We carry out an optical morphological and infrared spectral study for two young planetary nebulae(PNs)Hen2-158 and Pe 1-1 to understand their complex shapes and dust properties.Hubble Space Telescope optical images re...We carry out an optical morphological and infrared spectral study for two young planetary nebulae(PNs)Hen2-158 and Pe 1-1 to understand their complex shapes and dust properties.Hubble Space Telescope optical images reveal that these nebulae have several bipolar-lobed structures and a faint arc with a clear boundary is located at the northwestern side of Pe 1-1.The presence of this arc-shaped structure suggests that the object interacts with its nearby interstellar medium.Spitzer IRS spectroscopic observations of these young nebulae clearly show prominent unidentified infrared emission features and a weak silicate band in Pe 1-1,indicating that Hen 2-158 is a carbonrich nebula and Pe 1-1 has a mixed chemistry dust environment.Furthermore,we construct two three-dimensional models for these PNs to realize their intrinsic structures.The simulated models of the nebulae suggest that multipolar nebulae may be more numerous than we thought.Our analyses of spectral energy distributions for Hen 2-158 and Pe 1-1 show that they have low luminosities and low stellar effective temperatures,suggesting that these nebulae are young PNs.A possible correlation between typical multipolar young PNs and nested nebulae is also discussed.展开更多
Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault ...Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.展开更多
The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data gen...The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.展开更多
Craniomaxillofacial reconstruction implants,which are extensively used in head and neck surgery,are conventionally made in standardized forms.During surgery,the implant must be bended manually to match the anatomy of ...Craniomaxillofacial reconstruction implants,which are extensively used in head and neck surgery,are conventionally made in standardized forms.During surgery,the implant must be bended manually to match the anatomy of the individual bones.The bending process is time-consuming,especially for inexperienced surgeons.Moreover,repetitive bending may induce undesirable internal stress concentration,resulting in fatigue under masticatory loading in v iv o and causing various complications such as implant fracture,screw loosening,and bone resorption.There have been reports on the use of patient-specific 3D-printed implants for craniomaxillofacial reconstruction,although few reports have considered implant quality.In this paper,we present a systematic approach for making 3D-printed patientspecific surgical implants for craniomaxillofacial reconstruction.The approach consists of three parts:First,an easy-to-use design module is developed using Solidworks®software,which helps surgeons to design the implants and the axillary fixtures for surgery.Design engineers can then carry out the detailed design and use finite-element modeling(FEM)to optimize the design.Second,the fabrication process is carried out in three steps:0 testing the quality of the powder;(2)setting up the appropriate process parameters and running the 3D printing process;and (3)conducting post-processing treatments(i.e.,heat and surface treatments)to ensure the quality and performance of the implant.Third,the operation begins after the final checking of the implant and sterilization.After the surgery,postoperative rehabilitation follow-up can be carried out using our patient tracking software.Following this systematic approach,we have successfully conducted a total of 41 surgical cases.3D-printed patient-specific implants have a number of advantages;in particular,their use reduces surgery time and shortens patient recovery time.Moreover,the presented approach helps to ensure implant quality.展开更多
The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence...The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI technology.The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in China.Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.展开更多
Learning hydrophobic phenomena from nature is always a promising approach to design the superhydrophobic surface.Purple orchid leaf which processes superhydrophobicity is an ideal plant model,and through mimicking its...Learning hydrophobic phenomena from nature is always a promising approach to design the superhydrophobic surface.Purple orchid leaf which processes superhydrophobicity is an ideal plant model,and through mimicking its structure,the surface with excellent hydrophobicity is able to be obtained.However,the unclear of the diversity in wettability during the different vegetation stages and the absence of its relation to the surface morphology limits the further enhancement of the inspired structure.Here,we analyze the wettability difference as the leaf grows from tender to mature and then to senescent.Combining with the variation of surface morphology and chemical composition,the well-developed micro-scale basic unit bumps with dense nano-scale waxy layer on the surface are proven to be responsible for the best hydrophobicity of the mature leaf.The presence of the undeveloped or damaged micro-nano hierarchical structure reduces the formation of air pockets at the interface,leading to the decrease of the wettability for leaves at other stages.Moreover,by fabricating artificial leaves,the nano-waxy layer is proved to be more effective than that of the micro-bumps on the surface wettability.The results of study are of a great significance for guiding the design and fabrication of plant-inspired bionic superhydrophobic surface.展开更多
As a dynamic energy storage system,electric vehicles(EV)play important roles in future power grids.In this paper,a model for EV aggregator participation in the electricity market has been built with a focus on the fea...As a dynamic energy storage system,electric vehicles(EV)play important roles in future power grids.In this paper,a model for EV aggregator participation in the electricity market has been built with a focus on the feasibility issue of the model arising from economic interest inconsistencies between different stakeholders:EV owners and aggregator.In the model,the EV aggregator attends day-ahead energy and reserve markets for profit maximization by scheduling charging and discharging behaviors of EVs.This issue exists since different stakeholders have different interests which are not necessarily consistent,e.g.profit maximization leads to increasing EV owners'charging fee.To investigate the economic relationship between the two stakeholders,two multi-objective optimization methods(weighted sum and$\varepsilon$-constraint methods)are proposed to take the aggregator profit and EV owners'charging fee into account in the model.A sensitivity analysis is applied to examine the aggregator profit under different price scenarios,which reveals the internal relationship between EV owners'charging fees and aggregator profit.The proposed EV charging and discharging strategy in this paper could be used to determine the settlement price between the aggregator and owners to ensure the feasibility of participation from both EV owners and stakeholders in electricity markets.展开更多
Consider the precision attitude regulation with vibration suppression for an uncertain and disturbed flexible spacecraft.The disturbance at issue is typically any finite superposition of sinusoidal signals with unknow...Consider the precision attitude regulation with vibration suppression for an uncertain and disturbed flexible spacecraft.The disturbance at issue is typically any finite superposition of sinusoidal signals with unknown frequencies and step signals of unknown amplitudes.First we show that the conventional mathematical model for flexible spacecrafts is transformable to a multi-input multi-output(MIMO)strict-feedback nonlinear normal form.Particularly it is strongly minimum-phase and has a well-defined uniform vector relative degree.Then it enables us to develop an adaptive internal model-based controller in the framework of adaptive output regulation to solve the problem.It is proved that asymptotic stability can be guaranteed for the attitude regulation task and the vibration of flexible appendages vanishes asymptotically.Hence,the present study explores a new idea for control of flexible spacecraft in virtue of its system structures.展开更多
One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance.Generating training data using a virtual model is a...One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance.Generating training data using a virtual model is a potential solution for addressing such a problem,and the construction of a high-fidelity virtual model is fundamental and critical for data generation.In this study,a digital twin-assisted dynamic model updating method for fault diagnosis is thus proposed to improve the fidelity and reliability of a virtual model,which can enhance the generated data quality.First,a virtual model is established to mirror the vibration response of a physical entity using a dynamic modeling method.Second,the modeling method is validated through a frequency analysis of the generated signal.Then,based on the signal similarity indicator,a physical–virtual signal interaction method is proposed to dynamically update the virtual model in which parameter sensitivity analysis,surrogate technique,and optimization algorithm are applied to increase the efficiency during the model updating.Finally,the proposed method is successfully applied to the dynamic model updating of a single-stage helical gearbox;the virtual data generated by this model can be used for gear fault diagnosis.展开更多
Surface electromyography(sEMG)is widely used in monitoring human health.Nonetheless,it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx,because regul...Surface electromyography(sEMG)is widely used in monitoring human health.Nonetheless,it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx,because regular sEMG electrodes have stiff structures.In this study,we developed a stretchable,high-density sEMG electrode array via layerby-layer printing and lamination.The electrode offered a series of excellent human‒machine interface features,including conformal adhesion to the skin,high electron-to-ion conductivity(and thus lower contact impedance),prolonged environmental adaptability to resist water evaporation,and epidermal biocompatibility.This made the electrode more appropriate than commercial electrodes for long-term wearable,high-fidelity sEMG recording devices at complicated skin interfaces.Systematic in vivo studies were used to investigate its ability to classify swallowing activities,which was accomplished with high accuracy by decoding the sEMG signals from the chin via integration with an ear-mounted wearable system and machine learning algorithms.The results demonstrated the clinical feasibility of the system for noninvasive and comfortable recognition of swallowing motions for comfortable dysphagia rehabilitation.展开更多
This paper studies global robust tracking of uncertain Euler-Lagrange systems with input disturbances.The authors develop a robust regulation-based approach for the problem.Specifically,by introducing a novel nonlinea...This paper studies global robust tracking of uncertain Euler-Lagrange systems with input disturbances.The authors develop a robust regulation-based approach for the problem.Specifically,by introducing a novel nonlinear internal model,the authors solve global asymptotic trajectory tracking with disturbance rejection of multiple step/sinusoidal signals with unknown amplitudes,frequencies,and phases.Moreover,the authors show that a robustness property to actuator noises can be guaranteed in a sense of strong integral input-to-state stability(iISS).That is,the closed-loop system is not only i ISS but also input-to-state stable(ISS)to small magnitude actuator noises.Furthermore,the authors explore a by-product overparametrized linear regression estimation,coming up with robust estimation of the unknown parameters.Finally,the authors present several numerical examples to illustrate the theoretical results.展开更多
Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize ...Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.展开更多
This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a...This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a pair of pectoral fins,a wire-driven active body covered with soft skin,and a compliant tail.The CPG model consists of four input parameters:the flapping amplitude,the flapping angular velocity,the flapping offset,and the time ratio between the beat phase and the restore phase in flapping.The robot fish is equipped with three infrared sensors mounted on the left,front and right of the robot fish,as well as an inertial measurement unit,from which the surrounding obstacles and moving direction can be sensed.Based on these sensor signals,the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions.Four sets of experiments are presented,including avoiding a static obstacle,avoiding a moving obstacle,tracking a designated direction and tracking a designated direction with an obstacle in the path.The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively.展开更多
As a typical underwater robot,the robotic fish has been a hot topic of research in terms of the swimming control method and the reduction of energy consumption.Body flexibility and passive designs have been proven to ...As a typical underwater robot,the robotic fish has been a hot topic of research in terms of the swimming control method and the reduction of energy consumption.Body flexibility and passive designs have been proven to be effective approaches for improving the swimming performance of robotic fishes.However,the addition of passive structures or motions of these approaches makes it more difficult to control the movement of robotic fish.In this paper,we proposed a deep reinforcement learning-based method for online learning control of a robotic eel with multiple passive structures.First,we designed a robotic eel with two wire-driven segments and two complaint bodies made of elastic material.Second,a simulation model of the robotic eel was built and the validity of the model was tested.Following that,the neural network was trained in simulation and deployed directly on the robotic eel,which was controlled without an underlying control model or strategy and directly online by the neural network.Finally,extensive experiments verified the effectiveness of our control method,which offers a valuable and potential solution for robots difficult to model and control.展开更多
Soft robotic crawlers have limited payload capacity and crawling speed.This study proposes a high-performance inchworm-like modular robotic crawler based on fluidic prestressed composite(FPC)actuators.The FPC actuator...Soft robotic crawlers have limited payload capacity and crawling speed.This study proposes a high-performance inchworm-like modular robotic crawler based on fluidic prestressed composite(FPC)actuators.The FPC actuator is precurved and a pneumatic source is used to flatten it,requiring no energy cost to maintain the equilibrium curved shape.Pressurizing and depressurizing the actuators generate alternating stretching and bending motions of the actuators,achieving the crawling motion of the robotic crawler.Multi-modal locomotion(crawling,turning,and pipe climbing)is achieved by modular reconfiguration and gait design.An analytical kinematic model is proposed to characterize the quasi-static curvature and step size of a single-module crawler.Multiple configurations of robotic crawlers are fabricated to demonstrate the crawling ability of the proposed design.A set of systematic experiments are set up and conducted to understand how crawler responses vary as a function of FPC prestrains,input pressures,and actuation frequencies.As per the experiments,the maximum carrying load ratio(carrying load divided by robot weight)is found to be 22.32,and the highest crawling velocity is 3.02 body length(BL)per second(392 mm/s).Multi-modal capabilities are demonstrated by reconfiguring three soft crawlers,including a matrix crawler robot crawling in amphibious environments,and an inching crawler turning at an angular velocity of 2/s,as well as earthworm-like crawling robots climbing a 20 inclination slope and pipe.展开更多
基金C.-H.H.thanks supports from the Hong Kong Research Grants Council for GRF research support under the grants 17326116 and 17300417。
文摘We carry out an optical morphological and infrared spectral study for two young planetary nebulae(PNs)Hen2-158 and Pe 1-1 to understand their complex shapes and dust properties.Hubble Space Telescope optical images reveal that these nebulae have several bipolar-lobed structures and a faint arc with a clear boundary is located at the northwestern side of Pe 1-1.The presence of this arc-shaped structure suggests that the object interacts with its nearby interstellar medium.Spitzer IRS spectroscopic observations of these young nebulae clearly show prominent unidentified infrared emission features and a weak silicate band in Pe 1-1,indicating that Hen 2-158 is a carbonrich nebula and Pe 1-1 has a mixed chemistry dust environment.Furthermore,we construct two three-dimensional models for these PNs to realize their intrinsic structures.The simulated models of the nebulae suggest that multipolar nebulae may be more numerous than we thought.Our analyses of spectral energy distributions for Hen 2-158 and Pe 1-1 show that they have low luminosities and low stellar effective temperatures,suggesting that these nebulae are young PNs.A possible correlation between typical multipolar young PNs and nested nebulae is also discussed.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 52205100,52275111,and 52205101in part by the Natural Science Foundations of Guangdong Province-China under Grants 2023A1515012856in part by China Postdoctoral Science Foundation under Grant 2022M711197.
文摘Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.
基金supported in part by the National Nature Science Foundation of China under Grant 62001168in part by the Foundation and Application Research Grant of Guangzhou under Grant 202102020515。
文摘The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.
基金The study was partially supported by the Innovative Scientific Team Research Fund(2018IT100212)Science and Technology Bureau,Fo Shan,Guangdong,China.It was also partially supported by the Health and Medical Research Fund(05161626)Food and Health Bureau,Hong Kong,China.
文摘Craniomaxillofacial reconstruction implants,which are extensively used in head and neck surgery,are conventionally made in standardized forms.During surgery,the implant must be bended manually to match the anatomy of the individual bones.The bending process is time-consuming,especially for inexperienced surgeons.Moreover,repetitive bending may induce undesirable internal stress concentration,resulting in fatigue under masticatory loading in v iv o and causing various complications such as implant fracture,screw loosening,and bone resorption.There have been reports on the use of patient-specific 3D-printed implants for craniomaxillofacial reconstruction,although few reports have considered implant quality.In this paper,we present a systematic approach for making 3D-printed patientspecific surgical implants for craniomaxillofacial reconstruction.The approach consists of three parts:First,an easy-to-use design module is developed using Solidworks®software,which helps surgeons to design the implants and the axillary fixtures for surgery.Design engineers can then carry out the detailed design and use finite-element modeling(FEM)to optimize the design.Second,the fabrication process is carried out in three steps:0 testing the quality of the powder;(2)setting up the appropriate process parameters and running the 3D printing process;and (3)conducting post-processing treatments(i.e.,heat and surface treatments)to ensure the quality and performance of the implant.Third,the operation begins after the final checking of the implant and sterilization.After the surgery,postoperative rehabilitation follow-up can be carried out using our patient tracking software.Following this systematic approach,we have successfully conducted a total of 41 surgical cases.3D-printed patient-specific implants have a number of advantages;in particular,their use reduces surgery time and shortens patient recovery time.Moreover,the presented approach helps to ensure implant quality.
文摘The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI technology.The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in China.Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.
基金This work was financially supported by the National Key R&D Program of China(Grant No.2020YFB1711300)the National Natural Science Foundation of China(Grant No.52275425)the Natural Science Foundation of Guangdong Province for Distinguished Young Scholars(Grant No.2021B1515020087).
文摘Learning hydrophobic phenomena from nature is always a promising approach to design the superhydrophobic surface.Purple orchid leaf which processes superhydrophobicity is an ideal plant model,and through mimicking its structure,the surface with excellent hydrophobicity is able to be obtained.However,the unclear of the diversity in wettability during the different vegetation stages and the absence of its relation to the surface morphology limits the further enhancement of the inspired structure.Here,we analyze the wettability difference as the leaf grows from tender to mature and then to senescent.Combining with the variation of surface morphology and chemical composition,the well-developed micro-scale basic unit bumps with dense nano-scale waxy layer on the surface are proven to be responsible for the best hydrophobicity of the mature leaf.The presence of the undeveloped or damaged micro-nano hierarchical structure reduces the formation of air pockets at the interface,leading to the decrease of the wettability for leaves at other stages.Moreover,by fabricating artificial leaves,the nano-waxy layer is proved to be more effective than that of the micro-bumps on the surface wettability.The results of study are of a great significance for guiding the design and fabrication of plant-inspired bionic superhydrophobic surface.
文摘As a dynamic energy storage system,electric vehicles(EV)play important roles in future power grids.In this paper,a model for EV aggregator participation in the electricity market has been built with a focus on the feasibility issue of the model arising from economic interest inconsistencies between different stakeholders:EV owners and aggregator.In the model,the EV aggregator attends day-ahead energy and reserve markets for profit maximization by scheduling charging and discharging behaviors of EVs.This issue exists since different stakeholders have different interests which are not necessarily consistent,e.g.profit maximization leads to increasing EV owners'charging fee.To investigate the economic relationship between the two stakeholders,two multi-objective optimization methods(weighted sum and$\varepsilon$-constraint methods)are proposed to take the aggregator profit and EV owners'charging fee into account in the model.A sensitivity analysis is applied to examine the aggregator profit under different price scenarios,which reveals the internal relationship between EV owners'charging fees and aggregator profit.The proposed EV charging and discharging strategy in this paper could be used to determine the settlement price between the aggregator and owners to ensure the feasibility of participation from both EV owners and stakeholders in electricity markets.
基金This work was supported by the National Natural Science Foundation of China(Nos.61873250,62073168,61871221).
文摘Consider the precision attitude regulation with vibration suppression for an uncertain and disturbed flexible spacecraft.The disturbance at issue is typically any finite superposition of sinusoidal signals with unknown frequencies and step signals of unknown amplitudes.First we show that the conventional mathematical model for flexible spacecrafts is transformable to a multi-input multi-output(MIMO)strict-feedback nonlinear normal form.Particularly it is strongly minimum-phase and has a well-defined uniform vector relative degree.Then it enables us to develop an adaptive internal model-based controller in the framework of adaptive output regulation to solve the problem.It is proved that asymptotic stability can be guaranteed for the attitude regulation task and the vibration of flexible appendages vanishes asymptotically.Hence,the present study explores a new idea for control of flexible spacecraft in virtue of its system structures.
基金supported in part by the National Key R&D Program of China(Grant No.2018YFB1702400)the National Natural Science Foundation of China(Grant Nos.52275111,52205100,and 52205101)the Guangdong Basic and Applied Basic Research Foundation,China(Grant Nos.2021A1515110708 and 2023A1515012856).
文摘One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance.Generating training data using a virtual model is a potential solution for addressing such a problem,and the construction of a high-fidelity virtual model is fundamental and critical for data generation.In this study,a digital twin-assisted dynamic model updating method for fault diagnosis is thus proposed to improve the fidelity and reliability of a virtual model,which can enhance the generated data quality.First,a virtual model is established to mirror the vibration response of a physical entity using a dynamic modeling method.Second,the modeling method is validated through a frequency analysis of the generated signal.Then,based on the signal similarity indicator,a physical–virtual signal interaction method is proposed to dynamically update the virtual model in which parameter sensitivity analysis,surrogate technique,and optimization algorithm are applied to increase the efficiency during the model updating.Finally,the proposed method is successfully applied to the dynamic model updating of a single-stage helical gearbox;the virtual data generated by this model can be used for gear fault diagnosis.
基金supported by the National Natural Science Foundation of China(grant numbers 42177440 and 51903079)National Natural Science Foundation of China(grant no.52075177)+1 种基金National Key Research and Development Program of China(grant no.2021YFB3301400)Research Foundation of Guangdong Province(grant no.2019A050505001).
文摘Surface electromyography(sEMG)is widely used in monitoring human health.Nonetheless,it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx,because regular sEMG electrodes have stiff structures.In this study,we developed a stretchable,high-density sEMG electrode array via layerby-layer printing and lamination.The electrode offered a series of excellent human‒machine interface features,including conformal adhesion to the skin,high electron-to-ion conductivity(and thus lower contact impedance),prolonged environmental adaptability to resist water evaporation,and epidermal biocompatibility.This made the electrode more appropriate than commercial electrodes for long-term wearable,high-fidelity sEMG recording devices at complicated skin interfaces.Systematic in vivo studies were used to investigate its ability to classify swallowing activities,which was accomplished with high accuracy by decoding the sEMG signals from the chin via integration with an ear-mounted wearable system and machine learning algorithms.The results demonstrated the clinical feasibility of the system for noninvasive and comfortable recognition of swallowing motions for comfortable dysphagia rehabilitation.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61673216 and 62073168supported by the China Scholarship Council on his study at the University of Groningen,The Netherlandspartially done when he was with the School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China。
文摘This paper studies global robust tracking of uncertain Euler-Lagrange systems with input disturbances.The authors develop a robust regulation-based approach for the problem.Specifically,by introducing a novel nonlinear internal model,the authors solve global asymptotic trajectory tracking with disturbance rejection of multiple step/sinusoidal signals with unknown amplitudes,frequencies,and phases.Moreover,the authors show that a robustness property to actuator noises can be guaranteed in a sense of strong integral input-to-state stability(iISS).That is,the closed-loop system is not only i ISS but also input-to-state stable(ISS)to small magnitude actuator noises.Furthermore,the authors explore a by-product overparametrized linear regression estimation,coming up with robust estimation of the unknown parameters.Finally,the authors present several numerical examples to illustrate the theoretical results.
基金supported by National Nature Science Foundation of China(NSFC)(Nos.U20A20200,61811530281,and 61861136009)Guangdong Regional Joint Foundation(No.2019B1515120076)+1 种基金Fundamental Research for the Central Universitiesin part by the Foshan Science and Technology Innovation Team Special Project(No.2018IT100322)。
文摘Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(class A)(Grant No.XDA22040203)the Fundamental Research Funds for the Central Universities(Grant No.2019XX01)+1 种基金GDNRC[2020]031the Natural Science Foundation of Guangdong Province(Grant No.2020A1515010621).
文摘This paper presents a study on bioinspired closed-loop Central Pattern Generator(CPG)based control of a robot fish for obstacle avoidance and direction tracking.The biomimetic robot fish is made of a rigid head with a pair of pectoral fins,a wire-driven active body covered with soft skin,and a compliant tail.The CPG model consists of four input parameters:the flapping amplitude,the flapping angular velocity,the flapping offset,and the time ratio between the beat phase and the restore phase in flapping.The robot fish is equipped with three infrared sensors mounted on the left,front and right of the robot fish,as well as an inertial measurement unit,from which the surrounding obstacles and moving direction can be sensed.Based on these sensor signals,the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions.Four sets of experiments are presented,including avoiding a static obstacle,avoiding a moving obstacle,tracking a designated direction and tracking a designated direction with an obstacle in the path.The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively.
基金supported in part by the National Natural Science Foundation of China(62103152)in part by the Natural Science Foundation of Guangdong Province,China(2022A1515011479)+1 种基金in part by the KEY Laboratory of Robotics and Intelligent Equipment of Guangdong Regular Institutions of Higher Education,China(2017KSYS009)in part by the Innovation Center of Robotics and Intelligent Equipment。
文摘As a typical underwater robot,the robotic fish has been a hot topic of research in terms of the swimming control method and the reduction of energy consumption.Body flexibility and passive designs have been proven to be effective approaches for improving the swimming performance of robotic fishes.However,the addition of passive structures or motions of these approaches makes it more difficult to control the movement of robotic fish.In this paper,we proposed a deep reinforcement learning-based method for online learning control of a robotic eel with multiple passive structures.First,we designed a robotic eel with two wire-driven segments and two complaint bodies made of elastic material.Second,a simulation model of the robotic eel was built and the validity of the model was tested.Following that,the neural network was trained in simulation and deployed directly on the robotic eel,which was controlled without an underlying control model or strategy and directly online by the neural network.Finally,extensive experiments verified the effectiveness of our control method,which offers a valuable and potential solution for robots difficult to model and control.
基金supported by the National Natural Science Foundation of China under Grant No.62203174the Guangzhou Municipal Science and Technology Project under Grant No.202201010179.
文摘Soft robotic crawlers have limited payload capacity and crawling speed.This study proposes a high-performance inchworm-like modular robotic crawler based on fluidic prestressed composite(FPC)actuators.The FPC actuator is precurved and a pneumatic source is used to flatten it,requiring no energy cost to maintain the equilibrium curved shape.Pressurizing and depressurizing the actuators generate alternating stretching and bending motions of the actuators,achieving the crawling motion of the robotic crawler.Multi-modal locomotion(crawling,turning,and pipe climbing)is achieved by modular reconfiguration and gait design.An analytical kinematic model is proposed to characterize the quasi-static curvature and step size of a single-module crawler.Multiple configurations of robotic crawlers are fabricated to demonstrate the crawling ability of the proposed design.A set of systematic experiments are set up and conducted to understand how crawler responses vary as a function of FPC prestrains,input pressures,and actuation frequencies.As per the experiments,the maximum carrying load ratio(carrying load divided by robot weight)is found to be 22.32,and the highest crawling velocity is 3.02 body length(BL)per second(392 mm/s).Multi-modal capabilities are demonstrated by reconfiguring three soft crawlers,including a matrix crawler robot crawling in amphibious environments,and an inching crawler turning at an angular velocity of 2/s,as well as earthworm-like crawling robots climbing a 20 inclination slope and pipe.
