The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of ...The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.展开更多
Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output fee...Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output feedback stabilization,and synchronization.However,controlling pattern dynamics in reaction-diffusion systems with fractional-order diffusion remains an unresolved problem.This paper presents a proportional-derivative(PD)control strategy for the Schnakenberg system with fractional-order diffusion and cross-diffusion.Theoretical analysis explores the amplitude equation near the Turing bifurcation threshold,determining the selection and stability of pattern formations.Numerical simulations demonstrate that the PD controller accomplishes the modification of pattern structures and suppression of Turing instability by adjusting only two control parameters.Additionally,it is found that for smaller fractional diffusion order,the region can accommodate more hexagonal and stripe patterns in space.This work contributes to the control of complex pattern dynamics and offers a new approach to enhancing stability in fractional reaction-diffusion systems.展开更多
In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponent...In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.展开更多
This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting est...This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.展开更多
Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,...Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperativ...To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperative tracking control method considering the effectiveness of passive detection. First, the system model of passive detection in UAV formation is constructed.Then, the Geometric Dilution of Precision(GDOP) of bearing-only sensor nodes pair on the observation plane is analyzed. Building on this foundation, the pairwise form is expanded to obtain the optimal geometric configuration for the entire network. Subsequently, the Distributed Cubature Information Filtering(DCIF) is integrated with the weighted average consensus protocol to design the distributed cooperative observer suitable for the system model, enabling state estimation of the target. Finally, within the distributed architecture, the Nonlinear Model Predictive Controller(NMPC) is designed. This controller autonomously assembles the UAV formation during the assembly phase and forms an optimal detection array. The UAV formation then tracks the target using the virtual geometric center based on the established rigid geometric configuration. The simulation experiments validate that the proposed model and method can enhance the passive detection effectiveness of the UAV formation, thereby achieving stable and efficient distributed cooperative tracking for the maneuvering target.展开更多
This study addresses the issue of spray icing on the air intake grilles of ship power systems in cold maritime environments.Through numerical simulation methods,the influence of environmental parameters on icing chara...This study addresses the issue of spray icing on the air intake grilles of ship power systems in cold maritime environments.Through numerical simulation methods,the influence of environmental parameters on icing characteristics is revealed,and an energy-efficient zoned electric heating anti-icing strategy is proposed.A threedimensional grille model is constructed to systematically analyze the effects of environmental temperature(from−20℃to−4℃),droplet diameter(from 50μm to 500μm),and liquid water content(from 0.5 g/m³to 8 g/m³)on icing rates and blockage of the flow channel.The results indicate that low temperature and high liquid water content significantly exacerbate icing.Under the condition of an environmental temperature of−20℃,droplet diameter of 500μm,and liquid water content of 8 g/m³,the flow channel blockage ratio reaches 30.95%within 10 min.Additionally,as droplet diameter increases,the droplet impingement and icing regions become more concentrated toward the leading edge of blades.To mitigate grille icing in cold environments,an electric heating film configuration is employed for thermal protection.Optimization of the heating strategy reveals that the zoned heating approach,compared to the initial uniform heating scheme,effectively homogenizes surface temperature distribution while reducing total power consumption by 37.47%.This study validates the engineering applicability of the zoned electric heating anti/de-icing strategy,providing theoretical and technical support for the design of anti-icing systems in ship power systems operating in cold maritime regions.展开更多
Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information impl...Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information implicit in AEFS changes.In this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS.First,a time series dataset is created in the time domain using AEFS attributes.The AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the FCM.Second,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus.Thus,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain.Finally,the rationality and reliability of the proposed method are verified by combining radar charts and expert experience.The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first time.The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.展开更多
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
As a vessel navigates at high speeds in waves,considerable pitching motion can result in the discomfort of passengers.In this study is proposed a ride control system consisting of dual T-foils to generate a larger rig...As a vessel navigates at high speeds in waves,considerable pitching motion can result in the discomfort of passengers.In this study is proposed a ride control system consisting of dual T-foils to generate a larger righting moment than a common single T-foil system.One T-foil is mounted at the bow,and the other at the stern.Accordingly,different control strategies for dual T-foils were proposed To verify the stratigies,a model experiment was conducted in the Towing Tank,Dalian Unievrsity of Technology.The optimal control signal was determined by comparing the pitch responses,heave responses,bow accelerations,and stern accelerations of a vessel in regular waves.In addition,the control strategy for the best motion-reduction effect was investigated.The optimized dual T-foil system provides a 34%reduction in pitch motion.展开更多
In order to enhance the dynamic control precision of inertial stabilization platform(ISP),a disturbance sliding mode observer(DSMO)is proposed in this paper suppressing disturbance torques inherent within the system.T...In order to enhance the dynamic control precision of inertial stabilization platform(ISP),a disturbance sliding mode observer(DSMO)is proposed in this paper suppressing disturbance torques inherent within the system.The control accuracy of ISP is fundamentally circumscribed by various disturbance torques in rotating shaft.Therefore,a dynamic model of ISP incorporating composite perturbations is established with regard to the stabilization of axis in the inertial reference frame.Subsequently,an online estimator for control loop uncertainties based on the sliding mode control algorithm is designed to estimate the aggregate disturbances of various parameters uncertainties and other unmodeled disturbances that cannot be accurately calibrated.Finally,the proposed DSMO is integrated into a classical proportional-integral-derivative(PID)control scheme,utilizing feedforward approach to compensate the composite disturbance in the control loop online.The effectiveness of the proposed disturbance observer is validated through simulation and hardware experimentation,demonstrating a significant improvement in the dynamic control performance and robustness of the classical PID controller extensively utilized in the field of engineering.展开更多
Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most o...Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.展开更多
In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between ...In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.展开更多
In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling e...Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.展开更多
In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hy...In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.展开更多
Wearable strain sensors are arousing increasing research interests in recent years on account of their potentials in motion detection,personal and public healthcare,future entertainment,man-machine interaction,artific...Wearable strain sensors are arousing increasing research interests in recent years on account of their potentials in motion detection,personal and public healthcare,future entertainment,man-machine interaction,artificial intelligence,and so forth.Much research has focused on fiber-based sensors due to the appealing performance of fibers,including processing flexibility,wearing comfortability,outstanding lifetime and serviceability,low-cost and large-scale capacity.Herein,we review the latest advances in functionalization and device fabrication of fiber materials toward applications in fiber-based wearable strain sensors.We describe the approaches for preparing conductive fibers such as spinning,surface modification,and structural transformation.We also introduce the fabrication and sensing mechanisms of state-of-the-art sensors and analyze their merits and demerits.The applications toward motion detection,healthcare,man-machine interaction,future entertainment,and multifunctional sensing are summarized with typical examples.We finally critically analyze tough challenges and future remarks of fiber-based strain sensors,aiming to implement them in real applications.展开更多
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute...The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.展开更多
基金funded by the Science and Technology Vice President Project in Changping District,Beijing(Project Name:Research on multi-scale optimization and intelligent control technology of integrated energy systemProject number:202302007013).
文摘The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.
基金supported by the National Natural Science Foundation of China(62073172)the Natural Science Foundation of Jiangsu Province of China(BK20221329)。
文摘Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output feedback stabilization,and synchronization.However,controlling pattern dynamics in reaction-diffusion systems with fractional-order diffusion remains an unresolved problem.This paper presents a proportional-derivative(PD)control strategy for the Schnakenberg system with fractional-order diffusion and cross-diffusion.Theoretical analysis explores the amplitude equation near the Turing bifurcation threshold,determining the selection and stability of pattern formations.Numerical simulations demonstrate that the PD controller accomplishes the modification of pattern structures and suppression of Turing instability by adjusting only two control parameters.Additionally,it is found that for smaller fractional diffusion order,the region can accommodate more hexagonal and stripe patterns in space.This work contributes to the control of complex pattern dynamics and offers a new approach to enhancing stability in fractional reaction-diffusion systems.
文摘In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.
基金Research Grants Council of the Hong Kong Special Administrative Region of China (No. CityU-11211818)the Self-Planned Task of State Key Laboratory of Robotics and Systems of Harbin Institute of Technology (No. SKLRS201801A03)the National Natural Science Foundation of China (No. 61873311).
文摘This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(62073341)in part by the Natural Science Fund for Distinguished Young Scholars of Hunan Province(2019JJ20026).
文摘Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
基金supported by the National Natural Science Foundation of China (Nos. 62176214 and 61973253)。
文摘To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperative tracking control method considering the effectiveness of passive detection. First, the system model of passive detection in UAV formation is constructed.Then, the Geometric Dilution of Precision(GDOP) of bearing-only sensor nodes pair on the observation plane is analyzed. Building on this foundation, the pairwise form is expanded to obtain the optimal geometric configuration for the entire network. Subsequently, the Distributed Cubature Information Filtering(DCIF) is integrated with the weighted average consensus protocol to design the distributed cooperative observer suitable for the system model, enabling state estimation of the target. Finally, within the distributed architecture, the Nonlinear Model Predictive Controller(NMPC) is designed. This controller autonomously assembles the UAV formation during the assembly phase and forms an optimal detection array. The UAV formation then tracks the target using the virtual geometric center based on the established rigid geometric configuration. The simulation experiments validate that the proposed model and method can enhance the passive detection effectiveness of the UAV formation, thereby achieving stable and efficient distributed cooperative tracking for the maneuvering target.
基金supported in part by the Ship Preliminary Research Project (No.3020401020102)。
文摘This study addresses the issue of spray icing on the air intake grilles of ship power systems in cold maritime environments.Through numerical simulation methods,the influence of environmental parameters on icing characteristics is revealed,and an energy-efficient zoned electric heating anti-icing strategy is proposed.A threedimensional grille model is constructed to systematically analyze the effects of environmental temperature(from−20℃to−4℃),droplet diameter(from 50μm to 500μm),and liquid water content(from 0.5 g/m³to 8 g/m³)on icing rates and blockage of the flow channel.The results indicate that low temperature and high liquid water content significantly exacerbate icing.Under the condition of an environmental temperature of−20℃,droplet diameter of 500μm,and liquid water content of 8 g/m³,the flow channel blockage ratio reaches 30.95%within 10 min.Additionally,as droplet diameter increases,the droplet impingement and icing regions become more concentrated toward the leading edge of blades.To mitigate grille icing in cold environments,an electric heating film configuration is employed for thermal protection.Optimization of the heating strategy reveals that the zoned heating approach,compared to the initial uniform heating scheme,effectively homogenizes surface temperature distribution while reducing total power consumption by 37.47%.This study validates the engineering applicability of the zoned electric heating anti/de-icing strategy,providing theoretical and technical support for the design of anti-icing systems in ship power systems operating in cold maritime regions.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information implicit in AEFS changes.In this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS.First,a time series dataset is created in the time domain using AEFS attributes.The AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the FCM.Second,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus.Thus,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain.Finally,the rationality and reliability of the proposed method are verified by combining radar charts and expert experience.The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first time.The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
基金supported by Shenzhen 2022 Key Project for Technological Research(Grant Number JSGG20220831110803006)key technology research and demonstration project of 10 MW deep-sea floating offshore wind turbine(DTGD-2023-10174)key technology research task of floating offshore combined wind and wave power generation and MIIT program for Floating VAWT.
文摘As a vessel navigates at high speeds in waves,considerable pitching motion can result in the discomfort of passengers.In this study is proposed a ride control system consisting of dual T-foils to generate a larger righting moment than a common single T-foil system.One T-foil is mounted at the bow,and the other at the stern.Accordingly,different control strategies for dual T-foils were proposed To verify the stratigies,a model experiment was conducted in the Towing Tank,Dalian Unievrsity of Technology.The optimal control signal was determined by comparing the pitch responses,heave responses,bow accelerations,and stern accelerations of a vessel in regular waves.In addition,the control strategy for the best motion-reduction effect was investigated.The optimized dual T-foil system provides a 34%reduction in pitch motion.
基金supported by the National Natural Science Foundation of China(61803015).
文摘In order to enhance the dynamic control precision of inertial stabilization platform(ISP),a disturbance sliding mode observer(DSMO)is proposed in this paper suppressing disturbance torques inherent within the system.The control accuracy of ISP is fundamentally circumscribed by various disturbance torques in rotating shaft.Therefore,a dynamic model of ISP incorporating composite perturbations is established with regard to the stabilization of axis in the inertial reference frame.Subsequently,an online estimator for control loop uncertainties based on the sliding mode control algorithm is designed to estimate the aggregate disturbances of various parameters uncertainties and other unmodeled disturbances that cannot be accurately calibrated.Finally,the proposed DSMO is integrated into a classical proportional-integral-derivative(PID)control scheme,utilizing feedforward approach to compensate the composite disturbance in the control loop online.The effectiveness of the proposed disturbance observer is validated through simulation and hardware experimentation,demonstrating a significant improvement in the dynamic control performance and robustness of the classical PID controller extensively utilized in the field of engineering.
基金the financial support of the National Natural Science Foundation of China(12102077,12161076)the Natural Science and Technology Program of Liaoning Province(2023-BS-061).
文摘Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.
文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金financially supported by the National Natural Science Foundation of China (No. 61890921, 61890923, and 52372371)the key projects of Aero Engine and Gas Turbine Basic Science Center (No. P2022-B-V-001-001 and P2022B-V-002-001)。
文摘Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.
基金supported by the National GNSS Research Center Program of the Defense Acquisition Program Administration and Agency for Defense Developmentthe Ministry of Science and ICT of the Republic of Korea through the Space Core Technology Development Program (No. NRF2018M1A3A3A02065722)
文摘In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.
基金supported by the EU Horizon 2020 through project ETEXWELD-H2020-MSCA-RISE-2014(Grant No.644268)The University of Manchester through UMRI project“Graphene-Smart Textiles E-Healthcare Network”(AA14512)National Natural Science Foundation of China(No.22075046).
文摘Wearable strain sensors are arousing increasing research interests in recent years on account of their potentials in motion detection,personal and public healthcare,future entertainment,man-machine interaction,artificial intelligence,and so forth.Much research has focused on fiber-based sensors due to the appealing performance of fibers,including processing flexibility,wearing comfortability,outstanding lifetime and serviceability,low-cost and large-scale capacity.Herein,we review the latest advances in functionalization and device fabrication of fiber materials toward applications in fiber-based wearable strain sensors.We describe the approaches for preparing conductive fibers such as spinning,surface modification,and structural transformation.We also introduce the fabrication and sensing mechanisms of state-of-the-art sensors and analyze their merits and demerits.The applications toward motion detection,healthcare,man-machine interaction,future entertainment,and multifunctional sensing are summarized with typical examples.We finally critically analyze tough challenges and future remarks of fiber-based strain sensors,aiming to implement them in real applications.
文摘The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
