In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance pr...In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.展开更多
Computational tools on top of first principle calculations have played an indispensable role in revealing the molecular details,thermodynamics,and kinetics in catalytic reactions.Here we proposed a highly efficient dy...Computational tools on top of first principle calculations have played an indispensable role in revealing the molecular details,thermodynamics,and kinetics in catalytic reactions.Here we proposed a highly efficient dynamic strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of efficient potential energy surface(PES)and MD simulations.Taking CO adsorbate on Ru(0001)surface as the illustrative model system,we demonstrated the PES-based MD can efficiently generate reliable two-dimensional potential-of-mean-force(PMF)surfaces in a wide range of temperatures,and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface.Moreover,MD offers an effective way to describe the surface kinetics such as adsorbate on-surface movement,which goes beyond the most popular static approach based on free energy barrier and transition state theory(TST).We further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST.It is expected that this accurate yet efficient dynamic strategy can be powerful in understanding mechanisms and reactivity of a catalytic surface system,and further guides the rational design of heterogeneous catalysts.展开更多
In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing ...In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing from the traditional matrix encoding strategy,DMES dynamically chose the size of each message group in a given set of adoptable message sizes.The appearance possibilities of all adoptable sizes were set in accordance with the desired embedding performance(embedding rate or bit-change rate).Accordingly,a searching algorithm that could provide an optimal combination of appearance possibilities was proposed.Furthermore,the roulette wheel algorithm was employed to determine the size of each message group according to the optimal combination of appearance possibilities.The effectiveness of DMES was evaluated in StegVoIP,which is a typical covert communication system based on VoIP.The experimental results demonstrate that DMES can adjust embedding capacity and embedding transparency effectively and flexibly,and achieve the desired embedding performance in any case.For the desired embedding rate,the average errors are not more than 0.000 8,and the standard deviations are not more than 0.002 0;for the desired bit-change rate,the average errors are not more than 0.001 4,and the standard deviations are not more than 0.002 6.展开更多
Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential te...Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.展开更多
Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the p...Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.展开更多
Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the...Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.展开更多
This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.展开更多
Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles(AUVs) to collaborate with each other.In this work,a dynamic formation model was proposed,in which...Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles(AUVs) to collaborate with each other.In this work,a dynamic formation model was proposed,in which several algorithms were developed for the complex underwater environment.Dimension changeable particle swarm algorithm was used to find an optimized path by dynamically adjusting the number and the distribution of the path nodes.Position relationship based obstacle avoidance algorithm was designed to detour along the edges of obstacles.Virtual potential point based formation-keeping algorithm was employed by incorporating dynamic strategies which were decided by the current states of the formation.The virtual potential point was used to keep the formation structure when the AUV or the formation was deviated.Simulation results show that an optimal path can be dynamically planned with fewer path nodes and smaller fitness,even with a concave obstacle.It has been also proven that different formation-keeping strategies can be adaptively selected and the formation can change its structure in a narrow area and restore back after passing the obstacle.展开更多
The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one o...The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one of the representative solutions,but it still has room for improvement in terms of routing stability.In this paper,we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules(MTCR-CR).The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for intersatellite link(ISL)construction solutions that meet the minimum number of topology changes to avoid route oscillations.The simulation results in Beidou-3 show that compared with DT-DVTR,MTCR-CR reduces the number of routing changes by about 92%,the number of path changes caused by routing changes is about38%,and the rerouting time is reduced by approximately 47%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.展开更多
Selective Catalyst Reduction（SCR）Urea Dosing System（UDS）directly affects the system accuracy and the dynamic response performance of a vehicle.However,the UDS dynamic response is hard to keep up with the changes o...Selective Catalyst Reduction（SCR）Urea Dosing System（UDS）directly affects the system accuracy and the dynamic response performance of a vehicle.However,the UDS dynamic response is hard to keep up with the changes of the engine＇s operating conditions.That will lead to low NO_χconversion efficiency or NH_3 slip.In order to optimize the injection accuracy and the response speed of the UDS in dynamic conditions,an advanced control strategy based on an air-assisted volumetric UDS is presented.It covers the methods of flow compensation and switching working conditions.The strategy is authenticated on an UDS and tested in different dynamic conditions.The result shows that the control strategy discussed results in higher dynamic accuracy and faster dynamic response speed of UDS.The inject deviation range is improved from being between-8%and 10%to-4%and 2%and became more stable than before,and the dynamic response time was shortened from 200 ms to 150 ms.The ETC cycle result shows that after using the new strategy the NH_3 emission is reduced by 60%,and the NO_χemission remains almost unchanged.The trade-off between NO_χconversion efficiency and NH_3 slip is mitigated.The studied flow compensation and switching working conditions can improve the dynamic performance of the UDS significantly and make the UDS dynamic response keep up with the changes of the engine＇s operating conditions quickly.展开更多
Increasing urbanization in the cities of northern Mexico reflects a general trend to increased temperatures, so it is likely that heat waves amplify the frequency and intensity in urban centers, mainly located in arid...Increasing urbanization in the cities of northern Mexico reflects a general trend to increased temperatures, so it is likely that heat waves amplify the frequency and intensity in urban centers, mainly located in arid and semiarid as Mexicali city with extremely arid climate, very hot in summer and cold and rainy in winter. Mexicali, Baja California, Mexico is located at N32°38＇ and W115°20＇. The urban area is expanded over 14,890 hectares, with a population rise the 689,775. In the last four decades has experienced an accelerated industrial growth and mismatched land uses, for example： most of the industrial parks were established before the 1980 in what was the outskirts of the city, but nowadays practically are inside of the urban area contributing to the increase the urban temperature. The heat islands profile shows that are intensified in industrial areas as well as trade and services. The preliminary scenarios of climate change for Mexicali indicate that for the decade of 2080 the temperature will increase between 4.2℃ and 4.4℃. This paper addresses in a simulation context, an industrial and commercial city sector and their ability to implement urban heat island mitigation strategies. The simulation of this process requires several spatial analysis tools and specific knowledge about the processes that increase urban temperatures. In this work, only land use, land cover and buildings are considered. The proposed method takes into account the actual spatial organization to analyze trends for the proposed growth areas.展开更多
Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, can...Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.展开更多
This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give...This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give the definition of safe-region for investment. Moreover, in order to obtain the target wealth as quickly as possible, using Bellman dynamic programming principle, we get the optimal investment strategy and corresponding necessary expected time. At last we give some numerical computations for a set of different parameters.展开更多
Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-toler...Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.展开更多
Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties...Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction,a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed.First,Pearson correlation coefficients and mutual information are used to select representative features,reducing the impact of redundant features onmodel performance.Then,the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,thereby obtaining feature representations rich in temporal information.Subsequently,a multi-layer cascade structure is developed,progressively integrating prediction results from base learners such as LightGBM,XGBoost,Random Forest(RF),and Extreme Random Forest(ERF)to enhance model performance.Finally,an XGBoost-based meta-learner is utilized to integrate the outputs of the base learners and generate the final prediction results.The entire cascading process adopts a dynamic expansion strategy,where the decision to add new cascade layers is based on the R2 performance criterion.Experimental results demonstrate that the proposed model achieves high prediction accuracy and robustness under various weather conditions,showing significant improvements over traditional models and providing an effective solution for short-term PV power forecasting.展开更多
Dear Editor,This letter is concerned with the evolutionary dynamics of cooperative strategies in social dilemma situations.Stochastic punishment has been proposed,in which whether an individual acts as a punisher or n...Dear Editor,This letter is concerned with the evolutionary dynamics of cooperative strategies in social dilemma situations.Stochastic punishment has been proposed,in which whether an individual acts as a punisher or not is stochastic and depends on its preference for punishment.Meanwhile,both the cost of punishment and whether a defector would be punished are also stochastic.In previous models,the cost of punishment is considered to be either constant or proportional to the number of individuals to be punished.Furthermore,the hypothesis that all defectors should be penalized is frequently adopted.Actually,some defectors may refrain from being punished due to the presence of noise or the limitation of the punishment cost,and the cost of punishment is also dependent on the number of punishers.Thus,we establish an analytic model of stochastic punishment for infinite and well-mixed populations,investigate the effects of stochastic punishment on the evolution of cooperation,and analyze how to support the evolution of cooperation effectively when the stochastic punishment is possible.The objective of this letter is to design a cooperation-promoting stochastic punishment that will allow the system to evolve to full cooperation.The replicator equations have been used to explore the evolutionary dynamics of cooperation under stochastic punishment,and the conditions under which cooperation is favored by natural selection have been specified.展开更多
This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is t...This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is to enable all follower agents to achieve consensus tracking for the leader agent.Firstly,this article introduces an extended state observer for estimating each follower agent's unknown state and external disturbance.Subsequently,on the basis of the above-extended state observer and a dynamic event-triggered strategy,a distributed consensus tracking control protocol with disturbances restraint is developed,which can reduce the MAS's update frequency on the premise of ensuring the control protocol's effectiveness.Furthermore,the MAS's stability and the absence of Zeno behavior are analyzed and proved by the established Lyapunov functional and linear matrix inequality theory.Finally,the validity and feasibility of the proposed approach are validated through a group of comparative numerical simulation experiments.展开更多
In this paper a comprehensive introduction for modeling and control of networked evolutionary games （NEGs） via semi-tensor product （STP） approach is presented. First, we review the mathematical model of an NEG, wh...In this paper a comprehensive introduction for modeling and control of networked evolutionary games （NEGs） via semi-tensor product （STP） approach is presented. First, we review the mathematical model of an NEG, which consists of three ingredients： network graph, fundamental network game, and strategy updating rule. Three kinds of network graphs are considered, which are i） undirected graph for symmetric games; ii） directed graph for asymmetric games, and iii） d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games （FEGs） are discussed, which are i） two strategies and symmetric （S-2）; ii） two strategies and asymmetric （A-2）; and iii） three strategies and symmetric （S-3）. Three strategy updating rules （SUR） are introduced, which are i） Unconditional Imitation （UI）; ii） Fermi Rule（FR）; iii） Myopic Best Response Adjustment Rule （MBRA）. First, we review the fundamental evolutionary equation （FEE） and use it to construct network profile dynamics （NPD）of NEGs. To show how the dynamics of an NEG can be modeled as a discrete time dynamics within an algebraic state space, the fundamental evolutionary equation （FEE） of each player is discussed. Using FEEs, the network strategy profile dynamics （NSPD） is built by providing efficient algorithms. Finally, we consider three more complicated NEGs： i） NEG with different length historical information, ii） NEG with multi-species, and iii） NEG with time-varying payoffs. In all the cases, formulas are provided to construct the corresponding NSPDs. Using these NSPDs, certain properties are explored. Examples are presented to demonstrate the model constructing method, analysis and control design technique, and to reveal certain dynamic behaviors of NEGs.展开更多
As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centra...As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of entities.In this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power plants.We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory.This model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining problem.The distributed alternating direction multiplier method(ADMM)is employed to address the subproblems separately.Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating entity.The results confirm the practicality and effectiveness of the strategy.展开更多
In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refine...In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refinement action.A force Jacobian matrix is calibrated with only one demonstration,based on which stable and safe expert action can be generated.The deep deterministic policy gradients(DDPG)method is employed to learn the refinement action,which aims to improve the assembly efficiency.Second,an episode-step exploration strategy is developed,which uses the expert action as a benchmark and adjusts the exploration intensity dynamically.A safety-efficiency reward function is designed for the compliant insertion.Third,to improve the adaptability with different components,a skill saving and selection mechanism is proposed.Several typical components are used to train the skill models.And the trained models and force Jacobian matrices are saved in a skill pool.Given a new component,the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks.Fourth,a simulation environment is established under the guidance of the force Jacobian matrix,which avoids tedious training process on real robotic systems.Simulation and experiments are conducted to validate the effectiveness of the proposed methods.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61773142,62303136)China Postdoctoral Science Foundation(No.2023M740912)Postdoctoral Fellowship Program of CPSF,China(No.GZC20233447).
文摘In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.
基金financially supported by Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China(No.2021ZR109)the National Natural Science Foundation of China(Nos.21973094,22173104,22173105)the Opening Project of PCOSS of Xiamen University(No.201908)。
文摘Computational tools on top of first principle calculations have played an indispensable role in revealing the molecular details,thermodynamics,and kinetics in catalytic reactions.Here we proposed a highly efficient dynamic strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of efficient potential energy surface(PES)and MD simulations.Taking CO adsorbate on Ru(0001)surface as the illustrative model system,we demonstrated the PES-based MD can efficiently generate reliable two-dimensional potential-of-mean-force(PMF)surfaces in a wide range of temperatures,and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface.Moreover,MD offers an effective way to describe the surface kinetics such as adsorbate on-surface movement,which goes beyond the most popular static approach based on free energy barrier and transition state theory(TST).We further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST.It is expected that this accurate yet efficient dynamic strategy can be powerful in understanding mechanisms and reactivity of a catalytic surface system,and further guides the rational design of heterogeneous catalysts.
基金Project(2009AA01A402) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0650) supported by Program for New Century Excellent Talents in University Project(IRT-0725) supported by Program for Changjiang Scholars and Innovative Research Team in Chinese University
文摘In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing from the traditional matrix encoding strategy,DMES dynamically chose the size of each message group in a given set of adoptable message sizes.The appearance possibilities of all adoptable sizes were set in accordance with the desired embedding performance(embedding rate or bit-change rate).Accordingly,a searching algorithm that could provide an optimal combination of appearance possibilities was proposed.Furthermore,the roulette wheel algorithm was employed to determine the size of each message group according to the optimal combination of appearance possibilities.The effectiveness of DMES was evaluated in StegVoIP,which is a typical covert communication system based on VoIP.The experimental results demonstrate that DMES can adjust embedding capacity and embedding transparency effectively and flexibly,and achieve the desired embedding performance in any case.For the desired embedding rate,the average errors are not more than 0.000 8,and the standard deviations are not more than 0.002 0;for the desired bit-change rate,the average errors are not more than 0.001 4,and the standard deviations are not more than 0.002 6.
基金supported by the National Natural Science Foundation of China (51175502)
文摘Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51575528)the Science Foundation of China University of Petroleum,Beijing(No.2462022QEDX011).
文摘Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.
基金National Natural Science Foundation of China(No.61105114)the Key Technology R&D Program of Jiangsu Province,China(No.BE2010189)
文摘Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
基金Project(NS2013091)supported by the Basis Research Fund of Nanjing University of Aeronautics and Astronautics,China
文摘Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles(AUVs) to collaborate with each other.In this work,a dynamic formation model was proposed,in which several algorithms were developed for the complex underwater environment.Dimension changeable particle swarm algorithm was used to find an optimized path by dynamically adjusting the number and the distribution of the path nodes.Position relationship based obstacle avoidance algorithm was designed to detour along the edges of obstacles.Virtual potential point based formation-keeping algorithm was employed by incorporating dynamic strategies which were decided by the current states of the formation.The virtual potential point was used to keep the formation structure when the AUV or the formation was deviated.Simulation results show that an optimal path can be dynamically planned with fewer path nodes and smaller fitness,even with a concave obstacle.It has been also proven that different formation-keeping strategies can be adaptively selected and the formation can change its structure in a narrow area and restore back after passing the obstacle.
基金supported by the National Key Research and Development Program of China(No.2020YFB1806000)。
文摘The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one of the representative solutions,but it still has room for improvement in terms of routing stability.In this paper,we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules(MTCR-CR).The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for intersatellite link(ISL)construction solutions that meet the minimum number of topology changes to avoid route oscillations.The simulation results in Beidou-3 show that compared with DT-DVTR,MTCR-CR reduces the number of routing changes by about 92%,the number of path changes caused by routing changes is about38%,and the rerouting time is reduced by approximately 47%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2012AA111708)
文摘Selective Catalyst Reduction（SCR）Urea Dosing System（UDS）directly affects the system accuracy and the dynamic response performance of a vehicle.However,the UDS dynamic response is hard to keep up with the changes of the engine＇s operating conditions.That will lead to low NO_χconversion efficiency or NH_3 slip.In order to optimize the injection accuracy and the response speed of the UDS in dynamic conditions,an advanced control strategy based on an air-assisted volumetric UDS is presented.It covers the methods of flow compensation and switching working conditions.The strategy is authenticated on an UDS and tested in different dynamic conditions.The result shows that the control strategy discussed results in higher dynamic accuracy and faster dynamic response speed of UDS.The inject deviation range is improved from being between-8%and 10%to-4%and 2%and became more stable than before,and the dynamic response time was shortened from 200 ms to 150 ms.The ETC cycle result shows that after using the new strategy the NH_3 emission is reduced by 60%,and the NO_χemission remains almost unchanged.The trade-off between NO_χconversion efficiency and NH_3 slip is mitigated.The studied flow compensation and switching working conditions can improve the dynamic performance of the UDS significantly and make the UDS dynamic response keep up with the changes of the engine＇s operating conditions quickly.
文摘Increasing urbanization in the cities of northern Mexico reflects a general trend to increased temperatures, so it is likely that heat waves amplify the frequency and intensity in urban centers, mainly located in arid and semiarid as Mexicali city with extremely arid climate, very hot in summer and cold and rainy in winter. Mexicali, Baja California, Mexico is located at N32°38＇ and W115°20＇. The urban area is expanded over 14,890 hectares, with a population rise the 689,775. In the last four decades has experienced an accelerated industrial growth and mismatched land uses, for example： most of the industrial parks were established before the 1980 in what was the outskirts of the city, but nowadays practically are inside of the urban area contributing to the increase the urban temperature. The heat islands profile shows that are intensified in industrial areas as well as trade and services. The preliminary scenarios of climate change for Mexicali indicate that for the decade of 2080 the temperature will increase between 4.2℃ and 4.4℃. This paper addresses in a simulation context, an industrial and commercial city sector and their ability to implement urban heat island mitigation strategies. The simulation of this process requires several spatial analysis tools and specific knowledge about the processes that increase urban temperatures. In this work, only land use, land cover and buildings are considered. The proposed method takes into account the actual spatial organization to analyze trends for the proposed growth areas.
文摘Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.
文摘This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give the definition of safe-region for investment. Moreover, in order to obtain the target wealth as quickly as possible, using Bellman dynamic programming principle, we get the optimal investment strategy and corresponding necessary expected time. At last we give some numerical computations for a set of different parameters.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.
基金2023 Sustainable Development Science and Technology Innovation Action Plan Project of Chongming District Science and Technology Committee,Shanghai(CKST2023-01)Shanghai Science and Technology Commission Funded Project(19DZ2254800).
文摘Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction,a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed.First,Pearson correlation coefficients and mutual information are used to select representative features,reducing the impact of redundant features onmodel performance.Then,the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,thereby obtaining feature representations rich in temporal information.Subsequently,a multi-layer cascade structure is developed,progressively integrating prediction results from base learners such as LightGBM,XGBoost,Random Forest(RF),and Extreme Random Forest(ERF)to enhance model performance.Finally,an XGBoost-based meta-learner is utilized to integrate the outputs of the base learners and generate the final prediction results.The entire cascading process adopts a dynamic expansion strategy,where the decision to add new cascade layers is based on the R2 performance criterion.Experimental results demonstrate that the proposed model achieves high prediction accuracy and robustness under various weather conditions,showing significant improvements over traditional models and providing an effective solution for short-term PV power forecasting.
基金supported by the National Natural Science Foundation of China(NSFC)(61903080)the Fun-damental Research Funds for the Central Universities(2232023D-26).
文摘Dear Editor,This letter is concerned with the evolutionary dynamics of cooperative strategies in social dilemma situations.Stochastic punishment has been proposed,in which whether an individual acts as a punisher or not is stochastic and depends on its preference for punishment.Meanwhile,both the cost of punishment and whether a defector would be punished are also stochastic.In previous models,the cost of punishment is considered to be either constant or proportional to the number of individuals to be punished.Furthermore,the hypothesis that all defectors should be penalized is frequently adopted.Actually,some defectors may refrain from being punished due to the presence of noise or the limitation of the punishment cost,and the cost of punishment is also dependent on the number of punishers.Thus,we establish an analytic model of stochastic punishment for infinite and well-mixed populations,investigate the effects of stochastic punishment on the evolution of cooperation,and analyze how to support the evolution of cooperation effectively when the stochastic punishment is possible.The objective of this letter is to design a cooperation-promoting stochastic punishment that will allow the system to evolve to full cooperation.The replicator equations have been used to explore the evolutionary dynamics of cooperation under stochastic punishment,and the conditions under which cooperation is favored by natural selection have been specified.
基金supported by Guangdong Major Project of Basic and Applied Basic Research(Grant No.2023B0303000016)the National Natural Science Foundation of China(Grant No.U21A20487)+5 种基金Shenzhen Technology Project(Grant Nos.JCYJ20220818101206014,JCYJ20220818101211025)the CAS Key Technology Talent Program,the National Outstanding Youth Talents Support Program(Grant No.61822304)Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)Shanghai Municipal Commission of Science and Technology Project(Grant No.19511132101)the Projects of Major International(Regional)Joint Research Program of NSFC(Grant No.61720106011)the National Natural Science Foundation of China(Grant No.62372440)。
文摘This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is to enable all follower agents to achieve consensus tracking for the leader agent.Firstly,this article introduces an extended state observer for estimating each follower agent's unknown state and external disturbance.Subsequently,on the basis of the above-extended state observer and a dynamic event-triggered strategy,a distributed consensus tracking control protocol with disturbances restraint is developed,which can reduce the MAS's update frequency on the premise of ensuring the control protocol's effectiveness.Furthermore,the MAS's stability and the absence of Zeno behavior are analyzed and proved by the established Lyapunov functional and linear matrix inequality theory.Finally,the validity and feasibility of the proposed approach are validated through a group of comparative numerical simulation experiments.
基金This work was partially supported by National Natural Science Foundation of China （Nos. 61273013, 61333001, 61104065, 61322307）.
文摘In this paper a comprehensive introduction for modeling and control of networked evolutionary games （NEGs） via semi-tensor product （STP） approach is presented. First, we review the mathematical model of an NEG, which consists of three ingredients： network graph, fundamental network game, and strategy updating rule. Three kinds of network graphs are considered, which are i） undirected graph for symmetric games; ii） directed graph for asymmetric games, and iii） d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games （FEGs） are discussed, which are i） two strategies and symmetric （S-2）; ii） two strategies and asymmetric （A-2）; and iii） three strategies and symmetric （S-3）. Three strategy updating rules （SUR） are introduced, which are i） Unconditional Imitation （UI）; ii） Fermi Rule（FR）; iii） Myopic Best Response Adjustment Rule （MBRA）. First, we review the fundamental evolutionary equation （FEE） and use it to construct network profile dynamics （NPD）of NEGs. To show how the dynamics of an NEG can be modeled as a discrete time dynamics within an algebraic state space, the fundamental evolutionary equation （FEE） of each player is discussed. Using FEEs, the network strategy profile dynamics （NSPD） is built by providing efficient algorithms. Finally, we consider three more complicated NEGs： i） NEG with different length historical information, ii） NEG with multi-species, and iii） NEG with time-varying payoffs. In all the cases, formulas are provided to construct the corresponding NSPDs. Using these NSPDs, certain properties are explored. Examples are presented to demonstrate the model constructing method, analysis and control design technique, and to reveal certain dynamic behaviors of NEGs.
基金supported by the National Natural Science Foundation of China“Game control-based planning and simulation modelling of coupled optical storage hydrogen production system”(No.52277211).
文摘As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of entities.In this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power plants.We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory.This model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining problem.The distributed alternating direction multiplier method(ADMM)is employed to address the subproblems separately.Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating entity.The results confirm the practicality and effectiveness of the strategy.
基金supported by National Key Research and Development Program of China(No.2018AAA0103005)National Natural Science Foundation of China(No.61873266)。
文摘In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refinement action.A force Jacobian matrix is calibrated with only one demonstration,based on which stable and safe expert action can be generated.The deep deterministic policy gradients(DDPG)method is employed to learn the refinement action,which aims to improve the assembly efficiency.Second,an episode-step exploration strategy is developed,which uses the expert action as a benchmark and adjusts the exploration intensity dynamically.A safety-efficiency reward function is designed for the compliant insertion.Third,to improve the adaptability with different components,a skill saving and selection mechanism is proposed.Several typical components are used to train the skill models.And the trained models and force Jacobian matrices are saved in a skill pool.Given a new component,the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks.Fourth,a simulation environment is established under the guidance of the force Jacobian matrix,which avoids tedious training process on real robotic systems.Simulation and experiments are conducted to validate the effectiveness of the proposed methods.
