The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the S...The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.展开更多
The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network sele...The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.展开更多
Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and decl...Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting.To address these limitations,this study proposes CIPF-Informer,a novel digital twin framework that integrates the Informer architecture with Channel Independence(CI)and a Particle Filter(PF).The CI mechanism enhances robustness by decoupling multivariate state dependencies,while the PF captures the complex stochastic variations missed by purely deterministic models.The proposed framework was evaluated using the Massachusetts Institute of Technology(MIT)battery dataset against benchmark deep learning models.Results demonstrate that CIPF-Informer consistently achieves superior performance,in multivariate and long sequence forecasting scenarios.By effectively synergizing a model-based method with a data-driven model,CIPF-Informer provides a more reliable pathway for advancing Battery Management System(BMS)technologies,contributing to the development of safer and more sustainable energy storage systems.展开更多
The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of a...The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.展开更多
With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of i...With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.展开更多
A new method of using dynamic equalization technology to realize the maximum energy storage utilization was presented to overcome the influence of the disaccord among units of series super capacitor （SC） bank and en...A new method of using dynamic equalization technology to realize the maximum energy storage utilization was presented to overcome the influence of the disaccord among units of series super capacitor （SC） bank and ensure that the units could work safely. By considering in combination with the high specific power, low working voltage, wide voltage working range and noulinear external characteristics, we present constant duty ratio pulse frequency modulation mode and fuzzy control method based on state prediction in the active equalization circuit and accomplish the software and hardware design for the equalization system. The simulation analysis and experiment results of constant current muhi-cycle and variable current multi-cycle charge-discharge process verify the validity of the design.展开更多
Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate p...Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.展开更多
Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this stud...Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this study,we introduce a3D residual network(3D Res Net)framework based on 3D convolutional neural networks(3D CNN)to predict ground states phases in 3D dipolar spinor Bose–Einstein condensates(BECs).Our results show that the 3D Res Net framework predicts ground states with high accuracy and efficiency across a broad parameter space.To enhance phase transition predictions,we incorporate data augmentation techniques,leading to a notable improvement in the model's performance.The method is further validated in more complex scenarios,particularly when transverse magnetic fields are introduced.Compared to conventional imaginary-time evolution methods(ITEM),the 3D Res Net drastically reduces computational costs,offering a rapid and scalable solution for complex 3D multi-parameter nonlinear systems.展开更多
Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and li...Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and limited scalability,making it increasingly difficult to meet the evolving demands of modern society.In this context,recent advancements in machine learning technology have emerged as a promising solution for predicting and monitoring battery states,offering innovative approaches to battery management systems(BMS).By transforming raw operational data into actionable insights,machine learning has shifted the paradigm from reactive to predictive battery safety management,significantly enhancing system reliability and risk mitigation capabilities.This review delves into the implementation of machine learning in battery state prediction,including dataset selection,feature extraction,and model training.It also highlights the latest progress of these models in key applications such as state of health(SOH),state of charge(SOC),thermal runaway warning,fault detection,and remaining useful life(RUL).Finally,we critically examined the challenges and opportunities associated with leveraging machine learning to improve battery safety and performance,providing a comprehensive perspective for future research in this rapidly advancing field.展开更多
The bogie is a crucial component of urban rail vehicles,and its performance plays a decisive role in the safe operation of vehicles.Aiming at the intelligent operation and maintenance requirements of rail transit equi...The bogie is a crucial component of urban rail vehicles,and its performance plays a decisive role in the safe operation of vehicles.Aiming at the intelligent operation and maintenance requirements of rail transit equipment,in this paper,it takes several key parts of the urban rail vehicle bogie system as research objects,such as motor bearings,frames,fasteners,etc.,and proposes a three-dimensional(3D)visual collaborative maintenance method.Firstly,a multi-sensor urban rail vehicle bogie running simulation experiment analysis platform was constructed,thereby establishing a database of running state and performance characteristics of the bogie in the whole life cycle.Then,the health status of key components of bogie was predicted by the state interval prediction model.Finally,the three-dimensional visual collaborative maintenance model proposed in this paper was integrated to realize the early warning of the bogie operation faults,3D precise guidance of automatic location and maintenance operation information,and collaborative sharing of visual information among multiple users.展开更多
The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagg...The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.展开更多
Ballistic Missile Trajectory Prediction(BMTP)is critical to air defense systems.Most Trajectory Prediction(TP)methods focus on the coast and reentry phases,in which the Ballistic Missile(BM)trajectories are modeled as...Ballistic Missile Trajectory Prediction(BMTP)is critical to air defense systems.Most Trajectory Prediction(TP)methods focus on the coast and reentry phases,in which the Ballistic Missile(BM)trajectories are modeled as ellipses or the state components are propagated by the dynamic integral equations on time scales.In contrast,the boost-phase TP is more challenging because there are many unknown forces acting on the BM in this phase.To tackle this difficult problem,a novel BMTP method by using Gaussian Processes(GPs)is proposed in this paper.In particular,the GP is employed to train the prediction error model of the boost-phase trajectory database,in which the error refers to the difference between the true BM state at the prediction moment and the integral extrapolation of the BM state.And the final BMTP is a combination of the dynamic equation based numerical integration and the GP-based prediction error.Since the trained GP aims to capture the relationship between the numerical integration and the unknown error,the modified BM state prediction is closer to the true one compared with the original TP.Furthermore,the GP is able to output the uncertainty information of the TP,which is of great significance for determining the warning range centered on the predicted BM state.Simulation results show that the proposed method effectively improves the BMTP accuracy during the boost phase and provides reliable uncertainty estimation boundaries.展开更多
As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppr...As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.展开更多
To extend the PSRK (predictive Soave-Redlich-Kwong equation of state) model to vapor-liquid equilibria of polymer solutions, a new EOS-gE mixing rule is applied in which the term ∑ xi ln(b/bi) in the PSRK mixing rule...To extend the PSRK (predictive Soave-Redlich-Kwong equation of state) model to vapor-liquid equilibria of polymer solutions, a new EOS-gE mixing rule is applied in which the term ∑ xi ln(b/bi) in the PSRK mixing rule for the parameter a, and the combinatorial part in the original universal functional activity coefficient (UNIFAC) model are cancelled. To take into account the free volume contribution to the excess Gibbs energy in polymer solution, a quadratic mixing rule for the cross co-volume bij with an exponent equals to 1/2 is applied[bij1/2= 1/2(bi1/2+bj1/2)]. The literature reported Soave-Redlich-Kwong equation of state (SRK EOS) parameters ofpure polymer are employed. The PSRK model with the modified mixing rule is used to predict the vapor-liquid equilibrium (VLE) of 37 solvent-polymer systems over a large range of temperature and pressure with satisfactory results.展开更多
In this paper, we propose a new state predictive model following control system （MFCS）. The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property ...In this paper, we propose a new state predictive model following control system （MFCS）. The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property of the internal states for the control is given and the utility of this control design is guaranteed. Finally, an example is given to illustrate the effectiveness of the proposed method.展开更多
Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch...Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.展开更多
To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction ...To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.展开更多
Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the prog...Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's disease.However,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks.To address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score prediction.In our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction.Then,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each task.We validate the effectiveness of our proposed method on multiple datasets.In Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,respectively.Experimental results show that our proposed method outperforms most state-of-the-art methods.Our proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score prediction.Therefore,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.展开更多
The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable...The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.展开更多
Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intell...Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.展开更多
文摘The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.
基金National Key Research and Development Program of China(No.2022YFB3903404,2024YFC3015403)National Natural Science Foundation of China(NSFC No.42271431,42271425)Hubei Province Major Science and Technology Innovation Program(2024BAA011)。
文摘The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.
基金supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government Ministry of Knowledge Economy(No.RS-2023-00244330)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF RS-2023-00219052).
文摘Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting.To address these limitations,this study proposes CIPF-Informer,a novel digital twin framework that integrates the Informer architecture with Channel Independence(CI)and a Particle Filter(PF).The CI mechanism enhances robustness by decoupling multivariate state dependencies,while the PF captures the complex stochastic variations missed by purely deterministic models.The proposed framework was evaluated using the Massachusetts Institute of Technology(MIT)battery dataset against benchmark deep learning models.Results demonstrate that CIPF-Informer consistently achieves superior performance,in multivariate and long sequence forecasting scenarios.By effectively synergizing a model-based method with a data-driven model,CIPF-Informer provides a more reliable pathway for advancing Battery Management System(BMS)technologies,contributing to the development of safer and more sustainable energy storage systems.
基金supported by the National Natural Science Foundation of China(60574088608740536103406)
文摘The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.
基金financial supports from the National Key Research and Development Program of China(2018YFA0209600)the Natural Science Foundation of China(22022813,21878268,52075481)。
文摘With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.
基金the National High Technology Research and Development Programme of China(No.2002AA001028)the Tenth Five-year Industry Item of the Tackling Key Problem of Heilongjiang Province(No.CA02A201)
文摘A new method of using dynamic equalization technology to realize the maximum energy storage utilization was presented to overcome the influence of the disaccord among units of series super capacitor （SC） bank and ensure that the units could work safely. By considering in combination with the high specific power, low working voltage, wide voltage working range and noulinear external characteristics, we present constant duty ratio pulse frequency modulation mode and fuzzy control method based on state prediction in the active equalization circuit and accomplish the software and hardware design for the equalization system. The simulation analysis and experiment results of constant current muhi-cycle and variable current multi-cycle charge-discharge process verify the validity of the design.
基金supported in part by the Chunhui Project of the Ministry of Education of China(HZKY20220429)the Department of Science&Technology of Liaoning Province(2022-MS-300)the Educational Department of Liaoning Province(LJKMZ20220561)
文摘Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11904309 and 12305015)the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5528)the Natural Science Foundation of Hebei Province,China(Grant No.A2024205027)。
文摘Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this study,we introduce a3D residual network(3D Res Net)framework based on 3D convolutional neural networks(3D CNN)to predict ground states phases in 3D dipolar spinor Bose–Einstein condensates(BECs).Our results show that the 3D Res Net framework predicts ground states with high accuracy and efficiency across a broad parameter space.To enhance phase transition predictions,we incorporate data augmentation techniques,leading to a notable improvement in the model's performance.The method is further validated in more complex scenarios,particularly when transverse magnetic fields are introduced.Compared to conventional imaginary-time evolution methods(ITEM),the 3D Res Net drastically reduces computational costs,offering a rapid and scalable solution for complex 3D multi-parameter nonlinear systems.
基金supported by the National Key Research and Development Program of China(No.2021YFF0500600)Natural Science Foundation of Henan Province(No.252300421176)+1 种基金National Natural Science Foundation of China(No.22478361 and No.22108256)Frontier Exploration Projects of Longmen Laboratory(No.LMQYTSKT021)。
文摘Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and limited scalability,making it increasingly difficult to meet the evolving demands of modern society.In this context,recent advancements in machine learning technology have emerged as a promising solution for predicting and monitoring battery states,offering innovative approaches to battery management systems(BMS).By transforming raw operational data into actionable insights,machine learning has shifted the paradigm from reactive to predictive battery safety management,significantly enhancing system reliability and risk mitigation capabilities.This review delves into the implementation of machine learning in battery state prediction,including dataset selection,feature extraction,and model training.It also highlights the latest progress of these models in key applications such as state of health(SOH),state of charge(SOC),thermal runaway warning,fault detection,and remaining useful life(RUL).Finally,we critically examined the challenges and opportunities associated with leveraging machine learning to improve battery safety and performance,providing a comprehensive perspective for future research in this rapidly advancing field.
基金the Key Project of Research and Development Plan of HunanProvince under Grant 2018GK2044in part by the Natural Science Foundation of Hunan Provinceunder Grant 2018JJ4084+1 种基金in part by the National Natural Science Youth Fund Project under Grant51805168in part by the Science and Technology Talent Project of Hunan Province – HuxiangYouth Talent under Grant 2019RS2062.
文摘The bogie is a crucial component of urban rail vehicles,and its performance plays a decisive role in the safe operation of vehicles.Aiming at the intelligent operation and maintenance requirements of rail transit equipment,in this paper,it takes several key parts of the urban rail vehicle bogie system as research objects,such as motor bearings,frames,fasteners,etc.,and proposes a three-dimensional(3D)visual collaborative maintenance method.Firstly,a multi-sensor urban rail vehicle bogie running simulation experiment analysis platform was constructed,thereby establishing a database of running state and performance characteristics of the bogie in the whole life cycle.Then,the health status of key components of bogie was predicted by the state interval prediction model.Finally,the three-dimensional visual collaborative maintenance model proposed in this paper was integrated to realize the early warning of the bogie operation faults,3D precise guidance of automatic location and maintenance operation information,and collaborative sharing of visual information among multiple users.
基金The National Natural Science Foundation of China (No.50422283)the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)
文摘The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.
基金support from National Natural Science Foundation of China(Nos.61873205,61771399)Aerospace Science Foundation of China(No.2019-HT-XGD)Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JM-101).
文摘Ballistic Missile Trajectory Prediction(BMTP)is critical to air defense systems.Most Trajectory Prediction(TP)methods focus on the coast and reentry phases,in which the Ballistic Missile(BM)trajectories are modeled as ellipses or the state components are propagated by the dynamic integral equations on time scales.In contrast,the boost-phase TP is more challenging because there are many unknown forces acting on the BM in this phase.To tackle this difficult problem,a novel BMTP method by using Gaussian Processes(GPs)is proposed in this paper.In particular,the GP is employed to train the prediction error model of the boost-phase trajectory database,in which the error refers to the difference between the true BM state at the prediction moment and the integral extrapolation of the BM state.And the final BMTP is a combination of the dynamic equation based numerical integration and the GP-based prediction error.Since the trained GP aims to capture the relationship between the numerical integration and the unknown error,the modified BM state prediction is closer to the true one compared with the original TP.Furthermore,the GP is able to output the uncertainty information of the TP,which is of great significance for determining the warning range centered on the predicted BM state.Simulation results show that the proposed method effectively improves the BMTP accuracy during the boost phase and provides reliable uncertainty estimation boundaries.
基金Project(2018YFB2002100)supported by the National Key R&D Program of China。
文摘As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.
文摘To extend the PSRK (predictive Soave-Redlich-Kwong equation of state) model to vapor-liquid equilibria of polymer solutions, a new EOS-gE mixing rule is applied in which the term ∑ xi ln(b/bi) in the PSRK mixing rule for the parameter a, and the combinatorial part in the original universal functional activity coefficient (UNIFAC) model are cancelled. To take into account the free volume contribution to the excess Gibbs energy in polymer solution, a quadratic mixing rule for the cross co-volume bij with an exponent equals to 1/2 is applied[bij1/2= 1/2(bi1/2+bj1/2)]. The literature reported Soave-Redlich-Kwong equation of state (SRK EOS) parameters ofpure polymer are employed. The PSRK model with the modified mixing rule is used to predict the vapor-liquid equilibrium (VLE) of 37 solvent-polymer systems over a large range of temperature and pressure with satisfactory results.
文摘In this paper, we propose a new state predictive model following control system （MFCS）. The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property of the internal states for the control is given and the utility of this control design is guaranteed. Finally, an example is given to illustrate the effectiveness of the proposed method.
基金Research on Robust Decision and Full Stack Optimisation Techniques for Cloud Edge Intelligent Systems for Substation Inspection,Grant/Award Number:52550022001J。
文摘Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.
基金Supported by the National Natural Science Foundation of China(61175090,61703249)Shandong Provincial Natural Science Foundation,China(ZR2017MF045)
文摘To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.
基金supported in part by the National Natural Science Foundation of China(No.62172444)Natural Science Foundation of Hunan Province(No.2022JJ30753)+1 种基金Central South University Innovation-Driven Research Programme(No.2023CXQD018)High Performance Computing Center of Central South University.
文摘Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's disease.However,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks.To address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score prediction.In our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction.Then,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each task.We validate the effectiveness of our proposed method on multiple datasets.In Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,respectively.Experimental results show that our proposed method outperforms most state-of-the-art methods.Our proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score prediction.Therefore,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.
文摘The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.
基金the financial support partially provided by The Quality Engineering Project of Anhui Province(2019sjjd58,2020sxzx36)The Ministry of Education Cooperative Education Project(201901119016)+1 种基金The Chinese(Jiangsu)-Czech Bilateral Co-funding R&D Project(SBZ2018000220)the Key R&D Project of Anhui Science and Technology Department(202004b11020026).
文摘Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.
