In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways Hi...In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally alte...After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and utilized.This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach.At the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy resources.The effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and sustainability.This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management.However,there is still no extensive implementation of such sharing models in today’s electricitymarkets.Research on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market.This paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability,the finite-time stability poss...The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability,the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information.This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.展开更多
Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based softw...Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.展开更多
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i...Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.展开更多
The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position model.To guar...The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position model.To guarantee both transient performance and steady-state landing error,a prescribed performance evolution control(PPEC)method is developed for the relative position control.In addition,a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined threshold.Considering the wind and wave on the relative position model,an adaptive sliding mode observer(ASMO)is designed for the disturbance with unknown upper bound.Based on the dynamic surface control framework,a shipboard landing controller integrating PPEC and ASMO is established for the quadrotor,and the relative position control error is guaranteed to be uniformly ultimately bounded.Simulation results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.展开更多
Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have muc...Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have much lower torque density/thrust density compared with the same type PM machines.To overcome this challenge,electromagnetic-thermal coupled analysis is carried out with respect to water-cooled electrically-excited flux-switching linear machines(EEFSLM).The simulation results indicate that the conventional fixed copper loss method(FCLM)is no longer suitable for high thrust density design,since it is unable to consider the strong coupling between the electromagnetic and thermal performance.Hence,a multi-step electromagnetic-thermal joint optimisation method is proposed,which first ensures the consistency between the electromagnetic and thermal modelling and then considers the effect of different field/armature coil sizes.By using the proposed joint optimisation method,it is found that the combination of relatively large size of field coil and relatively low field copper loss is favourable for achieving high thrust force for the current EEFSLM design.Moreover,the thrust force is raised by 13-15%compared with using the FCLM.The electromagnetic and thermal performance of the EEFSLM is validated by the prototype test.展开更多
Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health ...Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health care,national defense and daily life.The artificial intelligence techniques are becoming useful as an alternate method of classical techniques or as a component of an integrated system.They are used to solve complicated problems in various fields and becoming increasingly popular nowadays.Especially,the investigation of human brain will promote the artificial intelligence techniques,utilizing the accumulating knowledge of neuroscience,brain-machine interface techniques,algorithms of spiking neural networks and neuromorphic supercomputers.Consequently,we provide a comprehensive survey of the research and motivations for brain-inspired artificial intelligence and its engineering over its history.The goals of this work are to provide a brief review of the research associated with brain-inspired artificial intelligence and its related engineering techniques,and to motivate further work by elucidating challenges in the field where new researches are required.展开更多
A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies t...A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.展开更多
The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control p...The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.展开更多
With the rapid development of urban rail transit,there have been an urgent problem of excessive stray current.Because the stray current distribution is random and difficult to verify in the field,we designed an improv...With the rapid development of urban rail transit,there have been an urgent problem of excessive stray current.Because the stray current distribution is random and difficult to verify in the field,we designed an improved stray current experimental platform by replacing the simulated aqueous solution with a real soil environment and by calculating the transition resistance by measuring the soil resistivity,which makes up for the defects in the previous references.Firstly,the mathematical models of rail-drainage net and rail-drainage netground were established,and the analytical expressions of current and voltage of rail,drainage net and other structures were derived.In addition,the simulation model was built,and the mathematical analysis results were compared with the simulation results.Secondly,the accuracy of the improved stray current experimental platform was verified by comparing the measured and simulation results.Finally,based on the experimental results,the influence factors of stray current were analyzed.The relevant conclusions provide experimental data and theoretical reference for the study of stray current in urban rail transit.展开更多
In light of the prevailing issue that the existing convolutional neural network(CNN)power quality disturbance identification method can only extract single-scale features,which leads to a lack of feature information a...In light of the prevailing issue that the existing convolutional neural network(CNN)power quality disturbance identification method can only extract single-scale features,which leads to a lack of feature information and weak anti-noise performance,a new approach for identifying power quality disturbances based on an adaptive Kalman filter(KF)and multi-scale channel attention(MS-CAM)fused convolutional neural network is suggested.Single and composite-disruption signals are generated through simulation.The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal,and subsequent integration of multi-scale features into the conventional CNN architecture is conducted.The multi-scale features of the signal are captured by convolution kernels of different sizes so that the model can obtain diverse feature expressions.The attention mechanism(ATT)is introduced to adaptively allocate the extracted features,and the features are fused and selected to obtain the new main features.The Softmax classifier is employed for the classification of power quality disturbances.Finally,by comparing the recognition accuracy of the convolutional neural network(CNN),the model using the attention mechanism,the bidirectional long-term and short-term memory network(MS-Bi-LSTM),and the multi-scale convolutional neural network(MSCNN)with the attention mechanism with the proposed method.The simulation results demonstrate that the proposed method is higher than CNN,MS-Bi-LSTM,and MSCNN,and the overall recognition rate exceeds 99%,and the proposed method has significant classification accuracy and robust classification performance.This achievement provides a new perspective for further exploration in the field of power quality disturbance classification.展开更多
Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and rese...Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and research directions.The future Industrial Internet places higher demands on communication quality.The easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet.Therefore,UAVs are considered as an integral part of Industry 4.0.In this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized.Then,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented.According to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent.Finally,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.展开更多
TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving i...TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.展开更多
This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is ...This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.展开更多
Terahertz time-domain spectroscopy is a kind of far-infrared spectroscopy technology,and its spectrum reflects the internal properties of substances with rich physical and chemical information,so the use of terahertz ...Terahertz time-domain spectroscopy is a kind of far-infrared spectroscopy technology,and its spectrum reflects the internal properties of substances with rich physical and chemical information,so the use of terahertz waves can be used to qualitatively identify food additives containing nitrogen elements.Analytic hierarchy process(AHP)was originally used to solve evaluation-type problems,and this paper introduces it into the field of terahertz spectral qualitative analysis,proposes a terahertz time-domain spectral qualitative identification method combined with analytic hierarchy process,and verifies the feasibility of the method by taking four common food additives(xylitol,L-alanine,sorbic acid,and benzoic acid)and two illegal additives(melamine,and Sudan Red No.I)as the objects of study.Firstly,the collected terahertz time-domain spectral data were pre-processed and transformed into a data set consisting of peaks,peak positions,peak numbers and overall trends;then,the data were divided into comparison and test sets,and a qualitative additive identification model incorporating analytic hierarchy process was constructed and parameter optimisation was performed.The results showed that the qualitative identification accuracies of additives based on single factors,i.e.,overall trend,peak value,peak position,and peak number,were 80.23%,70.93%,67.44%,and 40.70%,respectively,whereas the identification accuracy of the analytic hierarchy process qualitative identification method based on multi-factors could be improved to 92.44%.In addition,the fuzzy characterisation of the absorption spectrum data was binarised in the data pre-processing stage and used as the base data for the overall trend,and the recognition accuracy was improved to 94.19%by combining the fuzzy characterisation method of such data with the hierarchical analysis qualitative recognition model.The results show that it is feasible to use terahertz technology to identify different varieties of additives,and this paper constructs a hierarchical analytical qualitative model with better effect,which provides a new means for food additives detection,and the method is simple in steps,with a small demand for samples,which is suitable for the rapid detection of small samples.展开更多
Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV ...Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.展开更多
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extrac...Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.展开更多
基金funded by the National Natural Science Foundation of China(52167013)the Key Program of Natural Science Foundation of Gansu Province(24JRRA225)Natural Science Foundation of Gansu Province(23JRRA891).
文摘In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
基金funded by the National Natural Science Foundation of China(52167013)the Key Program of Natural Science Foundation of Gansu Province(24JRRA225)Natural Science Foundation of Gansu Province(23JRRA891).
文摘After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and utilized.This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach.At the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy resources.The effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and sustainability.This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management.However,there is still no extensive implementation of such sharing models in today’s electricitymarkets.Research on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market.This paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金partially supported by the National Natural Science Foundation of China(62003097,62121004,62033003,62073019)the Local Innovative and Research Teams Project of Guangdong Special Support Program(2019BT02X353)+2 种基金the Key Area Research and Development Program of Guangdong Province(2021B0101410005)the Joint Funds of Guangdong Basic and Applied Basic Research Foundation(2019A1515110505)。
文摘The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability,the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information.This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.
基金supported in part by the National Natural Science Foundation of China(61621003,62073079,62088101,12025107,11871463,11688101)。
文摘Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFF0708903)Ningbo Municipal Key Technology Research and Development Program of China(Grant No.2022Z006)Youth Fund of National Natural Science Foundation of China(Grant No.52205043)。
文摘Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.
基金partially supported by Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”(2018AAA0100803)the National Natural Science Foundation of China(62350048,T2121003,U1913602,91948204,U20B2071)the Academic Excellence Foundation of BUAA for Ph.D.Students。
文摘The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position model.To guarantee both transient performance and steady-state landing error,a prescribed performance evolution control(PPEC)method is developed for the relative position control.In addition,a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined threshold.Considering the wind and wave on the relative position model,an adaptive sliding mode observer(ASMO)is designed for the disturbance with unknown upper bound.Based on the dynamic surface control framework,a shipboard landing controller integrating PPEC and ASMO is established for the quadrotor,and the relative position control error is guaranteed to be uniformly ultimately bounded.Simulation results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.
基金supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant LY21E070002 and LY17E070002。
文摘Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have much lower torque density/thrust density compared with the same type PM machines.To overcome this challenge,electromagnetic-thermal coupled analysis is carried out with respect to water-cooled electrically-excited flux-switching linear machines(EEFSLM).The simulation results indicate that the conventional fixed copper loss method(FCLM)is no longer suitable for high thrust density design,since it is unable to consider the strong coupling between the electromagnetic and thermal performance.Hence,a multi-step electromagnetic-thermal joint optimisation method is proposed,which first ensures the consistency between the electromagnetic and thermal modelling and then considers the effect of different field/armature coil sizes.By using the proposed joint optimisation method,it is found that the combination of relatively large size of field coil and relatively low field copper loss is favourable for achieving high thrust force for the current EEFSLM design.Moreover,the thrust force is raised by 13-15%compared with using the FCLM.The electromagnetic and thermal performance of the EEFSLM is validated by the prototype test.
文摘Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health care,national defense and daily life.The artificial intelligence techniques are becoming useful as an alternate method of classical techniques or as a component of an integrated system.They are used to solve complicated problems in various fields and becoming increasingly popular nowadays.Especially,the investigation of human brain will promote the artificial intelligence techniques,utilizing the accumulating knowledge of neuroscience,brain-machine interface techniques,algorithms of spiking neural networks and neuromorphic supercomputers.Consequently,we provide a comprehensive survey of the research and motivations for brain-inspired artificial intelligence and its engineering over its history.The goals of this work are to provide a brief review of the research associated with brain-inspired artificial intelligence and its related engineering techniques,and to motivate further work by elucidating challenges in the field where new researches are required.
文摘A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.
基金supported by the National Natural Science Foundation of China(62273213,62073199,62103241)Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)+4 种基金Natural Science Foundation of Shandong Province(ZR2020MF095,ZR2021QF107)Taishan Scholarship Construction Engineeringthe Original Exploratory Program Project of National Natural Science Foundation of China(62250056)Major Basic Research of Natural Science Foundation of Shandong Province(ZR2021ZD14)High-level Talent Team Project of Qingdao West Coast New Area(RCTD-JC-2019-05)。
文摘The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.
基金supported by National Natural Science Foundation of China(Nos.51476073,51266004)Natural Science Foundation of Gansu Province(No.138RJZA199).
文摘With the rapid development of urban rail transit,there have been an urgent problem of excessive stray current.Because the stray current distribution is random and difficult to verify in the field,we designed an improved stray current experimental platform by replacing the simulated aqueous solution with a real soil environment and by calculating the transition resistance by measuring the soil resistivity,which makes up for the defects in the previous references.Firstly,the mathematical models of rail-drainage net and rail-drainage netground were established,and the analytical expressions of current and voltage of rail,drainage net and other structures were derived.In addition,the simulation model was built,and the mathematical analysis results were compared with the simulation results.Secondly,the accuracy of the improved stray current experimental platform was verified by comparing the measured and simulation results.Finally,based on the experimental results,the influence factors of stray current were analyzed.The relevant conclusions provide experimental data and theoretical reference for the study of stray current in urban rail transit.
基金The project is supported by the National Natural Science Foundation of China(52067013)the Key Projects of the Natural Science Foundation of Gansu Provincial Science and Technology Department(22JR5RA318).
文摘In light of the prevailing issue that the existing convolutional neural network(CNN)power quality disturbance identification method can only extract single-scale features,which leads to a lack of feature information and weak anti-noise performance,a new approach for identifying power quality disturbances based on an adaptive Kalman filter(KF)and multi-scale channel attention(MS-CAM)fused convolutional neural network is suggested.Single and composite-disruption signals are generated through simulation.The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal,and subsequent integration of multi-scale features into the conventional CNN architecture is conducted.The multi-scale features of the signal are captured by convolution kernels of different sizes so that the model can obtain diverse feature expressions.The attention mechanism(ATT)is introduced to adaptively allocate the extracted features,and the features are fused and selected to obtain the new main features.The Softmax classifier is employed for the classification of power quality disturbances.Finally,by comparing the recognition accuracy of the convolutional neural network(CNN),the model using the attention mechanism,the bidirectional long-term and short-term memory network(MS-Bi-LSTM),and the multi-scale convolutional neural network(MSCNN)with the attention mechanism with the proposed method.The simulation results demonstrate that the proposed method is higher than CNN,MS-Bi-LSTM,and MSCNN,and the overall recognition rate exceeds 99%,and the proposed method has significant classification accuracy and robust classification performance.This achievement provides a new perspective for further exploration in the field of power quality disturbance classification.
基金supported in part by National Key Research&Devel-opment Program of China(2021YFB2900801)in part by Guangdong Basic and Applied Basic Research Foundation(2022A1515110335)in party by Fundamental Research Funds for the Central Universities(FRF-TP-22-094A1).
文摘Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and research directions.The future Industrial Internet places higher demands on communication quality.The easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet.Therefore,UAVs are considered as an integral part of Industry 4.0.In this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized.Then,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented.According to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent.Finally,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.
文摘TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.
基金supported by the Major Science and Technology Projects in Gansu Province(2023ZDGA005).
文摘This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.
基金funded by Key Technology Tackling Programme of Inner Mongolia,grant number2021GG0361funded by Basic Research Operating Costs of Colleges and Universities Directly Under the Inner Mongolia Autonomous Region Project。
文摘Terahertz time-domain spectroscopy is a kind of far-infrared spectroscopy technology,and its spectrum reflects the internal properties of substances with rich physical and chemical information,so the use of terahertz waves can be used to qualitatively identify food additives containing nitrogen elements.Analytic hierarchy process(AHP)was originally used to solve evaluation-type problems,and this paper introduces it into the field of terahertz spectral qualitative analysis,proposes a terahertz time-domain spectral qualitative identification method combined with analytic hierarchy process,and verifies the feasibility of the method by taking four common food additives(xylitol,L-alanine,sorbic acid,and benzoic acid)and two illegal additives(melamine,and Sudan Red No.I)as the objects of study.Firstly,the collected terahertz time-domain spectral data were pre-processed and transformed into a data set consisting of peaks,peak positions,peak numbers and overall trends;then,the data were divided into comparison and test sets,and a qualitative additive identification model incorporating analytic hierarchy process was constructed and parameter optimisation was performed.The results showed that the qualitative identification accuracies of additives based on single factors,i.e.,overall trend,peak value,peak position,and peak number,were 80.23%,70.93%,67.44%,and 40.70%,respectively,whereas the identification accuracy of the analytic hierarchy process qualitative identification method based on multi-factors could be improved to 92.44%.In addition,the fuzzy characterisation of the absorption spectrum data was binarised in the data pre-processing stage and used as the base data for the overall trend,and the recognition accuracy was improved to 94.19%by combining the fuzzy characterisation method of such data with the hierarchical analysis qualitative recognition model.The results show that it is feasible to use terahertz technology to identify different varieties of additives,and this paper constructs a hierarchical analytical qualitative model with better effect,which provides a new means for food additives detection,and the method is simple in steps,with a small demand for samples,which is suitable for the rapid detection of small samples.
基金supported by the Unveiling and Leading Projects of Gansu Provincial Department of Transportation(JT-JJ-2023-008).
文摘Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.
基金the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216)Lanzhou Science and Technology Program(No.2022-2-111)+1 种基金Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103)Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03)。
文摘Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.
