(FedDPL)for malware classification by integrating Federated Learning with a specifically designed K-means.Under the Federated Learning framework,model training occurs locally without data sharing,effectively protecting user data privacy and preventing the leakage of sensitive information.Furthermore,to tackle the challenges of data heterogeneity and the lack of category information,FedDPL introduces a dynamic prototype learning mechanism,which adaptively adjusts the clustering prototypes in terms of position and number.Thus,the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced,resulting in improved clustering performance.Theoretically,it provides a more accurate detection of malicious behavior.Experimental results confirm that FedDPL excels in handling malware classification tasks,demonstrating superior accuracy,robustness,and privacy protection.展开更多
This paper presents an adaptive formation control method for a heterogeneous robot swarm,utilising a multilevel formation task tree to model various types of formation tasks and a single-state distributed k-winner-tak...This paper presents an adaptive formation control method for a heterogeneous robot swarm,utilising a multilevel formation task tree to model various types of formation tasks and a single-state distributed k-winner-take-all(S-DKWTA)algorithm to address the MRTA problem.In addition,we propose an enhanced load reassignment algorithm to resolve conflicts when using S-DKWTA.The S-DKWTA algorithm demonstrates the capability to manage multiple objectives and dynamically select leaders in real-time,thereby optimising formation efficiency and reducing energy consumption.The proposed approach integrates an enhanced artificial potential field(APF)to govern the motion of heterogeneous robot systems which encompasses both unmanned ground vehicles(UGVs)and unmanned aerial vehicles(UAVs),thereby achieving collision and obstacle avoidance.Simulations employing UGVs and UAVs swarm to achieve formation movement demonstrate the efficacy of this approach.The amalgamation of S-DKWTA and improved APF ensures stable and adaptable formation control,underscoring its potential for diverse multirobot applications.展开更多
This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consen...This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.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.
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 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funded by the National Natural Science Foundation of China,grant number 62172033.
文摘Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science.
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The synthesis method of propargylamines has always been the focus of research in organic synthetic methodology.A method of alkynylation of tertiary aliphatic amines with alkynes in the presence of copper doped zeolite Y as a catalyst and oxygen in the air as an oxidant has been developed.The most important feature of this reaction is that copper molecular siolite is used as catalyst,which avoids the intermolecular self-coupling of alkynes,and thus realizes the high efficiency propargylization of alkyl tertiary amines.
基金supported by the National Key Research and Development Program of China(2023YFA1508003)the National Natural Science Foundation of China(22408363,22302192)+6 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA29050300)the Youth Innovation Promotion Association CAS(2021181)the Key Research and Development Program of Liaoning(2023JH2/101800051)the Dalian of Science and Technology Project(2023RY012)the Postdoctoral Fellowship Program of CPSF(GZC20241677,GZB20230724)the Postdoctoral Science Foundation(2024T170900)the Doctoral Research Start-up Fund of Liaoning(2024-BSBA-28)。
文摘Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesized via impregnating a cobalt precursor into polymers(POPs-NVP).Comprehensive characterization revealed that the cobalt species on the catalysts exist as CoO with two distinct sizes:nanoparticles and single sites.The CoO nanoparticles on POPs-NVP exhibited outstanding hydroformylation activity(81.7%yield of aldehyde and alcohol,13.5%yield of alkane),while CoO single sites displayed robust olefin hydrogenation performance(62.6%yield of alkane,27.3% yield of aldehyde and alcohol).These divergent catalytic behaviors were attributed to distinct electron density distributions around surface-exposed cobalt species,which were critically governed by CoO sizes on catalysts.By elucidating the size-dependent effects of CoO/POPs-NVP catalysts,this work provided insights into the complex active species states in heterogeneous cobalt-based catalysts,and established valuable experimental and theoretical foundations for designing highly efficient cobalt-based heterogeneous catalysts for hydroformylation.
基金supported in part by the National Natural Science Foundation of China under Grant No.12471281in part by the National Statistical Science Research Project under Grant No.2022LD03。
文摘In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the Key Project of Science and Technology Research by Chongqing Education Commission under Grant KJZD-K202400610the Chongqing Natural Science Foundation General Project Grant CSTB2025NSCQ-GPX1263.
文摘The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication.Although various authentication and key agreement protocols have been developed,current approaches are constrained by homogeneous cryptosystem frameworks,namely public key infrastructure(PKI),identity-based cryptography(IBC),or certificateless cryptography(CLC),each presenting limitations in client-server architectures.Specifically,PKI incurs certificate management overhead,IBC introduces key escrow risks,and CLC encounters cross-system interoperability challenges.To overcome these shortcomings,this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client operations(eliminating PKI’s certificate dependency)with CLC for server implementation(mitigating IBC’s key escrow issue while preserving efficiency).Rigorous security analysis under the mBR(modified Bellare-Rogaway)model confirms the protocol’s resistance to adaptive chosen-ciphertext attacks.Quantitative comparisons demonstrate that the proposed protocol achieves 10.08%–71.34%lower communication overhead than existing schemes across multiple security levels(80-,112-,and 128-bit)compared to existing protocols.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金supported by the National Natural Science Foundation of China(No.52304358)Young Elite Scientists Sponsorship Program by CAST(No.YESS20230462).
文摘Heterogeneous nucleation,characterized by its low nucleation barrier and controllable nucleation sites,has been widely employed to manipulate the microstructures and properties of metallic materials.In recent years,the dispersion of inclusions,carbides,and microstructure refinement in steel have emerged as one of the key research directions in the development of high-quality steel.The current research status regarding the regulation of inclusions,carbides,and microstructures in steel through heterogeneous nucleation are reviewed.The key points and challenges in refining the second phase and microstructure in steel using inclusion particles are highlighted,aiming to provide inspiration and references for future scholars.Deoxidized inclusions,when refined and dispersed,exhibit favorable lattice matching with second phases(e.g.,nitrides,sulfides,carbides)in steel.This characteristic serves as the fundamental mechanism for achieving refinement of the second phase.Concurrently,the solid-solution alloying effect from deoxidizing metals contributes to second-phase refinement,an aspect that requires prioritized investigation.In addition to the single heterogeneous nucleation refinement effect,the two-stage heterogeneous nucleation refinement of the second phase and microstructure offers a new approach for follow-up research.Notably,second-phase particles added as heterogeneous nucleation sites via external addition often require surface modification to ensure their stable retention in steel at high temperatures,which remains a major challenge restricting the widespread application of this method.Currently,the explanation of heterogeneous nucleation phenomena primarily relies on empirical calculations of lattice mismatch between the substrate and the nucleating phase,which cannot fully elucidate the quantitative relationship on the interface between the substrate and the nucleation phase.On this basis,quantifying the electronic structure and nucleation barrier at the interface between the substrate and the nucleation phase is a critical direction worthy of increased attention in the future.
文摘Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated metamorphic rocks characterized by significant anisotropy,the dependence of elastic properties on the orientation of foliation complicates the measurement and interpretation of strain data.In this study,a series of wave propagation velocity tests and UCS tests are conducted on cylindrical and prismatic slate specimens to gain a better understanding of how to obtain and process deformability and strength results.Wave propagation velocity results demonstrate an increase with the dip of foliation planes crossed,which is consistent with previous studies.Based on UCS test results,two methodologies are considered for obtaining transversely isotropic deformability parameters:the least-squares method and the recently proposed generalized reduction gradient(GRG)algorithm.Their performance is assessed in the context of potentially variable and limited amounts of data.GRG algorithms provide an enhanced analysis technique for estimating anisotropic elastic properties when dealing with limited or heterogeneous laboratory test data.Different strength models have also been considered,including the classic Jaeger's weakness plane(JPW)and its subsequent modification,i.e.2HBJPW.The 2HBJPW approach has proven to be more consistent with the obtained results and enhances the representation of the strength properties of slates.Additionally,a finite element method(FEM)numerical approach is employed to compare results with analytical and experimental ones,demonstrating a good match,thereby offering calibrated inputs for rock engineering applications.
基金financially supported by the National Natural Science Foundation of China(No.52473338)the National Natural Science Foundation of China(Nos.52173004 and 51873055)+3 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA0540000)Advanced Materials-National Science and Technology Major Project(No.2025ZD0614000)Hebei Natural Science Foundation(No.E2022202015)Anhui Province Science and Technology Innovation Tackling Key Project(No.202423i08050025)。
文摘Heterogeneous polymerization represents a widely employed method in the polyolefin industry.In recent years,various heterogenization strategies for late transition metal catalysts have been developed,enabling effective control of polymer morphology and optimization of catalytic performance.However,while most studies have focused on designing anchoring groups and advancing support approaches,systematic investigations into how the support influences the catalytic behavior of the late transition metal catalysts.In this work,we fabricated supported α-diimine nickel catalysts by functionalizing the ligand with alkyl alcohol chains of varying lengths and supporting them onto MgCl_(2)supports.The ethylene polymerization behavior of these catalysts was then investigated.By precisely adjusting the alkyl alcohol chain length,the distance between the catalytically active metal center and the support surface was modulated.This approach demonstrates that support-induced steric hindrance effect can be effectively regulated by controlling the separation distance between the metal center and the support surface.
基金supported by the National Natural Science Foundation of China(22138004)Shaoxing Science and Technology Plan Project(2022B43001,2023B43001).
文摘To obtain protease-and lipase-producing halotolerant/halophilic strains suitable for shrimp paste(SP)fermentation,the microbial community structure and enzyme-producing microbial species were analyzed and predicted using metagenomics in 3 high-salt samples.Based on the linear salt gradient method,128 strains were screened.Eight halotolerant/halophilic strains highly producing 2 types of enzymes were identified and inoculated into lowsalt SP to assess the heterogeneity of SP.Physicochemical properties of SP indicated that Bacillus subtilis XJ-11,Virgibacillus halodenitrificans XJ-229,Piscibacillus halophilus XY-193,and Bacillus vallismortis HT-73 were more suitable for rapid fermentation of SP.Nutritional analysis showed that SP inoculated with V.halodenitrificans XJ-229 had the highest free amino acid content and SP inoculated with P.halophilus XY-193 had the highest unsaturated fatty acid content.The former had prominent umami,sweetness,and meaty aroma,weak bitterness and fishy flavor,and the closest flavor to the control(CP)based on sensory evaluation and E-nose analysis.A total of 61 volatile compounds were detected in all samples by SPME-GC-MS,of which 32,23,40,24,and 28 were detected in the CP and SP inoculated with B.subtilis XJ-11,V.halodenitrificans XJ-229,P.halophilus XY-193,and B.vallismortis HT-73,respectively,with 12,11,12,9,and 9 key flavor compounds.Among several samples,the highest levels of pyrazines,aldehydes,alcohols,and ketones were found in SP inoculated with B.subtilis XJ-11,V.halodenitrificans XJ-229,P.halophilus XY-193,and B.vallismortis HT-73,respectively.These results suggested that inoculation of different enzyme-producing halotolerant/halophilic strains resulted in differences in SP quality and main flavors.This study provides some references for process control and interpretation of heterogeneous mechanisms in low-salt SP fermented by inoculated strains.
基金supported by the National Key Research and Development Program of China(2021YFA1200203)the National Natural Science Foundation of China(Nos.52371097,51922026,52301136)+1 种基金the Fundamental Research Funds for the Central Universities(Nos.X2025003201,N25QNR005 and N25ZLE004)the Youth Science Foundation Project(Category A)of Liaoning Province(No.2025JH6/101100006).
文摘Magnesium alloys usually exhibit poor ductility because of their limited slip systems at room temperature.To overcome this intrinsic limitation,heterostructure design has emerged as an effective strategy for enhancing their mechanical performance,yet the development of orientation-based heterogeneous magnesium alloys remains relatively unexplored.In this work,by varying the triaxial cyclic compression(TCC)applied to an extruded Mg-2.9Y(wt.%)alloy,we obtained two materials that possessed comparable bimodal grain-size characteristics but showed notable differences in orientation heterogeneity.The material processed by TCC along three orthogonal directions for five complete cycles exhibited a predominantly hard orientation,with hard refined grains embedded within coarse grains of the same hard orientation.By applying an additional compression to plane A,the other material mainly comprising the soft orientation was obtained,with hard-oriented refined grains embedded in soft-oriented coarse grains.These materials exhibited quite different tensile properties and work hardening abilities.By combining microstructural characterization and crystal plasticity modeling,deformation micromechanism of the materials under tensile loading was explored.In the former,poor deformation coordination between the different domains led to strain localization in the refined grain region.However,the latter experienced a significant orientation transition due to tensile twinning.This promoted non-basalslip and improved deformation compatibility,resulting in the more persistent hetero-deformation induced hardening.These findings provide fundamental insights into the micromechanical behavior of heterostructured alloys and offer a new strategy for designing high-performance hexagonal close-packed materials by introducing heterogeneous orientation distributions.
基金supported by the National Natural Science Foundation of China under Grant No.62162009the Key Technologies R&D Program of He’nan Province under Grant No.242102211065+2 种基金the Postgraduate Education Reform and Quality Improvement Project of Henan Province under Grant Nos.YJS2025GZZ36,YJS2024AL112,and YJS2024JD38the Innovation Scientists and Technicians Troop Construction Projects of Henan Province under Grant No.CXTD2017099the Scientific Research Innovation Team of Xuchang University under Grant No.2022CXTD003.
文摘With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these issues,we propose Federated Dynamic Prototype Learning(FedDPL)for malware classification by integrating Federated Learning with a specifically designed K-means.Under the Federated Learning framework,model training occurs locally without data sharing,effectively protecting user data privacy and preventing the leakage of sensitive information.Furthermore,to tackle the challenges of data heterogeneity and the lack of category information,FedDPL introduces a dynamic prototype learning mechanism,which adaptively adjusts the clustering prototypes in terms of position and number.Thus,the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced,resulting in improved clustering performance.Theoretically,it provides a more accurate detection of malicious behavior.Experimental results confirm that FedDPL excels in handling malware classification tasks,demonstrating superior accuracy,robustness,and privacy protection.
基金supported by the National Natural Science Foundation of China(624B2140).
文摘This paper presents an adaptive formation control method for a heterogeneous robot swarm,utilising a multilevel formation task tree to model various types of formation tasks and a single-state distributed k-winner-take-all(S-DKWTA)algorithm to address the MRTA problem.In addition,we propose an enhanced load reassignment algorithm to resolve conflicts when using S-DKWTA.The S-DKWTA algorithm demonstrates the capability to manage multiple objectives and dynamically select leaders in real-time,thereby optimising formation efficiency and reducing energy consumption.The proposed approach integrates an enhanced artificial potential field(APF)to govern the motion of heterogeneous robot systems which encompasses both unmanned ground vehicles(UGVs)and unmanned aerial vehicles(UAVs),thereby achieving collision and obstacle avoidance.Simulations employing UGVs and UAVs swarm to achieve formation movement demonstrate the efficacy of this approach.The amalgamation of S-DKWTA and improved APF ensures stable and adaptable formation control,underscoring its potential for diverse multirobot applications.
基金supported by the National Natural Science Foundation of China(62463007,62463005)the Natural Science Foundation of Hainan Province(625RC710,625MS047)+1 种基金the System Control and Information Processing Education Ministry Key Laboratory Open Funding,China(Scip20240119)the Science Research Funding of Hainan University,China(KYQD(ZR)22180,KYQD(ZR)23180).
文摘This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.
