Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport i...Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.展开更多
Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significa...Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.展开更多
A novel vibration isolation device called the nonlinear energy sink(NES)with NiTiNOL-steel wire ropes(NiTi-ST)is applied to a whole-spacecraft system.The NiTi-ST is used to describe the damping of the NES,which is cou...A novel vibration isolation device called the nonlinear energy sink(NES)with NiTiNOL-steel wire ropes(NiTi-ST)is applied to a whole-spacecraft system.The NiTi-ST is used to describe the damping of the NES,which is coupled with the modified Bouc-Wen model of hysteresis.The NES with NiTi-ST vibration reduction principle uses the irreversibility of targeted energy transfer(TET)to concentrate the energy locally on the nonlinear oscillator,and then dissipates it through damping in the NES with NiTi-ST.The generalized vibration transmissibility,obtained by the root mean square treatment of the harmonic response of the nonlinear output frequency response functions(NOFRFs),is first used as the evaluation index to analyze the whole-spacecraft system in the future.An optimization analysis of the impact of system responses is performed using different parameters of NES with NiTi-ST based on the transmissibility of NOFRFs.Finally,the effects of vibration suppression by varying the parameters of NiTi-ST are analyzed from the perspective of energy absorption.The results indicate that NES with NiTi-ST can reduce excessive vibration of the whole-spacecraft system,without changing its natural frequency.Moreover,the NES with NiTi-ST can be directly used in practical engineering applications.展开更多
In the present study, the Volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetri...In the present study, the Volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetric viscous damping is assembled. The results reveal that the anti-symmetric nonlinear viscous damping can significantly reduce the force trans- missibility over all resonance regions for MDOF structures with little effect on the transmissibility over non-resonant and isolation regions. The results indicate that the vibration isolators with an anti-symmetric damping characteristic have great potential to solve the dilemma occurring in the design of linear viscously damped vibration isolators where an increase of the damping level reduces the force transmissibility over resonant frequencies but increases the transmissibility over non-resonant frequency regions. This work is an extension of a previous study in which MDOF structures installed on the mount through an isolator with cubic nonlinear damping are considered. The theoretical analysis results are also verified by simulation studies.展开更多
The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. On...The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common methodof describing the stress-strain relationship is using constitutive equations, with the unknown parameters fitted byexperimental data obtained via plane strain compression (PSC). Due to the highly nonlinear behaviour of the constitutive equations and the noise included in the PSC data, determination of the model parameters is difficult. Inthis paper, genetic algorithms were exploited to optimise parameters for the constitutive equations based on thePSC data. The original PSC data were processed to generate the stress-strain data, and data pre-processing wascarried out to remove the noise contained in the original PSC data. Several genetic optimisation schemes have beeninvestigated, with different coding schemes and different genetic operators for selection, crossover and mutation.It was found that the real value coded genetic algorithms converged much faster and were more efficient for theparameter optimisation problem.展开更多
Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorith...Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.展开更多
In order to improve the harsh dynamic environment experienced by heavy rockets during different external excitations,this study presents a novel active variable stiffness vibration isolator(AVS-VI)used as the vibratio...In order to improve the harsh dynamic environment experienced by heavy rockets during different external excitations,this study presents a novel active variable stiffness vibration isolator(AVS-VI)used as the vibration isolation device to reduce excessive vibration of the whole-spacecraft isolation system.The AVS-VI is composed of horizontal stiffness spring,positive stiffness spring,parallelogram linkage mechanism,piezoelectric actuator,acceleration sensor,viscoelastic damping,and PID active controller.Based on the AVS-VI,the generalized vibration transmissibility determined by the nonlinear output frequency response functions and the energy absorption rate is applied to analyze the isolation performance of the whole-spacecraft system with AVS-VI.The AVS-VI can conduct adaptive vibration suppression with variable stiffness to the whole-spacecraft system,and the analysis results indicate that the AVS-VI is efTective in reducing the extravagant vibration of the whole-spacecraft system,where the vibration isolation is decreased up to above 65%under different acceleration excitations.Finally,different parameters of AVS-VI are considered to optimize the whole-spacecraft system based on the generalized vibration transmissibility and the energy absorption rate.展开更多
Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. Thi...Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.展开更多
This paper is concerned with the application of forward Orthogonal Least Squares (OLS) algorithm to the design of Finite Impulse Response (FIR) filters. The focus of this study is a new FIR filter design procedure and...This paper is concerned with the application of forward Orthogonal Least Squares (OLS) algorithm to the design of Finite Impulse Response (FIR) filters. The focus of this study is a new FIR filter design procedure and to compare this with traditional methods known as the fir2() routine provided by MATLAB.展开更多
An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper.Firstly,an integrated observer framework is es...An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper.Firstly,an integrated observer framework is established for mechanical parameters’estimation,which consists of an extended sliding mode observer(ESMO)and a Luenberger observer.Aiming at minimizing the influence of parameters coupling,the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately.After obtaining estimates of the mechanical parameters,the optimal proportional integral(PI)parameters of the speed-loop are determined according to third-order best design method.As a result,the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system.Meanwhile,the disturbance is compensated according to the estimates.Finally,the experiments were carried out on simulation platform,and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.展开更多
This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune...This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.展开更多
Technologies that providemechanical assistance arerequired inthemdicalfeld,such asimplants that regenerate tssuethroughelongation and stimulation.One ofthe challenges is to develop actuators that combinethe benefits o...Technologies that providemechanical assistance arerequired inthemdicalfeld,such asimplants that regenerate tssuethroughelongation and stimulation.One ofthe challenges is to develop actuators that combinethe benefits ofhigh axialextension at lowpressures,modularity,multifunction,and load bearing capabilities into one design while maintaining their shape and softness.Overcoming such a challenge wll provide implants with enhanced capacity for mechanical assistance to induce tisueregeneration.We introduce two novel actuators(M2H)built of stacked Hyperelastic Balloning Membrane Actuators(HBMAs)that can be realized using helical and toroidal configurations.By restraining the HBMA expansion deterministicallyusing a semisoft exoskeleton,the actuatorsors areendoweded withh axial extension and radial expansion capabilties.These actuatorsare thus built of modules that canconfiguured different therapeutical needs and multifunctionality,to provideanatomically congruent stimulation.presentthe desigl,fabricationtestin;and numerical and experimental validation ofthe M2H-HBMAs.They can aaxiallyind6 in their helicaland toroidal configurations at input pressuresas low as 26 and 24 kPa,respectively.If the axial module is used separately,its extension capacity reaches>170%.The M2H-HBMAs can perform independent and simultaneousxpansion and extension motions with negligible intraluminaldeformation as well as stand at least 1kg of axial force without collapsing.The M2H-HBMAs overcome the limitations ofhyperexpanding machines that show low resistance to load.We envisage M2H-HBMAs as promising tools to perform tisueregeneration procedures.展开更多
Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accur...Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.展开更多
The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work.The MSRBF networ...The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work.The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems.In this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer.In the first stage,the image data is rendered into a multiple-input-single-output(MISO)system.In order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear way.The forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network.In the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection.To test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor.Classification results show that the new CAD method reached an accuracy of 93.5%in mini-Mammo graphic image analysis society(mini-MIAS),93.99%in digital database for screening mammography(DDSM)and 86.7%in the BreaKHis.We found that the MSRBF networks are able to build tailored and precise image models and,combined with the DCT,to extract high-quality features from both black and white and coloured images.展开更多
The disability,mortality and costs due to ionizing radiation(IR)-induced osteoporotic bone fractures are sub-stantial and no effective therapy exists.Ionizing radiation increases cellular oxidative damage,causing an i...The disability,mortality and costs due to ionizing radiation(IR)-induced osteoporotic bone fractures are sub-stantial and no effective therapy exists.Ionizing radiation increases cellular oxidative damage,causing an imbalance in bone turnover that is primarily driven via heightened activity of the bone-resorbing osteoclast.We demonstrate that rats exposed to sublethal levels of IR develop fragile,osteoporotic bone.At reactive surface sites,cerium ions have the ability to easily undergo redox cycling:drastically adjusting their electronic con-figurations and versatile catalytic activities.These properties make cerium oxide nanomaterials fascinating.We show that an engineered artificial nanozyme composed of cerium oxide,and designed to possess a higher fraction of trivalent(Ce^(3+))surface sites,mitigates the IR-induced loss in bone area,bone architecture,and strength.These investigations also demonstrate that our nanozyme furnishes several mechanistic avenues of protection and selectively targets highly damaging reactive oxygen species,protecting the rats against IR-induced DNA damage,cellular senescence,and elevated osteoclastic activity in vitro and in vivo.Further,we reveal that our nanozyme is a previously unreported key regulator of osteoclast formation derived from macrophages while also directly targeting bone progenitor cells,favoring new bone formation despite its exposure to harmful levels of IR in vitro.These findings open a new approach for the specific prevention of IR-induced bone loss using synthesis-mediated designer multifunctional nanomaterials.展开更多
Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed du...Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.展开更多
Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of pred...Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.展开更多
The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential dam...The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion path.Recently,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training efficiency.In this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment.Furthermore,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm.Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms.Tests showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,respectively.Moreover,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.展开更多
基金This work was funded by the UK Engineering and Physical Sciences Research Council(EP/N029496/1,EP/N029496/2,EP/N029356/1,EP/N029577/1,and EP/N029577/2).
文摘Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.
文摘Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.
基金Project supported by the National Natural Science Foundation of China(No.11772205)the Scientific Research Fund of Liaoning Provincial Education Department(No.L201703)+1 种基金the Liaoning Revitalization Talent Program(No.XLYC1807172)the Training Project of Liaoning Higher Education Institutions in Domestic and Overseas(No.2018LNGXGJWPY-YB008)
文摘A novel vibration isolation device called the nonlinear energy sink(NES)with NiTiNOL-steel wire ropes(NiTi-ST)is applied to a whole-spacecraft system.The NiTi-ST is used to describe the damping of the NES,which is coupled with the modified Bouc-Wen model of hysteresis.The NES with NiTi-ST vibration reduction principle uses the irreversibility of targeted energy transfer(TET)to concentrate the energy locally on the nonlinear oscillator,and then dissipates it through damping in the NES with NiTi-ST.The generalized vibration transmissibility,obtained by the root mean square treatment of the harmonic response of the nonlinear output frequency response functions(NOFRFs),is first used as the evaluation index to analyze the whole-spacecraft system in the future.An optimization analysis of the impact of system responses is performed using different parameters of NES with NiTi-ST based on the transmissibility of NOFRFs.Finally,the effects of vibration suppression by varying the parameters of NiTi-ST are analyzed from the perspective of energy absorption.The results indicate that NES with NiTi-ST can reduce excessive vibration of the whole-spacecraft system,without changing its natural frequency.Moreover,the NES with NiTi-ST can be directly used in practical engineering applications.
基金supported by the EPSRC (UK)the National Science Fund for Distinguished Young Scholars (11125209)the National Natural Science Foundation of China (10902068 and 51121063)
文摘In the present study, the Volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetric viscous damping is assembled. The results reveal that the anti-symmetric nonlinear viscous damping can significantly reduce the force trans- missibility over all resonance regions for MDOF structures with little effect on the transmissibility over non-resonant and isolation regions. The results indicate that the vibration isolators with an anti-symmetric damping characteristic have great potential to solve the dilemma occurring in the design of linear viscously damped vibration isolators where an increase of the damping level reduces the force transmissibility over resonant frequencies but increases the transmissibility over non-resonant frequency regions. This work is an extension of a previous study in which MDOF structures installed on the mount through an isolator with cubic nonlinear damping are considered. The theoretical analysis results are also verified by simulation studies.
文摘The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common methodof describing the stress-strain relationship is using constitutive equations, with the unknown parameters fitted byexperimental data obtained via plane strain compression (PSC). Due to the highly nonlinear behaviour of the constitutive equations and the noise included in the PSC data, determination of the model parameters is difficult. Inthis paper, genetic algorithms were exploited to optimise parameters for the constitutive equations based on thePSC data. The original PSC data were processed to generate the stress-strain data, and data pre-processing wascarried out to remove the noise contained in the original PSC data. Several genetic optimisation schemes have beeninvestigated, with different coding schemes and different genetic operators for selection, crossover and mutation.It was found that the real value coded genetic algorithms converged much faster and were more efficient for theparameter optimisation problem.
文摘Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.
基金the National Natural Science Foundation of China(Project Nos.12022213,11772205 and 11902203)the Scieatifie Research Fund of Liaoning Provineinl Education Department(No.L201703)+1 种基金the Program of Liaoning Revitalization Talents(XLYC1807172)the Tralning Project of Liaoning Higher Education Institutions in Domestic and Oveseas(Nos.2018LNGXGJWPY-YB008).
文摘In order to improve the harsh dynamic environment experienced by heavy rockets during different external excitations,this study presents a novel active variable stiffness vibration isolator(AVS-VI)used as the vibration isolation device to reduce excessive vibration of the whole-spacecraft isolation system.The AVS-VI is composed of horizontal stiffness spring,positive stiffness spring,parallelogram linkage mechanism,piezoelectric actuator,acceleration sensor,viscoelastic damping,and PID active controller.Based on the AVS-VI,the generalized vibration transmissibility determined by the nonlinear output frequency response functions and the energy absorption rate is applied to analyze the isolation performance of the whole-spacecraft system with AVS-VI.The AVS-VI can conduct adaptive vibration suppression with variable stiffness to the whole-spacecraft system,and the analysis results indicate that the AVS-VI is efTective in reducing the extravagant vibration of the whole-spacecraft system,where the vibration isolation is decreased up to above 65%under different acceleration excitations.Finally,different parameters of AVS-VI are considered to optimize the whole-spacecraft system based on the generalized vibration transmissibility and the energy absorption rate.
文摘Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.
文摘This paper is concerned with the application of forward Orthogonal Least Squares (OLS) algorithm to the design of Finite Impulse Response (FIR) filters. The focus of this study is a new FIR filter design procedure and to compare this with traditional methods known as the fir2() routine provided by MATLAB.
基金the National Key Research and Development Project of China(No.2019YFE0105300)the National Natural Science Foundation of China(Nos.61972443 and 62103143)+1 种基金the Hunan Provincial Key Research and Development Project of China(No.2022WK2006)the Hunan Provincial Hu-Xiang Young Talents Project of China(No.2018RS3095).
文摘An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper.Firstly,an integrated observer framework is established for mechanical parameters’estimation,which consists of an extended sliding mode observer(ESMO)and a Luenberger observer.Aiming at minimizing the influence of parameters coupling,the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately.After obtaining estimates of the mechanical parameters,the optimal proportional integral(PI)parameters of the speed-loop are determined according to third-order best design method.As a result,the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system.Meanwhile,the disturbance is compensated according to the estimates.Finally,the experiments were carried out on simulation platform,and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.
基金Project (No. 60073034) supported by the National Natural Sci-ence Foundation of China
文摘This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.
基金supported bythe Engineering and Physical Sciences Research Council(EP/S021035/1)the National Council of Science andTechnology of Mexico(536276).
文摘Technologies that providemechanical assistance arerequired inthemdicalfeld,such asimplants that regenerate tssuethroughelongation and stimulation.One ofthe challenges is to develop actuators that combinethe benefits ofhigh axialextension at lowpressures,modularity,multifunction,and load bearing capabilities into one design while maintaining their shape and softness.Overcoming such a challenge wll provide implants with enhanced capacity for mechanical assistance to induce tisueregeneration.We introduce two novel actuators(M2H)built of stacked Hyperelastic Balloning Membrane Actuators(HBMAs)that can be realized using helical and toroidal configurations.By restraining the HBMA expansion deterministicallyusing a semisoft exoskeleton,the actuatorsors areendoweded withh axial extension and radial expansion capabilties.These actuatorsare thus built of modules that canconfiguured different therapeutical needs and multifunctionality,to provideanatomically congruent stimulation.presentthe desigl,fabricationtestin;and numerical and experimental validation ofthe M2H-HBMAs.They can aaxiallyind6 in their helicaland toroidal configurations at input pressuresas low as 26 and 24 kPa,respectively.If the axial module is used separately,its extension capacity reaches>170%.The M2H-HBMAs can perform independent and simultaneousxpansion and extension motions with negligible intraluminaldeformation as well as stand at least 1kg of axial force without collapsing.The M2H-HBMAs overcome the limitations ofhyperexpanding machines that show low resistance to load.We envisage M2H-HBMAs as promising tools to perform tisueregeneration procedures.
基金by National Natural Science Foundation of China(No.61972443)National Key Research and Development Plan Program of China(No.2019YFE0105300)+1 种基金Hunan Provincial Hu-Xiang Young Talents Project of China(No.2018RS3095)Hunan Provincial Natural Science Foundation of China(No.2020JJ5199).
文摘Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
基金the financial support to Carlos Beltran-Perez from the Mexican National Council of Science and Technology (CONACYT)part of the work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/I011056/1 and platform grant EP/H00453X/1
文摘The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work.The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems.In this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer.In the first stage,the image data is rendered into a multiple-input-single-output(MISO)system.In order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear way.The forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network.In the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection.To test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor.Classification results show that the new CAD method reached an accuracy of 93.5%in mini-Mammo graphic image analysis society(mini-MIAS),93.99%in digital database for screening mammography(DDSM)and 86.7%in the BreaKHis.We found that the MSRBF networks are able to build tailored and precise image models and,combined with the DCT,to extract high-quality features from both black and white and coloured images.
基金University of Central Florida(ER Award:#25089A06)We would also like to acknowledge the National Science Foundation(NSF)Major Research Instrumentation(MRI)Program(Grant ID:ECCS:1726636)for the XPS measurements presented in this manuscript+6 种基金MM acknowledges the University of Huddersfield(UoH)EPSRC-DTP competition 2018–19(EP/R513234/1)for funding SMVice Chancellor’s Scholarship Scheme for funding KMTAnalysis was performed on the Orion computing facility at the UoH.Calculations were run on the ARCHER and ARCHER2 UK National Supercomputing Services via our membership of the UK HEC Materials Chemistry Consortium(MCCEPSRC EP/L000202,EP/R029431)AA acknowledges NIH NCI(Grant R01CA045424),Research Excellence Fund(REF)Center for Biomedical Research for support.AA also acknowledges the National Science Foundation(NSF)instrumentation award(CHE-1920110)JA’s work was supported by the National Aeronautics and Space Administration[grant No.80NSSC21M0309]issued through the NASA Office of STEM Engagement.
文摘The disability,mortality and costs due to ionizing radiation(IR)-induced osteoporotic bone fractures are sub-stantial and no effective therapy exists.Ionizing radiation increases cellular oxidative damage,causing an imbalance in bone turnover that is primarily driven via heightened activity of the bone-resorbing osteoclast.We demonstrate that rats exposed to sublethal levels of IR develop fragile,osteoporotic bone.At reactive surface sites,cerium ions have the ability to easily undergo redox cycling:drastically adjusting their electronic con-figurations and versatile catalytic activities.These properties make cerium oxide nanomaterials fascinating.We show that an engineered artificial nanozyme composed of cerium oxide,and designed to possess a higher fraction of trivalent(Ce^(3+))surface sites,mitigates the IR-induced loss in bone area,bone architecture,and strength.These investigations also demonstrate that our nanozyme furnishes several mechanistic avenues of protection and selectively targets highly damaging reactive oxygen species,protecting the rats against IR-induced DNA damage,cellular senescence,and elevated osteoclastic activity in vitro and in vivo.Further,we reveal that our nanozyme is a previously unreported key regulator of osteoclast formation derived from macrophages while also directly targeting bone progenitor cells,favoring new bone formation despite its exposure to harmful levels of IR in vitro.These findings open a new approach for the specific prevention of IR-induced bone loss using synthesis-mediated designer multifunctional nanomaterials.
基金This work was supported in part by the National Key Research and Development Project of China(No.2019YFE0105300)the National Natural Science Foundation of China(No.61972443)the Hunan Provincial Key Research and Development Project of China(No.2022WK2006).
文摘Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.
文摘Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.
基金supported by the Shenzhen Science and Technology Program(grant no.RCYX202-00714114736115 and ZDSYS20211021111415025)Shenzhen Institute of AI and Robotics for Society(grant no.AC01202101112).
文摘The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion path.Recently,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training efficiency.In this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment.Furthermore,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm.Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms.Tests showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,respectively.Moreover,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.
