The axial field hybrid permanent magnet memory machine(AFHPM-MM)employs a hybrid permanent magnet excitation combining NdFeB and AlNiCo,achieving high torque density and a wide flux adjustment range.A separated stator...The axial field hybrid permanent magnet memory machine(AFHPM-MM)employs a hybrid permanent magnet excitation combining NdFeB and AlNiCo,achieving high torque density and a wide flux adjustment range.A separated stator structure is adopted to enhance its antidemagnetization capability.To analyze the contributions of AlNiCo and NdFeB to the induced electromotive force(EMF)in the AFHPM-MM,a frozen permeability-based induced EMF calculation method is proposed.Theoretical analysis reveals that the conventional method exhibits substantial errors in calculating the AlNiCo-induced EMF,primarily attributed to its failure to adequately account for the dynamic magnetization characteristic discrepancies of AlNiCo under varying magnetization states.Through the analysis of magnetization variations in AlNiCo during the flux adjustment process under different magnetization states,an improved induced EMF calculation method is proposed.Comparative results indicate that,during the flux enhancement process,the average calculation error of the AlNiCo-induced EMF is reduced from 19.84%to 2.09%,whereas during the flux weakening process,the error is reduced from 3.87%to 1.67%.The proposed method achieves accurate induced EMF calculation for the AFHPM-MM.展开更多
In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies...In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.展开更多
This paper overviews the recent advances in variable flux memory machines(VFMMs)for traction applications with particular reference to newly emerged machine topologies and related control strategies.Due to the use of ...This paper overviews the recent advances in variable flux memory machines(VFMMs)for traction applications with particular reference to newly emerged machine topologies and related control strategies.Due to the use of flux memorable low coercive force(LCF)magnets,the air-gap flux of VFMM can be flexibly varied via a magnetizing current pulse.Thus,the copper loss associated with the flux weakening current and high-speed iron loss can be significantly reduced,and hence high efficiency can be achieved over a wide speed and torque/power operation.These merits make VFMM potentially attractive for electric vehicle(EV)applications.Various novel VFMMs are reviewed with particular reference to their topologies,working principle,characteristics and related control techniques.In order to tackle the drawbacks in the existing VFMMs,some new designs are introduced for performance improvement.Then,the electromagnetic characteristics of an exemplified EV-scaled switched flux memory machine and various benchmark traction machine choices,such as induction machine,synchronous reluctance machines,as well as commercially available Prius 2010 interior permanent magnet(IPM)machine are compared.Finally,the key challenges and development trends of VFMM are highlighted,respectively.展开更多
Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,su...Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.展开更多
With the rapid development of big data and artificial intelligence(AI),the cloud platform architecture system is constantly developing,optimizing,and improving.As such,new applications,like deep computing and high-per...With the rapid development of big data and artificial intelligence(AI),the cloud platform architecture system is constantly developing,optimizing,and improving.As such,new applications,like deep computing and high-performance computing,require enhanced computing power.To meet this requirement,a non-uniform memory access(NUMA)configuration method is proposed for the cloud computing system according to the affinity,adaptability,and availability of the NUMA architecture processor platform.The proposed method is verified based on the test environment of a domestic central processing unit(CPU).展开更多
Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurat...Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurately detect those hidden processes by analyzing memory data.WVMI dumps in-memory data of the target Windows operating systems from hypervisor and retrieves EPROCESS structures’address of process linked list first,and then generates Data Type Confidence Table（DTCT）.Next,it traverses the memory and identifies the similarities between the nodes in process linked list and the corresponding segments in the memory by utilizing DTCT.Finally,it locates the segments of Windows’EPROCESS and identifies the hidden processes by further comparison.Through extensive experiments,our experiment shows that the WVMI detects the hidden process with high identification rate,and it is independent of different versions of Windows operating system.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
Flux adjustable permanent magnet machines(FAPMMs)are a novel type of permanent magnet(PM)machines which are able to flexibly adjust the field excitation flux linkage and offer the distinctive advantages of high power ...Flux adjustable permanent magnet machines(FAPMMs)are a novel type of permanent magnet(PM)machines which are able to flexibly adjust the field excitation flux linkage and offer the distinctive advantages of high power density and high efficiency.They have attracted ever-increasing interests and are promising candidate machines for electric vehicle,machine-tool and aircraft applications.In this paper,the state-of-the-art of various FAPMMs are comprehensively reviewed according to three means of flux-adjustment,i.e.electrically adjusted by either auxiliary field windings or armature winding change,mechanically adjusted magnetic reluctances of flux paths,and memory machines by magnetizing/demagnetizing PMs.The corresponding flux-adjustable principles and electromagnetic characteristics are systematically elaborated and quantitatively compared.Their merits and demerits are highlighted,together with their recent developments.展开更多
基金The National Natural Science Foundation of China(No.52107039)the Fujian Provincial Natural Science Foundation for Youth(No.2021J05133)the Key Project of the National Natural Science Foundation of China(No.51937002)。
文摘The axial field hybrid permanent magnet memory machine(AFHPM-MM)employs a hybrid permanent magnet excitation combining NdFeB and AlNiCo,achieving high torque density and a wide flux adjustment range.A separated stator structure is adopted to enhance its antidemagnetization capability.To analyze the contributions of AlNiCo and NdFeB to the induced electromotive force(EMF)in the AFHPM-MM,a frozen permeability-based induced EMF calculation method is proposed.Theoretical analysis reveals that the conventional method exhibits substantial errors in calculating the AlNiCo-induced EMF,primarily attributed to its failure to adequately account for the dynamic magnetization characteristic discrepancies of AlNiCo under varying magnetization states.Through the analysis of magnetization variations in AlNiCo during the flux adjustment process under different magnetization states,an improved induced EMF calculation method is proposed.Comparative results indicate that,during the flux enhancement process,the average calculation error of the AlNiCo-induced EMF is reduced from 19.84%to 2.09%,whereas during the flux weakening process,the error is reduced from 3.87%to 1.67%.The proposed method achieves accurate induced EMF calculation for the AFHPM-MM.
基金supported by the CRRC Zhuzhou Institute Company Ltd.and in part by Key R&D projects in Hunan+1 种基金ChinaNo.2022GK2062。
文摘In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.
基金This work was jointly supported in part by National Natural Science Foundations of China under Grant 51377036 and 51377020in part by Natural Science Foundation of Jiangsu Province for Youth(BK20170674)+1 种基金in part by Specialized Research Fund for the Doctoral Program of Higher Education of China(20130092130005)in part by the Fundamental Research Funds for the Central Universities(2242017K41003).
文摘This paper overviews the recent advances in variable flux memory machines(VFMMs)for traction applications with particular reference to newly emerged machine topologies and related control strategies.Due to the use of flux memorable low coercive force(LCF)magnets,the air-gap flux of VFMM can be flexibly varied via a magnetizing current pulse.Thus,the copper loss associated with the flux weakening current and high-speed iron loss can be significantly reduced,and hence high efficiency can be achieved over a wide speed and torque/power operation.These merits make VFMM potentially attractive for electric vehicle(EV)applications.Various novel VFMMs are reviewed with particular reference to their topologies,working principle,characteristics and related control techniques.In order to tackle the drawbacks in the existing VFMMs,some new designs are introduced for performance improvement.Then,the electromagnetic characteristics of an exemplified EV-scaled switched flux memory machine and various benchmark traction machine choices,such as induction machine,synchronous reluctance machines,as well as commercially available Prius 2010 interior permanent magnet(IPM)machine are compared.Finally,the key challenges and development trends of VFMM are highlighted,respectively.
基金supported by the National Natural Science Foun-dation of China(62137002,62250009,62202367,82025020,and 82230072).
文摘Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.
基金the National Key Research and Development Program of China(No.2017YFC0212100)National High-tech R&D Program of China(No.2015AA015308).
文摘With the rapid development of big data and artificial intelligence(AI),the cloud platform architecture system is constantly developing,optimizing,and improving.As such,new applications,like deep computing and high-performance computing,require enhanced computing power.To meet this requirement,a non-uniform memory access(NUMA)configuration method is proposed for the cloud computing system according to the affinity,adaptability,and availability of the NUMA architecture processor platform.The proposed method is verified based on the test environment of a domestic central processing unit(CPU).
基金Supported by the National Natural Science Foundation of China(61170026)
文摘Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurately detect those hidden processes by analyzing memory data.WVMI dumps in-memory data of the target Windows operating systems from hypervisor and retrieves EPROCESS structures’address of process linked list first,and then generates Data Type Confidence Table（DTCT）.Next,it traverses the memory and identifies the similarities between the nodes in process linked list and the corresponding segments in the memory by utilizing DTCT.Finally,it locates the segments of Windows’EPROCESS and identifies the hidden processes by further comparison.Through extensive experiments,our experiment shows that the WVMI detects the hidden process with high identification rate,and it is independent of different versions of Windows operating system.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘Flux adjustable permanent magnet machines(FAPMMs)are a novel type of permanent magnet(PM)machines which are able to flexibly adjust the field excitation flux linkage and offer the distinctive advantages of high power density and high efficiency.They have attracted ever-increasing interests and are promising candidate machines for electric vehicle,machine-tool and aircraft applications.In this paper,the state-of-the-art of various FAPMMs are comprehensively reviewed according to three means of flux-adjustment,i.e.electrically adjusted by either auxiliary field windings or armature winding change,mechanically adjusted magnetic reluctances of flux paths,and memory machines by magnetizing/demagnetizing PMs.The corresponding flux-adjustable principles and electromagnetic characteristics are systematically elaborated and quantitatively compared.Their merits and demerits are highlighted,together with their recent developments.
