Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise...Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes＇ sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes＇ sequence in the graph. Jeffries-Matusita distance （JMD） is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method.展开更多
Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing an...Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.展开更多
The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to ...The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.展开更多
In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structu...In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.展开更多
Background:China's Notifiable Disease Reporting System comprehensively collects data on pediatric acute gas-troenteritis cases from all levels of healthcare.This study evaluated the effectiveness of the pentavalen...Background:China's Notifiable Disease Reporting System comprehensively collects data on pediatric acute gas-troenteritis cases from all levels of healthcare.This study evaluated the effectiveness of the pentavalent rotavirus vaccine against rotavirus gastroenteritis in children under five years of age during seasonal epidemic periods.Methods:Using a test-negative design,we analyzed laboratory data from diarrhea patients aged 2-59 months who sought medical care in Longhua District,Shenzhen,Guangdong Province,China between January 2020 and March 2024.Vaccination history and potential confounders,including sex,household registration status,prema-turity,and birth weight,were collected retrospectively.Directed acyclic graphs were used to identify adjustment variables.Vaccine effectiveness was calculated as(1−adjusted odds ratio)×100%using unconditional logistic regression,comparing rotavirus-positive cases with rotavirus-negative controls.Results:The incidence of rotavirus gastroenteritis peaked and then progressively declined from January 2020 through March 2023,with no seasonal resurgence observed in the first quarter of 2023.Analysis of 928 epidemic-season cases(674 RV+;254 RV-)revealed an overall pentavalent rotavirus(RV5)vaccine effectiveness of 79.6%(95%CI:68.9%-86.6%).Dose-specific vaccine effectiveness varied substantially:1 dose vaccine effectiveness registered at 26.7%(95%CI:−162.1%-79.5%),rising to 76.6%(95%CI:46.2%-89.8%)for 2 doses,and reaching 82.7%(95%CI:72.2%-89.2%)for 3 doses.Age-stratified analysis showed vaccine effectiveness of 79.5%(95%CI:56.8%-90.3%)in children aged 2-11 months,83.8%(95%CI:66.4%-92.2%)for 12-23 months,and 79.6%(95%CI:68.9%-86.6%)in those aged 24-35 months,while vaccine effectiveness was 31.2%(95%CI:−251.8%-86.5%)in children aged 36-59 months.Conclusions:A three-dose regimen of pentavalent rotavirus vaccine provides significant effectiveness against rotavirus gastroenteritis for children under 36 months of age during epidemic seasons,thereby alleviating the burden on public health systems.展开更多
Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyc...Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.展开更多
To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional...To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks,and the synchronous traditional FL will also reduce the training efficiency.To address these issues,in this paper,we propose a Directed Acyclic Graph(DAG)blockchain-enabled generalized Federated Dropout(FD)learning strategy,which could improve the efficiency of FL while ensuring the model generalization.Specifically,the DAG maintained by multiple edge servers will guarantee the security and traceability of the data,and the Reputation-based Tips Selection Algorithm(RTSA)is proposed to reduce the blockchain consensus delay.Second,the semi-asynchronous training among Intelligent Devices(IDs)is adopted to improve the training efficiency,and a reputation-based FD technology is proposed to prevent overfitting of the model.In addition,a Hybrid Optimal Resource Allocation(HORA)algorithm is introduced to minimize the network delay.Finally,simulation results demonstrate the effectiveness and superiority of the proposed algorithms.展开更多
Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,suc...This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,such as decoders,pose significant challenges due to their interlayer dependencies and high computational demands,especially under edge resource constraints.To address these challenges,we propose a two-phase optimization algorithm that first handles dependencyaware task allocation and subsequently optimizes energy consumption.By modeling the inference process using directed acyclic graphs(DAGs)and applying constraint relaxation techniques,our approach effectively reduces execution latency and energy usage.Experimental results demonstrate that our method achieves a reduction of up to 20%in task completion time and approximately 30%savings in energy consumption compared to traditional methods.These outcomes underscore our solution’s robustness in managing complex sequential dependencies and dynamic MEC conditions,enhancing quality of service.Thus,our work presents a practical and efficient resource optimization strategy for deploying models in resourceconstrained MEC scenarios.展开更多
Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for co...Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination Belief Rule Base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.展开更多
In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
Based on the key function of version management in PDM system, this paper discusses the function and the realization of version management and the transitions of version states with a workflow. A directed aeyclic grap...Based on the key function of version management in PDM system, this paper discusses the function and the realization of version management and the transitions of version states with a workflow. A directed aeyclic graph is used to describe a version model. Three storage modes of the directed acyelic graph version model in the database, the bumping block and the PDM working memory are presented and the conversion principle of these three modes is given. The study indicates that building a dynamic product structure configuration model based on versions is the key to resolve the problem. Thus a version model of single product object is built. Then the version management model in product structure configuration is built and the application of version management of PDM syster is presented as a case.展开更多
In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor ...In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.展开更多
A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm incl...A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm includes dynamic-group, detachgraph and front-sink components. The priority rules used are new. Relationship number, potentiality, weight and merge degree are defined for cluster＇s priority, and task potentiality for tasks＇ priority. Experiments show the algorithm could get good result in short time. The algorithm produces another optimal solution for the classic MJD benchmark. Its average performance is better than five latter-day representative algorithms, especially six benchmarks of the nines.展开更多
In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority ea...In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority earliest-finish-time(HPEFT)are proposed.The main idea hidden behind these algorithms is to adopt task depth,combined with task out-degree for the accurate analysis of task prioritization and precise processor allocation to achieve time optimization.Each algorithm is divided into three stages:task levelization,task prioritization,and processor allocation.In task levelization,the workflow is divided into several independent task sets on the basis of task depth.In task prioritization,the heterogeneous priority ranking value(HPRV)of the task is calculated using task out-degree,and a non-increasing ranking queue is generated on the basis of HPRV.In processor allocation,the sorted tasks are assigned one by one to the processor to minimize makespan and complete the task-processor mapping.Simulation experiments through practical applications and stochastic workflows confirm that the three algorithms can effectively shorten the workflow makespan,and the LOEFT algorithm performs the best,and it can be concluded that task depth combined with out-degree is an effective means of reducing completion time.展开更多
In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no...In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application.To this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure probability.The results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load conditions.In addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
To provide diversified services in the intelligent transportation systems,smart vehicles will generate unprecedented amounts of data every day.Due to data security and user privacy issues,Federated Learning(FL)is cons...To provide diversified services in the intelligent transportation systems,smart vehicles will generate unprecedented amounts of data every day.Due to data security and user privacy issues,Federated Learning(FL)is considered a potential solution to ensure privacy-preserving in data sharing.However,there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles(Io V),such as unreliable communications and malicious attacks.In this paper,we propose a Directed Acyclic Graph(DAG)based Swarm Learning(DSL),which integrates edge computing,FL,and blockchain technologies to provide secure data sharing and model training in Io Vs.To deal with the high mobility of vehicles,the dynamic vehicle association algorithm is introduced,which could optimize the connections between vehicles and road side units to improve the training efficiency.Moreover,to enhance the anti-attack property of the DSL algorithm,a malicious attack detection method is adopted,which could recognize malicious vehicles by the site confirmation rate.Furthermore,an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors.Finally,simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy,convergence rates and security compared with existing algorithms.展开更多
Two problems for task schedules in a multiprocessor parallel system are discussed in Ans paper (1) given a partially ordered set of tasks represented by the venices of an acyclic directed graph with their correspondin...Two problems for task schedules in a multiprocessor parallel system are discussed in Ans paper (1) given a partially ordered set of tasks represented by the venices of an acyclic directed graph with their corresponding processing bines, derive the lower bound on the Annimum time(LBMT) needed to process the task graph for a given number of processors. (2) Determine the lower bound on minimum number of processors(LBMP) needed to complete those tasks in minimum bine. It is shown that the proposed LBMT is sharper than previously Known values and the comPUtational aspeCts of these bounds are also discussed.展开更多
In low earth orbit（LEO） and medium earth orbit（MEO） satellite networks, the network topology changes rapidly because of the high relative speed movement of satellites. When some inter-satellite links （ISLs） fail...In low earth orbit（LEO） and medium earth orbit（MEO） satellite networks, the network topology changes rapidly because of the high relative speed movement of satellites. When some inter-satellite links （ISLs） fail, they can not be repaired in a short time. In order to increase the robustness for LEO/MEO satel- lite networks, an effective dynamic routing algorithm is proposed. All the routes to a certain node are found by constructing a destination oriented acyclic directed graph（DOADG） with the node as the destination. In this algorithm, multiple routes are provided, loop-free is guaranteed, and as long as the DOADG maintains, it is not necessary to reroute even if some ISLs fail. Simulation results show that comparing to the conventional routing algorithms, it is more efficient and reliable, costs less transmission overhead and converges faster.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61201310)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201160)the China Postdoctoral Science Foundation(Grant No.20110491067)
文摘Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes＇ sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes＇ sequence in the graph. Jeffries-Matusita distance （JMD） is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method.
基金supported by the National Key R&D Program of China(2020YFB1807801,2020YFB1807800)in part by Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education(cqupt-mct-202003)+2 种基金in part by Key Lab of Information Network Security,Ministry of Public Security under Grant C19603in part by National Natural Science Foundation of China(Grant No.61901067 and 61901013)in part by Chongqing Municipal Natural Science Foundation(Grant No.cstc2020jcyj-msxmX0339).
文摘Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.
文摘The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.
文摘In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.
基金supported by the Longhua District Medical Research Grant(Grant Nos:2021097,2021132,and 2023026)the Shenzhen Longhua District Science and Innovation Program(No.LHKJCXJCYJ202004)+2 种基金the Shenzhen San-Ming Project(No.SZSM201809085)the Medical Science and Technology Research Foundation of Guangdong Province(A2023248)the Shenzhen Science and Technology Innovation Program(No.JCYJ20210324123005014).
文摘Background:China's Notifiable Disease Reporting System comprehensively collects data on pediatric acute gas-troenteritis cases from all levels of healthcare.This study evaluated the effectiveness of the pentavalent rotavirus vaccine against rotavirus gastroenteritis in children under five years of age during seasonal epidemic periods.Methods:Using a test-negative design,we analyzed laboratory data from diarrhea patients aged 2-59 months who sought medical care in Longhua District,Shenzhen,Guangdong Province,China between January 2020 and March 2024.Vaccination history and potential confounders,including sex,household registration status,prema-turity,and birth weight,were collected retrospectively.Directed acyclic graphs were used to identify adjustment variables.Vaccine effectiveness was calculated as(1−adjusted odds ratio)×100%using unconditional logistic regression,comparing rotavirus-positive cases with rotavirus-negative controls.Results:The incidence of rotavirus gastroenteritis peaked and then progressively declined from January 2020 through March 2023,with no seasonal resurgence observed in the first quarter of 2023.Analysis of 928 epidemic-season cases(674 RV+;254 RV-)revealed an overall pentavalent rotavirus(RV5)vaccine effectiveness of 79.6%(95%CI:68.9%-86.6%).Dose-specific vaccine effectiveness varied substantially:1 dose vaccine effectiveness registered at 26.7%(95%CI:−162.1%-79.5%),rising to 76.6%(95%CI:46.2%-89.8%)for 2 doses,and reaching 82.7%(95%CI:72.2%-89.2%)for 3 doses.Age-stratified analysis showed vaccine effectiveness of 79.5%(95%CI:56.8%-90.3%)in children aged 2-11 months,83.8%(95%CI:66.4%-92.2%)for 12-23 months,and 79.6%(95%CI:68.9%-86.6%)in those aged 24-35 months,while vaccine effectiveness was 31.2%(95%CI:−251.8%-86.5%)in children aged 36-59 months.Conclusions:A three-dose regimen of pentavalent rotavirus vaccine provides significant effectiveness against rotavirus gastroenteritis for children under 36 months of age during epidemic seasons,thereby alleviating the burden on public health systems.
基金supported by the National Natural Science Foundation of China(61571063,61202399,61171051)
文摘Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.
基金supported in part by the National Key R&D Program of China under Grant 2021YFB1714100in part by the National Natural Science Foundation of China(NSFC)under Grant 62371082 and 62001076in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-MSX0726 and cstc2020jcyjmsxmX0878.
文摘To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks,and the synchronous traditional FL will also reduce the training efficiency.To address these issues,in this paper,we propose a Directed Acyclic Graph(DAG)blockchain-enabled generalized Federated Dropout(FD)learning strategy,which could improve the efficiency of FL while ensuring the model generalization.Specifically,the DAG maintained by multiple edge servers will guarantee the security and traceability of the data,and the Reputation-based Tips Selection Algorithm(RTSA)is proposed to reduce the blockchain consensus delay.Second,the semi-asynchronous training among Intelligent Devices(IDs)is adopted to improve the training efficiency,and a reputation-based FD technology is proposed to prevent overfitting of the model.In addition,a Hybrid Optimal Resource Allocation(HORA)algorithm is introduced to minimize the network delay.Finally,simulation results demonstrate the effectiveness and superiority of the proposed algorithms.
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
文摘This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,such as decoders,pose significant challenges due to their interlayer dependencies and high computational demands,especially under edge resource constraints.To address these challenges,we propose a two-phase optimization algorithm that first handles dependencyaware task allocation and subsequently optimizes energy consumption.By modeling the inference process using directed acyclic graphs(DAGs)and applying constraint relaxation techniques,our approach effectively reduces execution latency and energy usage.Experimental results demonstrate that our method achieves a reduction of up to 20%in task completion time and approximately 30%savings in energy consumption compared to traditional methods.These outcomes underscore our solution’s robustness in managing complex sequential dependencies and dynamic MEC conditions,enhancing quality of service.Thus,our work presents a practical and efficient resource optimization strategy for deploying models in resourceconstrained MEC scenarios.
基金supported by the Natural Science Foundation of China(Nos.61773388,61751304,61833016,61702142,U1811264 and 61966009)the Shaanxi Outstanding Youth Science Foundation,China(No.2020JC-34)+2 种基金the Key Research and Development Plan of Hainan,China(No.ZDYF2019007)China Postdoctoral Science Foundation(No.2020M673668)Guangxi Key Laboratory of Trusted Software,China(No.KX202050)。
文摘Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination Belief Rule Base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
基金the Scientific Technology Development Project of Heilongjiang(Grant No.WH05A01 and GB05A103)Scientific Technology Development Project of Harbin
文摘Based on the key function of version management in PDM system, this paper discusses the function and the realization of version management and the transitions of version states with a workflow. A directed aeyclic graph is used to describe a version model. Three storage modes of the directed acyelic graph version model in the database, the bumping block and the PDM working memory are presented and the conversion principle of these three modes is given. The study indicates that building a dynamic product structure configuration model based on versions is the key to resolve the problem. Thus a version model of single product object is built. Then the version management model in product structure configuration is built and the application of version management of PDM syster is presented as a case.
文摘In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.
基金Supported by the National Natural Science Foundation of China (7047107)the Ph.D. Programs Foundation of Ministry of Education of China (20020487046)
文摘A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm includes dynamic-group, detachgraph and front-sink components. The priority rules used are new. Relationship number, potentiality, weight and merge degree are defined for cluster＇s priority, and task potentiality for tasks＇ priority. Experiments show the algorithm could get good result in short time. The algorithm produces another optimal solution for the classic MJD benchmark. Its average performance is better than five latter-day representative algorithms, especially six benchmarks of the nines.
基金The Natural Science Foundation of Hunan Province(No.2018JJ2153)the Scientific Research Fund of Hunan Provincial Education Department(No.18B356)+1 种基金the Foundation of the Research Center of Hunan Emergency Communication Engineering Technology(No.2018TP2022)the Innovation Foundation for Postgraduate of the Hunan Institute of Science and Technology(No.YCX2018A06).
文摘In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority earliest-finish-time(HPEFT)are proposed.The main idea hidden behind these algorithms is to adopt task depth,combined with task out-degree for the accurate analysis of task prioritization and precise processor allocation to achieve time optimization.Each algorithm is divided into three stages:task levelization,task prioritization,and processor allocation.In task levelization,the workflow is divided into several independent task sets on the basis of task depth.In task prioritization,the heterogeneous priority ranking value(HPRV)of the task is calculated using task out-degree,and a non-increasing ranking queue is generated on the basis of HPRV.In processor allocation,the sorted tasks are assigned one by one to the processor to minimize makespan and complete the task-processor mapping.Simulation experiments through practical applications and stochastic workflows confirm that the three algorithms can effectively shorten the workflow makespan,and the LOEFT algorithm performs the best,and it can be concluded that task depth combined with out-degree is an effective means of reducing completion time.
基金the National Natural Science Foundation of China under Grant 61701059,Grant 61941114,and Grant 61831002,in part by the Fundamental Research Funds for the Central Universities of New TeachersProject,in part by the Chongqing Technological Innovation and Application Development Projects under Grant cstc2019jscx-msxm1322,and in part by the Eighteentg Open Foundation of State Key Lab of Integrated Services Networks of Xidian University under Grant ISN20-05.
文摘In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application.To this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure probability.The results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load conditions.In addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62371082,61831002,and 62001076in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-MSX0726 and cstc2020jcyj-msxmX0878。
文摘To provide diversified services in the intelligent transportation systems,smart vehicles will generate unprecedented amounts of data every day.Due to data security and user privacy issues,Federated Learning(FL)is considered a potential solution to ensure privacy-preserving in data sharing.However,there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles(Io V),such as unreliable communications and malicious attacks.In this paper,we propose a Directed Acyclic Graph(DAG)based Swarm Learning(DSL),which integrates edge computing,FL,and blockchain technologies to provide secure data sharing and model training in Io Vs.To deal with the high mobility of vehicles,the dynamic vehicle association algorithm is introduced,which could optimize the connections between vehicles and road side units to improve the training efficiency.Moreover,to enhance the anti-attack property of the DSL algorithm,a malicious attack detection method is adopted,which could recognize malicious vehicles by the site confirmation rate.Furthermore,an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors.Finally,simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy,convergence rates and security compared with existing algorithms.
文摘Two problems for task schedules in a multiprocessor parallel system are discussed in Ans paper (1) given a partially ordered set of tasks represented by the venices of an acyclic directed graph with their corresponding processing bines, derive the lower bound on the Annimum time(LBMT) needed to process the task graph for a given number of processors. (2) Determine the lower bound on minimum number of processors(LBMP) needed to complete those tasks in minimum bine. It is shown that the proposed LBMT is sharper than previously Known values and the comPUtational aspeCts of these bounds are also discussed.
基金the National Natural Science Foundation of Tianjin(07JCYBTC14800)
文摘In low earth orbit（LEO） and medium earth orbit（MEO） satellite networks, the network topology changes rapidly because of the high relative speed movement of satellites. When some inter-satellite links （ISLs） fail, they can not be repaired in a short time. In order to increase the robustness for LEO/MEO satel- lite networks, an effective dynamic routing algorithm is proposed. All the routes to a certain node are found by constructing a destination oriented acyclic directed graph（DOADG） with the node as the destination. In this algorithm, multiple routes are provided, loop-free is guaranteed, and as long as the DOADG maintains, it is not necessary to reroute even if some ISLs fail. Simulation results show that comparing to the conventional routing algorithms, it is more efficient and reliable, costs less transmission overhead and converges faster.
