This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consens...This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.展开更多
This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynami...This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.展开更多
Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services ope...Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.展开更多
BACKGROUND Septic shock is a severe form of sepsis characterised by deterioration in circulatory and cellular-metabolic parameters.Despite standard therapy,the outcomes are poor.Newer adjuvant therapy,such as CytoSorb...BACKGROUND Septic shock is a severe form of sepsis characterised by deterioration in circulatory and cellular-metabolic parameters.Despite standard therapy,the outcomes are poor.Newer adjuvant therapy,such as CytoSorb®extracorporeal haemoadsorption device,has been investigated and shown promising outcome.However,there is a lack of some guidance to make clinical decisions on the use of CytoSorb®haemoadsorption as an adjuvant therapy in septic shock in Indian Setting.Therefore,this expert consensus was formulated.AIM To formulate/establish specific consensus statements on the use of CytoSorb®haemoadsorption treatment based on the best available evidence and contextualised to the Indian scenario.METHODS We performed a comprehensive literature on CytoSorb®haemoadsorption in sepsis,septic shock in PubMed selecting papers published between January 2011 and March 20232021 in English language.The statements for a consensus document were developed based on the summarised literature analysis and identification of knowledge gaps.Using a modified Delphi approach combining evidence appraisal and expert opinion,the following topics related to CytoSorb®in septic shock were addressed:need for adjuvant therapy,initiation timeline,need for Interleukin-6 levels,duration of therapy,change of adsorbers,safety,prerequisite condition,efficacy endpoints and management flowchart.Eleven expert members from critical care,emergency medicine,and the intensive care participated and voted on nine statements and one open-ended question.RESULTS Eleven expert members from critical care,emergency medicine,and the intensive care participated and voted on nine statements and one open-ended question.All 11 experts in the consensus group(100%)participated in the first,second and third round of voting.After three iterative voting rounds and adapting two statements,consensus was achieved on nine statements out of nine statements.The consensus expert panel also recognised the necessity to form an association or society that can keep a registry regarding the use of CytoSorb®for all indications in the open-ended question(Q10)focusing on“future recommendations for CytoSorb®therapy”.CONCLUSION This Indian perspective consensus statement supports and provides guidance on the use of CytoSorb®haemoadsorption as an adjuvant treatment in patients with septic shock to achieve optimal outcomes.展开更多
Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains.Therefore,this paper proposes a new consortium chain generation ...Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains.Therefore,this paper proposes a new consortium chain generation model,deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes,and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security.Furthermore,the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived.When either of the two is lower than the threshold,the consortium institution or the verification node set members are dynamically adjusted.On this basis,an innovative consortium chain generation mechanism based on the Asynchronous Binary Byzantine Consensus Mechanism(ABBCM)is proposed.Based on the extended consortium chain consensus mechanism,a certain consensus value set can be combined into multiple proposals,which can realize crossdomain asynchronous message passing between multi-consortium chains without reducing the system’s security.In addition,experiments are carried out under four classical Byzantine Attack(BA)behaviors,BA1 to BA4.The results show that the proposed method can obtain better delay than the classical random Byzantine consensus algorithm Coin,effectively improving the consensus efficiency based on asynchronous message passing in the consortium chain and thus meeting the throughput of most Internet of Things(IoT)applications.展开更多
Continuous renal replacement therapy(CRRT)is widely used for treating critically-ill patients in the emergency department in China.Anticoagulant therapy is needed to prevent clotting in the extracorporeal circulation ...Continuous renal replacement therapy(CRRT)is widely used for treating critically-ill patients in the emergency department in China.Anticoagulant therapy is needed to prevent clotting in the extracorporeal circulation during CRRT.Regional citrate anticoagulation(RCA)has been shown to potentially be safer and more effective,and is now recommended as the preferred anticoagulant method for CRRT.However,there is still a lack of unified standards for RCA management in the world,and there are many problems in using this method in clinical practice.The Emergency Medical Doctor Branch of the Chinese Medical Doctor Association(CMDA)organized a panel of domestic emergency medicine experts and international experts of CRRT to discuss RCA-related issues,including the advantages and disadvantages of RCA in CRRT anticoagulation,the principle of RCA,parameter settings for RCA,monitoring of RCA(mainly metabolic acid-base disorders),and special issues during RCA.Based on the latest available research evidence as well as the paneled experts'clinical experience,considering the generalizability,suitability,and potential resource utilization,while also balancing clinical advantages and disadvantages,a total of 16 guideline recommendations were formed from the experts'consensus.展开更多
The robotic liver resection(RLR)has been increasingly applied in recent years and its benefits shown in some aspects owing to the technical advancement of robotic surgical system,however,controversies still exist.Base...The robotic liver resection(RLR)has been increasingly applied in recent years and its benefits shown in some aspects owing to the technical advancement of robotic surgical system,however,controversies still exist.Based on the foundation of the previous consensus statement,this new consensus document aimed to update clinical recommendations and provide guidance to improve the outcomes of RLR clinical practice.The guideline steering group and guideline expert group were formed by 29 international experts of liver surgery and evidence-based medicine(EBM).Relevant literature was reviewed and analyzed by the evidence evaluation group.According to the WHO Handbook for Guideline Development,the Guidance Principles of Development and Amendment of the Guidelines for Clinical Diagnosis and Treatment in China 2022,a total of 14 recommendations were generated.Among them were 8 recommendations formulated by the GRADE method,and the remaining 6 recommendations were formulated based on literature review and experts’opinion due to insufficient EBM results.This international experts consensus guideline offered guidance for the safe and effective clinical practice and the research direction of RLR in future.展开更多
This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs.The dynamics of followers are allowed to be non-mini...This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs.The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees.This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree.A distributed adaptive pole placement control scheme is developed,which consists of a distributed observer and an adaptive pole placement control law.It is shown that under the proposed distributed adaptive control scheme,all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically.The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.展开更多
Corona virus disease 2019(COVID-19)infection has become a major public health issue affecting human health.The main goal of epidemic prevention and control at the current stage in China is to“protect people’s health...Corona virus disease 2019(COVID-19)infection has become a major public health issue affecting human health.The main goal of epidemic prevention and control at the current stage in China is to“protect people’s health and prevent severe cases”.Patients with lung cancer who receive antitumor therapy have low immunity,and the risk of severe illness and death once infected is much higher than healthy people,so they are vulnerable to COVID-19 infection.At present,less attention has been paid to the prevention and treatment of COVID-19 infection in patients with lung cancer in domestic guidelines and consensus.Based on the published data in China and abroad,we proposed recommendations and formed expert consensus on the vaccination of COVID-19,the use of neutralizing antibodies and small molecule antiviral drugs for patients with lung cancer,for physician’s reference.展开更多
In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of...In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader.Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties,the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances.Furthermore,the Lyapunov stability theory is used to demonstrate that followers’angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded.Finally,the numerical simulation results confirm the efficacy of the proposed controllers.展开更多
In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a...In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.展开更多
This paper studies the connectivity-maintaining consensus of multi-agent systems.Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication manag...This paper studies the connectivity-maintaining consensus of multi-agent systems.Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved.In this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus.The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving consensus.For the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving consensus.Simulation results indicate the effectiveness and advantages of our scheme.展开更多
The problem of fixed-time group consensus for second-order multi-agent systems with disturbances is investigated.For cooperative-competitive network,two different control protocols,fixed-time group consensus and fixed...The problem of fixed-time group consensus for second-order multi-agent systems with disturbances is investigated.For cooperative-competitive network,two different control protocols,fixed-time group consensus and fixed-time eventtriggered group consensus,are designed.It is demonstrated that there is no Zeno behavior under the designed eventtriggered control.Meanwhile,it is proved that for an arbitrary initial state of the system,group consensus within the settling time could be obtained under the proposed control protocols by using matrix analysis and graph theory.Finally,a series of numerical examples are propounded to illustrate the performance of the proposed control protocol.展开更多
With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bri...With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).展开更多
This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reput...This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.展开更多
One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approac...One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.展开更多
This article deals with the consensus problem of multi-agent systems by developing a fixed-time consensus control approach with a dynamic event-triggered rule. First, a new fixedtime stability condition is obtained wh...This article deals with the consensus problem of multi-agent systems by developing a fixed-time consensus control approach with a dynamic event-triggered rule. First, a new fixedtime stability condition is obtained where the less conservative settling time is given such that the theoretical settling time can well reflect the real consensus time. Second, a dynamic event-triggered rule is designed to decrease the use of chip and network resources where Zeno behaviors can be avoided after consensus is achieved, especially for finite/fixed-time consensus control approaches. Third, in terms of the developed dynamic event-triggered rule, a fixed-time consensus control approach by introducing a new item is proposed to coordinate the multi-agent system to reach consensus. The corresponding stability of the multi-agent system with the proposed control approach and dynamic eventtriggered rule is analyzed based on Lyapunov theory and the fixed-time stability theorem. At last, the effectiveness of the dynamic event-triggered fixed-time consensus control approach is verified by simulations and experiments for the problem of magnetic map construction based on multiple mobile robots.展开更多
Time synchronization is one of the base techniques in wireless sensor networks(WSNs).This paper proposes a novel time synchronization protocol which is a robust consensusbased algorithm in the existence of transmissio...Time synchronization is one of the base techniques in wireless sensor networks(WSNs).This paper proposes a novel time synchronization protocol which is a robust consensusbased algorithm in the existence of transmission delay and packet loss.It compensates for transmission delay and packet loss firstly,and then,estimates clock skew and clock offset in two steps.Simulation and experiment results show that the proposed protocol can keep synchronization error below 2μs in the grid network of 10 nodes or the random network of 90 nodes.Moreover,the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.展开更多
Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated ...Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.展开更多
Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
基金supported in part by the National Natural Science Foundation of China (NSFC)(61703086, 61773106)the IAPI Fundamental Research Funds (2018ZCX27)
文摘This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.
基金supported in part by the National Natural Science Foundation of China(51939001,61976033,62273072)the Natural Science Foundation of Sichuan Province (2022NSFSC0903)。
文摘This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.
基金sponsored by the National Natural Science Foundation of China Nos.62172353,62302114 and U20B2046Future Network Scientific Research Fund Project No.FNSRFP-2021-YB-48Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1221045。
文摘Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.
文摘BACKGROUND Septic shock is a severe form of sepsis characterised by deterioration in circulatory and cellular-metabolic parameters.Despite standard therapy,the outcomes are poor.Newer adjuvant therapy,such as CytoSorb®extracorporeal haemoadsorption device,has been investigated and shown promising outcome.However,there is a lack of some guidance to make clinical decisions on the use of CytoSorb®haemoadsorption as an adjuvant therapy in septic shock in Indian Setting.Therefore,this expert consensus was formulated.AIM To formulate/establish specific consensus statements on the use of CytoSorb®haemoadsorption treatment based on the best available evidence and contextualised to the Indian scenario.METHODS We performed a comprehensive literature on CytoSorb®haemoadsorption in sepsis,septic shock in PubMed selecting papers published between January 2011 and March 20232021 in English language.The statements for a consensus document were developed based on the summarised literature analysis and identification of knowledge gaps.Using a modified Delphi approach combining evidence appraisal and expert opinion,the following topics related to CytoSorb®in septic shock were addressed:need for adjuvant therapy,initiation timeline,need for Interleukin-6 levels,duration of therapy,change of adsorbers,safety,prerequisite condition,efficacy endpoints and management flowchart.Eleven expert members from critical care,emergency medicine,and the intensive care participated and voted on nine statements and one open-ended question.RESULTS Eleven expert members from critical care,emergency medicine,and the intensive care participated and voted on nine statements and one open-ended question.All 11 experts in the consensus group(100%)participated in the first,second and third round of voting.After three iterative voting rounds and adapting two statements,consensus was achieved on nine statements out of nine statements.The consensus expert panel also recognised the necessity to form an association or society that can keep a registry regarding the use of CytoSorb®for all indications in the open-ended question(Q10)focusing on“future recommendations for CytoSorb®therapy”.CONCLUSION This Indian perspective consensus statement supports and provides guidance on the use of CytoSorb®haemoadsorption as an adjuvant treatment in patients with septic shock to achieve optimal outcomes.
基金supported by Henan University Science and Technology Innovation Talent Support Program(23HASTIT029)the National Natural Science Foundation of China(61902447)+3 种基金Tianjin Natural Science Foundation Key Project(22JCZDJC00600)Research Project of Humanities and Social Sciences in Universities of Henan Province(2024-ZDJH-061)Key Scientific Research Projects of Colleges and Universities in Henan Province(23A520054)Henan Science and Technology Research Project(232102210124).
文摘Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains.Therefore,this paper proposes a new consortium chain generation model,deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes,and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security.Furthermore,the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived.When either of the two is lower than the threshold,the consortium institution or the verification node set members are dynamically adjusted.On this basis,an innovative consortium chain generation mechanism based on the Asynchronous Binary Byzantine Consensus Mechanism(ABBCM)is proposed.Based on the extended consortium chain consensus mechanism,a certain consensus value set can be combined into multiple proposals,which can realize crossdomain asynchronous message passing between multi-consortium chains without reducing the system’s security.In addition,experiments are carried out under four classical Byzantine Attack(BA)behaviors,BA1 to BA4.The results show that the proposed method can obtain better delay than the classical random Byzantine consensus algorithm Coin,effectively improving the consensus efficiency based on asynchronous message passing in the consortium chain and thus meeting the throughput of most Internet of Things(IoT)applications.
文摘Continuous renal replacement therapy(CRRT)is widely used for treating critically-ill patients in the emergency department in China.Anticoagulant therapy is needed to prevent clotting in the extracorporeal circulation during CRRT.Regional citrate anticoagulation(RCA)has been shown to potentially be safer and more effective,and is now recommended as the preferred anticoagulant method for CRRT.However,there is still a lack of unified standards for RCA management in the world,and there are many problems in using this method in clinical practice.The Emergency Medical Doctor Branch of the Chinese Medical Doctor Association(CMDA)organized a panel of domestic emergency medicine experts and international experts of CRRT to discuss RCA-related issues,including the advantages and disadvantages of RCA in CRRT anticoagulation,the principle of RCA,parameter settings for RCA,monitoring of RCA(mainly metabolic acid-base disorders),and special issues during RCA.Based on the latest available research evidence as well as the paneled experts'clinical experience,considering the generalizability,suitability,and potential resource utilization,while also balancing clinical advantages and disadvantages,a total of 16 guideline recommendations were formed from the experts'consensus.
文摘The robotic liver resection(RLR)has been increasingly applied in recent years and its benefits shown in some aspects owing to the technical advancement of robotic surgical system,however,controversies still exist.Based on the foundation of the previous consensus statement,this new consensus document aimed to update clinical recommendations and provide guidance to improve the outcomes of RLR clinical practice.The guideline steering group and guideline expert group were formed by 29 international experts of liver surgery and evidence-based medicine(EBM).Relevant literature was reviewed and analyzed by the evidence evaluation group.According to the WHO Handbook for Guideline Development,the Guidance Principles of Development and Amendment of the Guidelines for Clinical Diagnosis and Treatment in China 2022,a total of 14 recommendations were generated.Among them were 8 recommendations formulated by the GRADE method,and the remaining 6 recommendations were formulated based on literature review and experts’opinion due to insufficient EBM results.This international experts consensus guideline offered guidance for the safe and effective clinical practice and the research direction of RLR in future.
基金This work was supported by Research Grants Council of Hong Kong(CityU-11205221).
文摘This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs.The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees.This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree.A distributed adaptive pole placement control scheme is developed,which consists of a distributed observer and an adaptive pole placement control law.It is shown that under the proposed distributed adaptive control scheme,all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically.The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.
文摘Corona virus disease 2019(COVID-19)infection has become a major public health issue affecting human health.The main goal of epidemic prevention and control at the current stage in China is to“protect people’s health and prevent severe cases”.Patients with lung cancer who receive antitumor therapy have low immunity,and the risk of severe illness and death once infected is much higher than healthy people,so they are vulnerable to COVID-19 infection.At present,less attention has been paid to the prevention and treatment of COVID-19 infection in patients with lung cancer in domestic guidelines and consensus.Based on the published data in China and abroad,we proposed recommendations and formed expert consensus on the vaccination of COVID-19,the use of neutralizing antibodies and small molecule antiviral drugs for patients with lung cancer,for physician’s reference.
基金This work was supported in part by the National Key Research and Development Program of China(2021YFB3202200)Guangdong Basic and Applied Basic Research Foundation(2020B1515120071,2021B1515120017).
文摘In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader.Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties,the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances.Furthermore,the Lyapunov stability theory is used to demonstrate that followers’angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded.Finally,the numerical simulation results confirm the efficacy of the proposed controllers.
基金supported in part by the National Natural Science Foundation of China(U1804147,61833001,61873139,61573129)the Innovative Scientists and Technicians Team of Henan Polytechnic University(T2019-2)the Innovative Scientists and Technicians Team of Henan Provincial High Education(20IRTSTHN019)。
文摘In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.
基金supported by the National Key Research and Development Program of China(2018AAA0101701)the National Natural Science Foundation of China(62173224,61833012)。
文摘This paper studies the connectivity-maintaining consensus of multi-agent systems.Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved.In this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus.The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving consensus.For the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving consensus.Simulation results indicate the effectiveness and advantages of our scheme.
基金Project supported by the Graduate Student Research Innovation Project of Chongqing(Grant No.CYS22482)the National Natural Science Foundation of China(Grant No.61773082)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K202000601)the Research Program of Chongqing Talent,China(Grant No.cstc2021ycjhbgzxm0044).
文摘The problem of fixed-time group consensus for second-order multi-agent systems with disturbances is investigated.For cooperative-competitive network,two different control protocols,fixed-time group consensus and fixed-time eventtriggered group consensus,are designed.It is demonstrated that there is no Zeno behavior under the designed eventtriggered control.Meanwhile,it is proved that for an arbitrary initial state of the system,group consensus within the settling time could be obtained under the proposed control protocols by using matrix analysis and graph theory.Finally,a series of numerical examples are propounded to illustrate the performance of the proposed control protocol.
基金supported by the Technology Project of State Grid Tianjin Electric Power Company(KJ22-1-47).
文摘With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).
基金funded by the Ministry of Education,Culture,Research,and Technology(Kemendikbudristek)of Indonesia under PDD Grant with Grant Number NKB1016/UN2.RST/HKP.05.00/2022.
文摘This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.
文摘One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.
基金supported in part by the National Natural Science Foundation of China (62073108)the Zhejiang Provincial Natural Science Foundation(LZ23F030004)+1 种基金the Key Research and Development Project of Zhejiang Province (2019C04018)the Fundamental Research Funds for the Provincial Universities of Zhejiang (GK229909299001-004)。
文摘This article deals with the consensus problem of multi-agent systems by developing a fixed-time consensus control approach with a dynamic event-triggered rule. First, a new fixedtime stability condition is obtained where the less conservative settling time is given such that the theoretical settling time can well reflect the real consensus time. Second, a dynamic event-triggered rule is designed to decrease the use of chip and network resources where Zeno behaviors can be avoided after consensus is achieved, especially for finite/fixed-time consensus control approaches. Third, in terms of the developed dynamic event-triggered rule, a fixed-time consensus control approach by introducing a new item is proposed to coordinate the multi-agent system to reach consensus. The corresponding stability of the multi-agent system with the proposed control approach and dynamic eventtriggered rule is analyzed based on Lyapunov theory and the fixed-time stability theorem. At last, the effectiveness of the dynamic event-triggered fixed-time consensus control approach is verified by simulations and experiments for the problem of magnetic map construction based on multiple mobile robots.
文摘Time synchronization is one of the base techniques in wireless sensor networks(WSNs).This paper proposes a novel time synchronization protocol which is a robust consensusbased algorithm in the existence of transmission delay and packet loss.It compensates for transmission delay and packet loss firstly,and then,estimates clock skew and clock offset in two steps.Simulation and experiment results show that the proposed protocol can keep synchronization error below 2μs in the grid network of 10 nodes or the random network of 90 nodes.Moreover,the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP2/42/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R114)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
