Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which...Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.展开更多
Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing be...Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.展开更多
Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the p...Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the production line,the manufacturing layer and the workshop layer.The dynamics of re-entrant production lines are governed by hyperbolic partial differential equations(PDEs)based on the law of mass conservation.展开更多
Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitation...Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: They either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system parameters.Consequently, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form(FMCF), which is inspired by full-measured system(FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form(OCF) in existing FMS theories.The paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded conditions.The paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.展开更多
Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution netwo...Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.展开更多
The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addre...The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addressing these core bottlenecks.This paper provides a systematic review of the research background,technological evolution,core systems,key challenges,and future directions of AI technology in the field of distributed photovoltaic power generation system optimization.At the same time,this paper analyzes the current technical bottlenecks and cutting-edge response strategies.Finally,it explores fusion innovation directions such as quantum-classical hybrid algorithms and neural symbolic systems,as well as business model expansion paths such as carbon finance integration and community energy autonomy.展开更多
This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise co...This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple ...Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.展开更多
Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under dir...Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.展开更多
In this paper, distributed event-triggered performance constraint control is proposed for Heterogeneous Multiagent Systems (HMASs) including quadrotor unmanned aerial vehicles and unmanned ground vehicles in the prese...In this paper, distributed event-triggered performance constraint control is proposed for Heterogeneous Multiagent Systems (HMASs) including quadrotor unmanned aerial vehicles and unmanned ground vehicles in the presence of unknown external disturbances. To tackle the problem of different dynamic characteristics and facilitate the controller design, the virtual variable is introduced in the z axis of the nonlinear model of unmanned ground vehicles. By using this approach, a universal model is established for the HMAS. Moreover, a distributed disturbance observer is established to cope with the adverse influence of the external disturbances. Then, an Appointed-Time Prescribed Performance Function (ATPPF) is designed to restrict the tracking error in the predefined regions. On this basis, the distributed performance constraint controller is proposed for the HMAS based on the ATPPF and the distributed disturbance observer. Furthermore, the improved event-triggered mechanism is proposed with a dynamic threshold, which depends on the distance between the tracking error and the boundary of the ATPPF. Finally, the effectiveness of the proposed control method is verified by the comparative experiments on an HMAS.展开更多
A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environment...A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.展开更多
Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performan...Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model ...Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model uncertainties,a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs).Subsequently,a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control(FOSMC)method is developed,and a Feedback Multilayer Fuzzy Neural Network(FMFNN)is introduced to estimate the lumped uncertainties.This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration,even in the presence of various uncertainties.Additionally,an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents,and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis.Finally,to confirm the effectiveness of the proposed formation control protocol,some numerical simulations are presented.展开更多
This article presents information on the distribution of Ferula tadshikorum Pimenov species in the botanical—geographical region of Uzbekistan, based on data collected from available resources, including internationa...This article presents information on the distribution of Ferula tadshikorum Pimenov species in the botanical—geographical region of Uzbekistan, based on data collected from available resources, including international sites, and databases, completed papers and directly conducted field studies. At the same time, under the circumstance that the natural area is directly getting smaller, the necessity and relevance of the thesis work on the evaluation of species’ natural reserves and the evaluation of the modern situations are demonstrated through examples. The Surkhandarya region is considered to have the largest number of plant species in Uzbekistan, and it is also distinguished by the variety of species. According to the location of the botanical-geographical regions of Uzbekistan, the territory of the Surkhandarya region is divided into five botanical-geographical regions (BGR). The 5 × 5 km2 grid system map includes 882 cells. Surkhan-Sherabod (BGR), Baisun (BGR), Sangardak-Topalang (BGR), Babatag (BGR), and Kuhitang (BGR) corresponded to these. Simultaneously, the 5 × 5 km2 grid system map of Uzbekistan’s flora in the Surkhandarya region revealed the presence of F. tadshikorum in 109 indices. On the territory of the region, the botanic-geographic region mainly includes the F. tadshikorum, Babatag (BGR), Baisun (BGR), and Kohitang (BGR) indices. The natural resources of F. tadshikorum in these areas were also analyzed.展开更多
The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users wit...The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users with the right to issue a Data Subject Access Request(DSAR).Responding to such requests requires database administrators to identify information related to an individual accurately.However,manual compliance poses significant challenges and is error-prone.Database administrators need to write queries through time-consuming labor.The demand for large amounts of data by AI systems has driven the development of NoSQL databases.Due to the flexible schema of NoSQL databases,identifying personal information becomes even more challenging.This paper develops an automated tool to identify personal information that can help organizations respond to DSAR.Our tool employs a combination of various technologies,including schema extraction of NoSQL databases and relationship identification from query logs.We describe the algorithm used by our tool,detailing how it discovers and extracts implicit relationships from NoSQL databases and generates relationship graphs to help developers accurately identify personal data.We evaluate our tool on three datasets,covering different database designs,achieving an F1 score of 0.77 to 1.Experimental results demonstrate that our tool successfully identifies information relevant to the data subject.Our tool reduces manual effort and simplifies GDPR compliance,showing practical application value in enhancing the privacy performance of NOSQL databases and AI systems.展开更多
The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the...The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.展开更多
Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store.Despite the increasing reliance on database management systems,this field continues...Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store.Despite the increasing reliance on database management systems,this field continues to face numerous cyber-attacks.Database management systems serve as the foundation of any information system or application.Any cyber-attack can result in significant damage to the database system and loss of sensitive data.Consequently,cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats.Risk assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate risks.The primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems,including classifying threats,vulnerabilities,impacts,and countermeasures.This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat.Additionally,this research aims to explore technical countermeasures to protect database systems from cyber threats.This study employs the content analysis method to collect,analyze,and classify data in terms of types of threats,vulnerabilities,and countermeasures.The results indicate that SQL injection attacks and Denial of Service(DoS)attacks were the most prevalent technical threats in database systems,each accounting for 9%of incidents.Vulnerable audit trails,intrusion attempts,and ransomware attacks were classified as the second level of technical threats in database systems,comprising 7%and 5%of incidents,respectively.Furthermore,the findings reveal that insider threats were the most common non-technical threats in database systems,accounting for 5%of incidents.Moreover,the results indicate that weak authentication,unpatched databases,weak audit trails,and multiple usage of an account were the most common technical vulnerabilities in database systems,each accounting for 9%of vulnerabilities.Additionally,software bugs,insecure coding practices,weak security controls,insecure networks,password misuse,weak encryption practices,and weak data masking were classified as the second level of security vulnerabilities in database systems,each accounting for 4%of vulnerabilities.The findings from this work can assist organizations in understanding the types of cyber threats and developing robust strategies against cyber-attacks.展开更多
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金supported by the National Key Research and Development Program of China(No.2022YFA1604703)the National Natural Science Foundation of China(No.12375189)the National Key Research and Development Program of China(No.2021YFA1601300)。
文摘Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.
基金supported by NSF China(No.T2421002,62061146002,62020106005)。
文摘Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.
文摘Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the production line,the manufacturing layer and the workshop layer.The dynamics of re-entrant production lines are governed by hyperbolic partial differential equations(PDEs)based on the law of mass conservation.
基金supported by the National Natural Science Foundation of China(62133008,62303273,62188101,62373226,62473173)Young Taishan Scholars Program of Shandong Province of China(tsqn202408206)+2 种基金a Project of Shandong Province Higher Educational Youth and Innovation Talent Introduction and Education Programthe Natural Science Foundation of Shandong Province,China(ZR2023QF072)China Postdoctoral Science Foundation(2022M721932)
文摘Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: They either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system parameters.Consequently, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form(FMCF), which is inspired by full-measured system(FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form(OCF) in existing FMS theories.The paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded conditions.The paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.
基金This research was funded by“Chunhui Program”Collaborative Scientific Research Project of the Ministry of Education of the People’s Republic of China(Project No.HZKY20220242)the S&T Program of Hebei(Project No.225676163GH).
文摘Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.
文摘The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addressing these core bottlenecks.This paper provides a systematic review of the research background,technological evolution,core systems,key challenges,and future directions of AI technology in the field of distributed photovoltaic power generation system optimization.At the same time,this paper analyzes the current technical bottlenecks and cutting-edge response strategies.Finally,it explores fusion innovation directions such as quantum-classical hybrid algorithms and neural symbolic systems,as well as business model expansion paths such as carbon finance integration and community energy autonomy.
基金supported by the National Natural Science Foundation of China(61673130).
文摘This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
基金Financial support from the National Natural Science Foundation of China under Grant(22393954 and 22078358)is gratefully acknowledged.
文摘Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.
基金supported by the National Natural Science Foundation of China(62073113,62003122,62303148)the Fundamental Research Funds for the Central Universities(MCCSE2023A01,JZ2023HGTA0201,JZ2023HGQA0109)the Anhui Provincial Natural Science Foundation(2308085QF204)
文摘Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.
基金supported in part by the National Natural Science Foundation of China(Nos.U23B2036,U2013201).
文摘In this paper, distributed event-triggered performance constraint control is proposed for Heterogeneous Multiagent Systems (HMASs) including quadrotor unmanned aerial vehicles and unmanned ground vehicles in the presence of unknown external disturbances. To tackle the problem of different dynamic characteristics and facilitate the controller design, the virtual variable is introduced in the z axis of the nonlinear model of unmanned ground vehicles. By using this approach, a universal model is established for the HMAS. Moreover, a distributed disturbance observer is established to cope with the adverse influence of the external disturbances. Then, an Appointed-Time Prescribed Performance Function (ATPPF) is designed to restrict the tracking error in the predefined regions. On this basis, the distributed performance constraint controller is proposed for the HMAS based on the ATPPF and the distributed disturbance observer. Furthermore, the improved event-triggered mechanism is proposed with a dynamic threshold, which depends on the distance between the tracking error and the boundary of the ATPPF. Finally, the effectiveness of the proposed control method is verified by the comparative experiments on an HMAS.
基金Project(20030533011)supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.
基金co-supported by the National Natural Science Foundation of China(Nos.71971115 and 62173305)the Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(No.KYCX22_0366).
文摘Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
基金supported by the Beijing Municipal Science&Technology Commission China(No.Z19111000270000)the National Natural Science Foundation of China(Nos.62203050,51774042).
文摘Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model uncertainties,a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs).Subsequently,a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control(FOSMC)method is developed,and a Feedback Multilayer Fuzzy Neural Network(FMFNN)is introduced to estimate the lumped uncertainties.This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration,even in the presence of various uncertainties.Additionally,an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents,and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis.Finally,to confirm the effectiveness of the proposed formation control protocol,some numerical simulations are presented.
文摘This article presents information on the distribution of Ferula tadshikorum Pimenov species in the botanical—geographical region of Uzbekistan, based on data collected from available resources, including international sites, and databases, completed papers and directly conducted field studies. At the same time, under the circumstance that the natural area is directly getting smaller, the necessity and relevance of the thesis work on the evaluation of species’ natural reserves and the evaluation of the modern situations are demonstrated through examples. The Surkhandarya region is considered to have the largest number of plant species in Uzbekistan, and it is also distinguished by the variety of species. According to the location of the botanical-geographical regions of Uzbekistan, the territory of the Surkhandarya region is divided into five botanical-geographical regions (BGR). The 5 × 5 km2 grid system map includes 882 cells. Surkhan-Sherabod (BGR), Baisun (BGR), Sangardak-Topalang (BGR), Babatag (BGR), and Kuhitang (BGR) corresponded to these. Simultaneously, the 5 × 5 km2 grid system map of Uzbekistan’s flora in the Surkhandarya region revealed the presence of F. tadshikorum in 109 indices. On the territory of the region, the botanic-geographic region mainly includes the F. tadshikorum, Babatag (BGR), Baisun (BGR), and Kohitang (BGR) indices. The natural resources of F. tadshikorum in these areas were also analyzed.
基金supported by the National Natural Science Foundation of China(No.62302242)the China Postdoctoral Science Foundation(No.2023M731802).
文摘The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users with the right to issue a Data Subject Access Request(DSAR).Responding to such requests requires database administrators to identify information related to an individual accurately.However,manual compliance poses significant challenges and is error-prone.Database administrators need to write queries through time-consuming labor.The demand for large amounts of data by AI systems has driven the development of NoSQL databases.Due to the flexible schema of NoSQL databases,identifying personal information becomes even more challenging.This paper develops an automated tool to identify personal information that can help organizations respond to DSAR.Our tool employs a combination of various technologies,including schema extraction of NoSQL databases and relationship identification from query logs.We describe the algorithm used by our tool,detailing how it discovers and extracts implicit relationships from NoSQL databases and generates relationship graphs to help developers accurately identify personal data.We evaluate our tool on three datasets,covering different database designs,achieving an F1 score of 0.77 to 1.Experimental results demonstrate that our tool successfully identifies information relevant to the data subject.Our tool reduces manual effort and simplifies GDPR compliance,showing practical application value in enhancing the privacy performance of NOSQL databases and AI systems.
文摘The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant No.KFU242068).
文摘Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store.Despite the increasing reliance on database management systems,this field continues to face numerous cyber-attacks.Database management systems serve as the foundation of any information system or application.Any cyber-attack can result in significant damage to the database system and loss of sensitive data.Consequently,cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats.Risk assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate risks.The primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems,including classifying threats,vulnerabilities,impacts,and countermeasures.This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat.Additionally,this research aims to explore technical countermeasures to protect database systems from cyber threats.This study employs the content analysis method to collect,analyze,and classify data in terms of types of threats,vulnerabilities,and countermeasures.The results indicate that SQL injection attacks and Denial of Service(DoS)attacks were the most prevalent technical threats in database systems,each accounting for 9%of incidents.Vulnerable audit trails,intrusion attempts,and ransomware attacks were classified as the second level of technical threats in database systems,comprising 7%and 5%of incidents,respectively.Furthermore,the findings reveal that insider threats were the most common non-technical threats in database systems,accounting for 5%of incidents.Moreover,the results indicate that weak authentication,unpatched databases,weak audit trails,and multiple usage of an account were the most common technical vulnerabilities in database systems,each accounting for 9%of vulnerabilities.Additionally,software bugs,insecure coding practices,weak security controls,insecure networks,password misuse,weak encryption practices,and weak data masking were classified as the second level of security vulnerabilities in database systems,each accounting for 4%of vulnerabilities.The findings from this work can assist organizations in understanding the types of cyber threats and developing robust strategies against cyber-attacks.
