Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, ...Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, low cost, and energy efficiency, at the expense of high write latency, high write power consumption, and limited write endurance. NVMMs have become a competitive alternative of Dynamic Random Access Memory (DRAM), and will fundamentally change the landscape of memory systems. They bring many research opportunities as well as challenges on system architectural designs, memory management in operating systems (OSes), and programming models for hybrid memory systems. In this article, we revisit the landscape of emerging NVMM technologies, and then survey the state-of-the-art studies of NVMM technologies. We classify those studies with a taxonomy according to different dimensions such as memory architectures, data persistence, performance improvement, energy saving, and wear leveling. Second, to demonstrate the best practices in building NVMM systems, we introduce our recent work of hybrid memory system designs from the dimensions of architectures, systems, and applications. At last, we present our vision of future research directions of NVMMs and shed some light on design challenges and opportunities.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
Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory media.Most previous proposals ...Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory media.Most previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM resource.In this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory hierarchy.Since the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of Optane.This design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane DCPMM.We also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement.Moreover,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM cache.We implement Mocha in an architectural simulator.Experimental results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.展开更多
新型非易失性存储器(non-volatile memory,NVM)技术日渐成熟,延迟越来越低,带宽越来越高,未来将不仅有可能取代以动态随机存储器(dynamic random access memory,DRAM)为代表的易失型存储设备在主存中的垄断地位,还有可能取代传统Flash...新型非易失性存储器(non-volatile memory,NVM)技术日渐成熟,延迟越来越低,带宽越来越高,未来将不仅有可能取代以动态随机存储器(dynamic random access memory,DRAM)为代表的易失型存储设备在主存中的垄断地位,还有可能取代传统Flash和机械硬盘作为外存服务未来的计算机系统.如何综合各类新型存储的特性,设计高能效的存储架构,实现可应对大数据、云计算所需求的新型主存系统已经成为工业界和学术界的研究热点.提出基于高性能SOC FPGA阵列的NVM验证架构,互联多级FPGA,利用多层次FPGA结构扩展链接多片NVM.依据所提出的验证架构,设计了基于多层次FPGA的主从式NVM控制器,并完成适用于该架构的硬件原型设计.该架构不仅可以实现测试同类型多片NVM协同工作,也可以进行混合NVM存储管理方案验证.展开更多
An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the re...An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.展开更多
Air pollution threatens human health,necessitating effective and convenient air quality monitoring.Recently,there has been a growing interest in using camera images for air quality estimation.However,a major challenge...Air pollution threatens human health,necessitating effective and convenient air quality monitoring.Recently,there has been a growing interest in using camera images for air quality estimation.However,a major challenge has been nighttime detection due to the limited visibility of nighttime images.Here we present a hybrid deep learning model,capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images.Our model,which integrates a convolutional neural network(CNN)and long short-term memory(LSTM),adeptly captures spatial-temporal image features,enabling air quality estimation at any time of day,including PM_(2.5) and PM10 concentrations,as well as the air quality index(AQI).Compared to independent CNN networks that solely extract spatial features,our model demonstrates superior accuracy on self-constructed datasets with R^(2)?0.94 and RMSE=5.11 mg m^(-3) for PM_(2.5),R^(2)=0.92 and RMSE=7.30 mg m^(-3) for PM10,and R^(2)=0.94 and RMSE?5.38 for AQI.Furthermore,our model excels in daytime air quality estimation and enhances nighttime predictions,elevating overall accuracy.Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model,reaffirming its applicability and superiority for air quality monitoring.展开更多
随着互联网和云计算技术的迅猛发展,现有动态随机存储器(Dynamic Random Access Memory,DRAM)已无法满足一些实时系统对性能、能耗的需求.新型非易失存储器(Non-Volatile Memory,NVM)的出现为计算机存储体系的发展带来了新的契机.本文针...随着互联网和云计算技术的迅猛发展,现有动态随机存储器(Dynamic Random Access Memory,DRAM)已无法满足一些实时系统对性能、能耗的需求.新型非易失存储器(Non-Volatile Memory,NVM)的出现为计算机存储体系的发展带来了新的契机.本文针对NVM和DRAM混合内存系统架构,提出一种高效的混合内存页面管理机制.该机制针对内存介质写特性的不同,将具有不同访问特征的数据页保存在合适的内存空间中,以减少系统的迁移操作次数,从而提升系统性能.同时该机制使用一种两路链表使得NVM介质的写操作分布更加均匀,以提升使用寿命.最后,本文在Linux内核中对所提机制进行仿真实验.并与现有内存管理机制进行对比,实验结果证明了所提方法的有效性.展开更多
Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent yea...Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years.This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process,simple structure and freedom in substrate choices.This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials.Recent progresses in this ferroelectric memory field,hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems.In this paper,a general review of this type of ferroelectric non-volatile memory is provided,which include the device structure,organic ferroelectric materials,electrical characteristics and working principles.We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.展开更多
he advance in Non-Volatile Memory(NVM)has changed the traditional DRAM-onlymemorysystem.Compared to DRAM,NVM has the advantages of nonvolatility and large capacity.However,as the read/write speed of NVM is still lower...he advance in Non-Volatile Memory(NVM)has changed the traditional DRAM-onlymemorysystem.Compared to DRAM,NVM has the advantages of nonvolatility and large capacity.However,as the read/write speed of NVM is still lower than that of DRAM,building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer architecture.This paper aims to optimize the well-known B^(+)-tree for hybrid memory.The novelty of this study is two-fold.First,we observed that the space utilization of internal nodes in B^(+)-tree is generally below 70%.Inspired by this observation,we propose to maintain hot keys in the free space within internal nodes,yielding a new index named HATree(Hotness-Aware Tree).The new idea of HATree is to use the unused space of the parent of leaf nodes(PLNs)as the hotspot data cache.Thus,no extra space is needed,and the in-node hotspot cache can efficiently improve query performance.Second,to further improve the update performance of HATree,we propose to utilize the eADR technology supported by the third-generation Intel Xeon Scalable Processors to enhance HATree with instant log persistence,which results in the new HATree-Log structure.We conduct extensive experiments on real hybrid memory architecture involving DRAM and Intel Optane Persistent Memory to evaluate the performance of HATree and HATree-Log.Three state-of-the-art indices for hybrid memory,namely NBTree,LBTree,and FPTree,are included in the experiments,and the results suggest the efficiency of HATree and HATree-Log.展开更多
This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of ir...This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital information.The long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the HDT.The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation.The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy.The manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via LSTM.The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation.The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes.The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optical scanner.The proposed method demonstrates effec展开更多
Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and adaptability.This article synthesizes research data to examine the cogniti...Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and adaptability.This article synthesizes research data to examine the cognitive benefits of music instruction,including increased IQ,language proficiency,memory,and attention.Traditional face-to-face training,while personalized and socially interactive,faces limitations such as budget constraints and accessibility.Modern digital platforms offer individualized learning paths with AI-driven feedback but may lack necessary interpersonal interaction.This paper proposes a hybrid approach to music education,integrating traditional and digital methods to maximize cognitive gains.Further research is recommended to explore the implementation of these integrated learning strategies in varied educational settings.展开更多
Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years beca...Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption,NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory(DRAM). In this paper, we assume to use hybrid random access memory(RAM)and NVM architecture for the smart bracelet system.This paper presents a data management algorithm named bracelet power-aware data management(BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.展开更多
An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagno...An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.展开更多
基金Supported jointly by the National Natural Science Foundation of China under Grants Nos. 61672251, 61732010, 61825202, and 61929103.
文摘Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, low cost, and energy efficiency, at the expense of high write latency, high write power consumption, and limited write endurance. NVMMs have become a competitive alternative of Dynamic Random Access Memory (DRAM), and will fundamentally change the landscape of memory systems. They bring many research opportunities as well as challenges on system architectural designs, memory management in operating systems (OSes), and programming models for hybrid memory systems. In this article, we revisit the landscape of emerging NVMM technologies, and then survey the state-of-the-art studies of NVMM technologies. We classify those studies with a taxonomy according to different dimensions such as memory architectures, data persistence, performance improvement, energy saving, and wear leveling. Second, to demonstrate the best practices in building NVMM systems, we introduce our recent work of hybrid memory system designs from the dimensions of architectures, systems, and applications. At last, we present our vision of future research directions of NVMMs and shed some light on design challenges and opportunities.
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 jointly by the National Key Research and Development Program of China (No.2022YFB4500303)the National Natural Science Foundation of China (NSFC) (Grant Nos.62072198,61832006,61825202,61929103).
文摘Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory media.Most previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM resource.In this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory hierarchy.Since the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of Optane.This design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane DCPMM.We also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement.Moreover,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM cache.We implement Mocha in an architectural simulator.Experimental results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.
基金supported by the National Science Foundation for Excellent Young Scholars of China(Grant No.51922006).
文摘An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.
基金supported by the National Key Research and Development Program of China[2021YFE0112300]the National Natural Science Foundation of China(NSFC)[41771420]+1 种基金the State Scholarship Fund from the China Scholarship Council(CSC)[201906865016]the Postgraduate Research&Practice Innovation Program of Jiangsu Province[KYCX21_1341].
文摘Air pollution threatens human health,necessitating effective and convenient air quality monitoring.Recently,there has been a growing interest in using camera images for air quality estimation.However,a major challenge has been nighttime detection due to the limited visibility of nighttime images.Here we present a hybrid deep learning model,capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images.Our model,which integrates a convolutional neural network(CNN)and long short-term memory(LSTM),adeptly captures spatial-temporal image features,enabling air quality estimation at any time of day,including PM_(2.5) and PM10 concentrations,as well as the air quality index(AQI).Compared to independent CNN networks that solely extract spatial features,our model demonstrates superior accuracy on self-constructed datasets with R^(2)?0.94 and RMSE=5.11 mg m^(-3) for PM_(2.5),R^(2)=0.92 and RMSE=7.30 mg m^(-3) for PM10,and R^(2)=0.94 and RMSE?5.38 for AQI.Furthermore,our model excels in daytime air quality estimation and enhances nighttime predictions,elevating overall accuracy.Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model,reaffirming its applicability and superiority for air quality monitoring.
文摘随着互联网和云计算技术的迅猛发展,现有动态随机存储器(Dynamic Random Access Memory,DRAM)已无法满足一些实时系统对性能、能耗的需求.新型非易失存储器(Non-Volatile Memory,NVM)的出现为计算机存储体系的发展带来了新的契机.本文针对NVM和DRAM混合内存系统架构,提出一种高效的混合内存页面管理机制.该机制针对内存介质写特性的不同,将具有不同访问特征的数据页保存在合适的内存空间中,以减少系统的迁移操作次数,从而提升系统性能.同时该机制使用一种两路链表使得NVM介质的写操作分布更加均匀,以提升使用寿命.最后,本文在Linux内核中对所提机制进行仿真实验.并与现有内存管理机制进行对比,实验结果证明了所提方法的有效性.
文摘Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years.This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process,simple structure and freedom in substrate choices.This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials.Recent progresses in this ferroelectric memory field,hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems.In this paper,a general review of this type of ferroelectric non-volatile memory is provided,which include the device structure,organic ferroelectric materials,electrical characteristics and working principles.We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.
基金This paper was supported by the National Natural Science Foundation of China(Grant No.62072419).
文摘he advance in Non-Volatile Memory(NVM)has changed the traditional DRAM-onlymemorysystem.Compared to DRAM,NVM has the advantages of nonvolatility and large capacity.However,as the read/write speed of NVM is still lower than that of DRAM,building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer architecture.This paper aims to optimize the well-known B^(+)-tree for hybrid memory.The novelty of this study is two-fold.First,we observed that the space utilization of internal nodes in B^(+)-tree is generally below 70%.Inspired by this observation,we propose to maintain hot keys in the free space within internal nodes,yielding a new index named HATree(Hotness-Aware Tree).The new idea of HATree is to use the unused space of the parent of leaf nodes(PLNs)as the hotspot data cache.Thus,no extra space is needed,and the in-node hotspot cache can efficiently improve query performance.Second,to further improve the update performance of HATree,we propose to utilize the eADR technology supported by the third-generation Intel Xeon Scalable Processors to enhance HATree with instant log persistence,which results in the new HATree-Log structure.We conduct extensive experiments on real hybrid memory architecture involving DRAM and Intel Optane Persistent Memory to evaluate the performance of HATree and HATree-Log.Three state-of-the-art indices for hybrid memory,namely NBTree,LBTree,and FPTree,are included in the experiments,and the results suggest the efficiency of HATree and HATree-Log.
基金Supported by National Key Research and Development Project of China(Grant No.2022YFB3303303)Zhejiang Provincial Research and Development Project of China(Grant No.LGG22E050010)Key Open Fund of State Key Laboratory of Materials Processing and Die and Mould Technology of China(Grant No.P2024-001).
文摘This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital information.The long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the HDT.The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation.The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy.The manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via LSTM.The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation.The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes.The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optical scanner.The proposed method demonstrates effec
文摘Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and adaptability.This article synthesizes research data to examine the cognitive benefits of music instruction,including increased IQ,language proficiency,memory,and attention.Traditional face-to-face training,while personalized and socially interactive,faces limitations such as budget constraints and accessibility.Modern digital platforms offer individualized learning paths with AI-driven feedback but may lack necessary interpersonal interaction.This paper proposes a hybrid approach to music education,integrating traditional and digital methods to maximize cognitive gains.Further research is recommended to explore the implementation of these integrated learning strategies in varied educational settings.
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09
文摘Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption,NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory(DRAM). In this paper, we assume to use hybrid random access memory(RAM)and NVM architecture for the smart bracelet system.This paper presents a data management algorithm named bracelet power-aware data management(BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.
基金co-supported by the National Natural Science Foundation of China(Nos.61903061,61903059 and 61890925)Natural Science Foundation of Liaoning Province,China(No.2020-MS-098)+1 种基金Aeronautical Science Foundation of China(No.20200013063001)the Fundamental Research Funds for the Central Universities,China(No.DUT20JC22)。
文摘An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.
