Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.Th...Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.展开更多
Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),a...Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.展开更多
With increasing reforms related to integrated energy systems(IESs),each energy subsystem,as a participant based on bounded rationality,significantly influences the optimal scheduling of the entire IES through mutual l...With increasing reforms related to integrated energy systems(IESs),each energy subsystem,as a participant based on bounded rationality,significantly influences the optimal scheduling of the entire IES through mutual learning and imitation.A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives.This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation.The studied IES includes cogeneration,power-to-gas,and carbon capture systems.Based on the Stackelberg master-slave game theory,sellers are used as leaders in the upper layer to set the prices of electricity and heat,while energy producers,energy storage providers,and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system.An IES bilayer optimization model based on the Stackelberg master-slave game was developed.Finally,the Karush-Kuhn-Tucker(KKT)condition and linear relaxation technology are used to convert the bilayer game model to a single layer.CPLEX,which is a mathematical program solver,is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system.As an experimental demonstration,we simulated an IES coupled with an IEEE 39-node electrical grid system,a six-node heat network system,and a six-node gas network system.The simulation results confirm the effectiveness and feasibility of the proposed model.展开更多
The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns o...The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.展开更多
In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power su...In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.展开更多
Permanent magnet synchronous motors(PMSMs)have been widely employed in the industry. Finite-control-set model predictive control(FCS-MPC), as an advanced control scheme, has been developed and applied to improve the p...Permanent magnet synchronous motors(PMSMs)have been widely employed in the industry. Finite-control-set model predictive control(FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.展开更多
Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and...Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.展开更多
Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,...Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,the conventional MPCs have various issues,including unsatisfactory steady-state performance,variable switching frequency,and difficult selection of appropriate weighting factors.This paper proposes two different improved MPC methods to deal with these issues.One method is the two-vector dimensionless model predictive torque control(MPTC).Two cost functions(torque and flux) and fuzzy decision-making are used to eliminate the weighting factor and select the first optimum vector.The torque cost function selects a second vector whose duty cycle is determined based on the torque error.The other method is the two-vector dimensionless model predictive current control(MPCC).The first vector is selected the same as in the conventional MPC method.Two separate current cost functions and fuzzy decision-making are used to select the second vector whose duty cycle is determined based on the current error.Both proposed methods utilize the space vector PWM modulator to regulate the switching frequency.Numerical simulation results show that the proposed methods have better steady-state and transient performances than the conventional MPCs and other existing improved MPCs.展开更多
Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective funct...Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.展开更多
This paper reveals a new design of UHF CubeSat antenna based on a modified Planar Inverted F Antenna(PIFA)for CubeSat communication.The design utilizes a CubeSat face as the ground plane.There is a gap of 5 mm beneath...This paper reveals a new design of UHF CubeSat antenna based on a modified Planar Inverted F Antenna(PIFA)for CubeSat communication.The design utilizes a CubeSat face as the ground plane.There is a gap of 5 mm beneath the radiating element that facilitates the design providing with space for solar panels.The prototype is fabricated using Aluminum metal sheet and measured.The antenna achieved resonance at 419 MHz.Response of the antenna has been investigated after placing a solar panel.Lossy properties of solar panels made the resonance shift about 20 MHz.This design addresses the frequency shifting issue after placing the antenna with the CubeSat body.This phenomenon has been analyzed considering a typical 1U and 2U CubeSat body with the antenna.The antenna achieved a positive realized gain of 0.7 dB and approximately 78%of efficiency at the resonant frequency with providing 85%of open space for solar irradiance onto the solar panel.展开更多
The performance of traditional flux switching permanent magnet tubular machine(FSPMTM)are improved by using new material and structure in this paper.The existing silicon steel sheet making for all mover cores or part ...The performance of traditional flux switching permanent magnet tubular machine(FSPMTM)are improved by using new material and structure in this paper.The existing silicon steel sheet making for all mover cores or part of stator cores are replaced by soft magnetic composite(SMC)cores,and the lamination direction of the silicon steel sheet in stator cores have be changed.The eddy current loss of the machine with hybrid cores will be reduced greatly as the magnetic flux will not pass through the silicon steel sheet vertically.In order to reduce the influence of end effect,the unequal stator width design method is proposed.With the new design,the symmetry of the permanent magnet flux linkage has been improved greatly and the cogging force caused by the end effect has been reduced.Both 2-D and 3-D finite element methods(FEM)are applied for the quantitative analysis.展开更多
This paper proposes a new rotary flux switching transverse flux machine with the ability of linear motion(FSTFMaLM),in which both the stator and the rotor cores are made by using soft magnetic composite(SMC)materials....This paper proposes a new rotary flux switching transverse flux machine with the ability of linear motion(FSTFMaLM),in which both the stator and the rotor cores are made by using soft magnetic composite(SMC)materials.With the special design pattern,for the rotary motion model,the proposed machine can combine both the advantages of the flux switching permanent magnet machine(FSPMM)and the transverse flux machine(TFM).It can output with relatively high torque density,and as there is no windings or the magnets on the rotor cores,the proposed machine can operate in the high speed region to improve the output power.With the adoption of the SMC materials,the manufacturing of this machine can be quite easy.By stacking the rotor core together and prolong it with the determined length in the axial direction,in addition with the special control algorithm,the proposed machine can have the ability of the linear motion.In this paper,the operation principle of this machine has been explained and the design methods are also presented.To seek the better performance,the main dimension of the machine is optimized,and for the performance evaluation,the finite element method(FEM)is adopted.The proposed machine can be used for the electric driving systems,robotic systems or other applications where the linear motion ability is required.展开更多
This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnos...This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip development.The localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising characteristics.Unlike traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic chamber.Moreover,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane waveguide.Experimental demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS channels.These experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.展开更多
The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or ...The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network connectivity.To address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction.In light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels.The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood.Besides,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative environment.The proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River inMalaysia.The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.展开更多
Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO_(2) emissions.The structure of these technologies relies on the dee...Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO_(2) emissions.The structure of these technologies relies on the deep inte-gration of advanced data-driven techniques which can ensure efficient energy generation,transmission,and distribu-tion.After conducting thorough research for more than a decade,the concept of the smart grid(SG)has emerged,and its practice around the world paves the ways for efficient use of reliable energy technology.However,many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid(NGSG).Also,to deal with the non-linearity and uncertainty,the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity.This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation,including intelligent control,agent-based energy conversion,edge computing for energy management,internet of things(IoT)enabled inverter,agent-oriented demand side management,etc.Also,a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques(DDTs)for the sustainable operation of the SG.The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering,technology,et al.Finally,the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied,while some major future issues are highlighted.This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.展开更多
Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by grad...Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.展开更多
Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in t...Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.展开更多
A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially ...A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ridethrough capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system ongrid operation performance is verified.展开更多
This paper presents a smart electrical car park model where the power flows among electrical vehicles(EVs)as well as between EVs and the main grid.Based on this model,an optimal charging/discharging scheme is proposed...This paper presents a smart electrical car park model where the power flows among electrical vehicles(EVs)as well as between EVs and the main grid.Based on this model,an optimal charging/discharging scheme is proposed.The fluctuation of hourly electricity rates is considered in this strategy to select a proper charging/discharging rate for each EV with less expenditure during each charging period.The proposed smart electrical car park is able to buy or sell electricity in the form of active and/or reactive power,i.e.kWh and/or kVARh,from or to the main grid to improve the power quality.According to the current state of charge of the EV’s battery bank,customers and the grid demands,a control center makes the decisions and sends the instructions of specific charging/discharging mode to each charging station.The performance of the proposed charging/discharging algorithm is simulated in Matlab.A comparison between the proposed and the unregulated charging/discharging strategies has been implemented.The results demonstrate that the proposed scheme can achieve better economic profits for EV customers and increase the commercial benefits for the car park owner.展开更多
Narrowband red,green,blue self-filtering perovskite photodetectors and a broadband white photodetector are incorporated into a single pixel imaging camera to mimic the long-,medium-,and short-wavelength cone cells and...Narrowband red,green,blue self-filtering perovskite photodetectors and a broadband white photodetector are incorporated into a single pixel imaging camera to mimic the long-,medium-,and short-wavelength cone cells and rod cells in human visual system,leading to the demonstration of high-resolution color images in diffuse mode.展开更多
文摘Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.
基金supported by the University of Technology Sydney (UTS) Chancellor’s Postdoctoral Fellowship (PRO18-6147)Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) (PRO20-9959)
文摘Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.
基金supported by the National Natural Science Foundation of China(Grant No.62063016)。
文摘With increasing reforms related to integrated energy systems(IESs),each energy subsystem,as a participant based on bounded rationality,significantly influences the optimal scheduling of the entire IES through mutual learning and imitation.A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives.This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation.The studied IES includes cogeneration,power-to-gas,and carbon capture systems.Based on the Stackelberg master-slave game theory,sellers are used as leaders in the upper layer to set the prices of electricity and heat,while energy producers,energy storage providers,and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system.An IES bilayer optimization model based on the Stackelberg master-slave game was developed.Finally,the Karush-Kuhn-Tucker(KKT)condition and linear relaxation technology are used to convert the bilayer game model to a single layer.CPLEX,which is a mathematical program solver,is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system.As an experimental demonstration,we simulated an IES coupled with an IEEE 39-node electrical grid system,a six-node heat network system,and a six-node gas network system.The simulation results confirm the effectiveness and feasibility of the proposed model.
文摘The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.
基金supported by the National Natural Science Foundation of China(Grant No.62063016).
文摘In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.
基金supported in part by the National Natural Science Foundation of China(51875261)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX21_3331)+1 种基金the Faculty of Agricultural Equipment of Jiangsu University(NZXB20210103)。
文摘Permanent magnet synchronous motors(PMSMs)have been widely employed in the industry. Finite-control-set model predictive control(FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.
文摘Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.
文摘Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,the conventional MPCs have various issues,including unsatisfactory steady-state performance,variable switching frequency,and difficult selection of appropriate weighting factors.This paper proposes two different improved MPC methods to deal with these issues.One method is the two-vector dimensionless model predictive torque control(MPTC).Two cost functions(torque and flux) and fuzzy decision-making are used to eliminate the weighting factor and select the first optimum vector.The torque cost function selects a second vector whose duty cycle is determined based on the torque error.The other method is the two-vector dimensionless model predictive current control(MPCC).The first vector is selected the same as in the conventional MPC method.Two separate current cost functions and fuzzy decision-making are used to select the second vector whose duty cycle is determined based on the current error.Both proposed methods utilize the space vector PWM modulator to regulate the switching frequency.Numerical simulation results show that the proposed methods have better steady-state and transient performances than the conventional MPCs and other existing improved MPCs.
基金Supported by the National Natural Science Foundation of China under Grant No 61671142the Fundamental Research Funds for the Central Universities under Grant No 02190022117021
文摘Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
文摘This paper reveals a new design of UHF CubeSat antenna based on a modified Planar Inverted F Antenna(PIFA)for CubeSat communication.The design utilizes a CubeSat face as the ground plane.There is a gap of 5 mm beneath the radiating element that facilitates the design providing with space for solar panels.The prototype is fabricated using Aluminum metal sheet and measured.The antenna achieved resonance at 419 MHz.Response of the antenna has been investigated after placing a solar panel.Lossy properties of solar panels made the resonance shift about 20 MHz.This design addresses the frequency shifting issue after placing the antenna with the CubeSat body.This phenomenon has been analyzed considering a typical 1U and 2U CubeSat body with the antenna.The antenna achieved a positive realized gain of 0.7 dB and approximately 78%of efficiency at the resonant frequency with providing 85%of open space for solar irradiance onto the solar panel.
基金This work was supported in part by the National Natural Science Foundation of China under project 51877065Hebei Province Education Department Youth Talent Leading Project under grant BJ2018037in part by the State Key Laboratory of Reliability and Intelligence of Electrical Equipment under grant EERIKF2018005.
文摘The performance of traditional flux switching permanent magnet tubular machine(FSPMTM)are improved by using new material and structure in this paper.The existing silicon steel sheet making for all mover cores or part of stator cores are replaced by soft magnetic composite(SMC)cores,and the lamination direction of the silicon steel sheet in stator cores have be changed.The eddy current loss of the machine with hybrid cores will be reduced greatly as the magnetic flux will not pass through the silicon steel sheet vertically.In order to reduce the influence of end effect,the unequal stator width design method is proposed.With the new design,the symmetry of the permanent magnet flux linkage has been improved greatly and the cogging force caused by the end effect has been reduced.Both 2-D and 3-D finite element methods(FEM)are applied for the quantitative analysis.
基金This work was supported in part by the National Natural Science Foundation of China under project 51877065Hebei Province Education Department Youth Talent Leading Project under grant BJ2018037.
文摘This paper proposes a new rotary flux switching transverse flux machine with the ability of linear motion(FSTFMaLM),in which both the stator and the rotor cores are made by using soft magnetic composite(SMC)materials.With the special design pattern,for the rotary motion model,the proposed machine can combine both the advantages of the flux switching permanent magnet machine(FSPMM)and the transverse flux machine(TFM).It can output with relatively high torque density,and as there is no windings or the magnets on the rotor cores,the proposed machine can operate in the high speed region to improve the output power.With the adoption of the SMC materials,the manufacturing of this machine can be quite easy.By stacking the rotor core together and prolong it with the determined length in the axial direction,in addition with the special control algorithm,the proposed machine can have the ability of the linear motion.In this paper,the operation principle of this machine has been explained and the design methods are also presented.To seek the better performance,the main dimension of the machine is optimized,and for the performance evaluation,the finite element method(FEM)is adopted.The proposed machine can be used for the electric driving systems,robotic systems or other applications where the linear motion ability is required.
基金supported by A*STAR under the“Nanosystems at the Edge”programme(Grant No.A18A4b0055).
文摘This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip development.The localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising characteristics.Unlike traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic chamber.Moreover,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane waveguide.Experimental demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS channels.These experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.
基金This work was supported by Ministry of Higher Education,Fundamental Research Grant Scheme,Vote Number 21H14,and Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia(Grant ID:GGPM-2020-029 and Grant ID:PPFTSM-2020).
文摘The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network connectivity.To address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction.In light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels.The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood.Besides,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative environment.The proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River inMalaysia.The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.
文摘Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO_(2) emissions.The structure of these technologies relies on the deep inte-gration of advanced data-driven techniques which can ensure efficient energy generation,transmission,and distribu-tion.After conducting thorough research for more than a decade,the concept of the smart grid(SG)has emerged,and its practice around the world paves the ways for efficient use of reliable energy technology.However,many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid(NGSG).Also,to deal with the non-linearity and uncertainty,the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity.This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation,including intelligent control,agent-based energy conversion,edge computing for energy management,internet of things(IoT)enabled inverter,agent-oriented demand side management,etc.Also,a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques(DDTs)for the sustainable operation of the SG.The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering,technology,et al.Finally,the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied,while some major future issues are highlighted.This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.
基金This work was supported in part by the National Key R&D Program of China(No.2018YFB2100400)in part by the National Natural Science Foundation of China(No.62002077,61872100)+2 种基金in part by the China Postdoctoral Science Foundation(No.2020M682657)in part by Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110385)in part by Zhejiang Lab(No.2020NF0AB01),in part by Guangzhou Science and Technology Plan Project(202102010440).
文摘Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.
基金funding from the Humanities and Social Sciences Projects of the Ministry of Education(Grant No.18YJC760112,Bin Yang)the Social Science Fund of Jiangsu Province(Grant No.18YSD002,Bin Yang)Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)(Grant No.kfj180402,Lingyun Xiang).
文摘Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.
基金supported by the National Natural Science Foundation of China (No. 52005306)the Shandong Provincial Natural Science Foundation (No. ZR2020QE220)the Open Fund of Key Laboratory of Modern Power Simulation and Control&Renewable Energy Technology,Ministry of Education,Northeast Electric Power University (No. MPSS2022-02)。
文摘A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ridethrough capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system ongrid operation performance is verified.
文摘This paper presents a smart electrical car park model where the power flows among electrical vehicles(EVs)as well as between EVs and the main grid.Based on this model,an optimal charging/discharging scheme is proposed.The fluctuation of hourly electricity rates is considered in this strategy to select a proper charging/discharging rate for each EV with less expenditure during each charging period.The proposed smart electrical car park is able to buy or sell electricity in the form of active and/or reactive power,i.e.kWh and/or kVARh,from or to the main grid to improve the power quality.According to the current state of charge of the EV’s battery bank,customers and the grid demands,a control center makes the decisions and sends the instructions of specific charging/discharging mode to each charging station.The performance of the proposed charging/discharging algorithm is simulated in Matlab.A comparison between the proposed and the unregulated charging/discharging strategies has been implemented.The results demonstrate that the proposed scheme can achieve better economic profits for EV customers and increase the commercial benefits for the car park owner.
文摘Narrowband red,green,blue self-filtering perovskite photodetectors and a broadband white photodetector are incorporated into a single pixel imaging camera to mimic the long-,medium-,and short-wavelength cone cells and rod cells in human visual system,leading to the demonstration of high-resolution color images in diffuse mode.
