The static and dynamic magnetic controlling characteristics of NiMnGa magnetically controlled shape memory alloy （MSMA） were experimentally studied. The results show that the characteristics of induced strain with r...The static and dynamic magnetic controlling characteristics of NiMnGa magnetically controlled shape memory alloy （MSMA） were experimentally studied. The results show that the characteristics of induced strain with respect to the magnetic field are nonlinear with saturation nature, and dependent on the temperature as well as the load applied to the MSMA. The magnetic shape memory effect can be observed only in complete martensite phase at room temperature. The magnetic permeability of MSMA is not constant and reduces with the increment of magnetic field. The relative saturation magnetic permeability of MSMA is about 1.5.展开更多
Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncer...Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncertainty can be controlled by the non-Markovian effect and the atom-cavity coupling. The results show that increasing the atom-cavity coupling can enlarge the oscillating frequencies of the entropic uncertainty and can decrease the minimal value of the entropic uncertainty. Enhancing the non-Markovian effect can reduce the minimal value of the entropic uncertainty. In particular, if the atom-cavity coupling or the non-Markovian effect is very strong, the entropic uncertainty will be very dose to zero at certain time points, thus Bob can minimize his uncertainty about Alice＇s measurement outcomes,展开更多
This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with timevarying delay. The system under consideration is more general than those in other existent work...This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with timevarying delay. The system under consideration is more general than those in other existent works. The controller, which is dependent on the magnitudes and derivative of the delay, is proposed in terms of linear matrix inequality （LMI）. The closed-loop system is asymptotically stable for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration.展开更多
Floating gate memory devices based on two-dimensional materials hold tremendous potential for high-performance nonvolatile memory.However,the memory performance of the devices utilizing the same two-dimensional hetero...Floating gate memory devices based on two-dimensional materials hold tremendous potential for high-performance nonvolatile memory.However,the memory performance of the devices utilizing the same two-dimensional heterostructures exhibits significant differences from lab to lab,which is often attributed to variations in material thickness or interface quality without a detailed exploration.Such uncontrollable performance coupled with an insufficient understanding of the underlying working mechanism hinders the advancement of high-performance floating gate memory.Here,we report controllable and stable memory performance in floating gate memory devices through device structure design under precisely identical conditions.For the first time,the general differences in polarity and on/off ratio of the memory window caused by distinct structural features have been revealed and the underlying working mechanisms were clearly elucidated.Moreover,controllable tunneling paths that are responsible for two-terminal memory performance have also been demonstrated.The findings provide a general and reliable strategy for polarity control and performance optimization of two-dimensional floating gate memory devices.展开更多
This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a co...This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN control.This method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity estimation.Subsequently,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control algorithm.The notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control parameters.Moreover,the DMETM is established to reduce the actuation frequency of the controller.Unlike the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering intervals.Utilizing a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed time.Finally,simulation results are presented to validate the effectiveness of the proposed algorithm.展开更多
The intrinsic neural timescale(INT)provides temporal windows in brain activity that process information of different durations,crucial for the integration and segregation of external inputs and ultimately shaping cogn...The intrinsic neural timescale(INT)provides temporal windows in brain activity that process information of different durations,crucial for the integration and segregation of external inputs and ultimately shaping cognition and behavior.Recent research has uncovered a pronounced INT hierarchy along the adult hippocampus's longaxis.Yet,the development of INT organization within the hippocampus—particularly the pattern of its hierarchical structure and its impact on cognitive development—has not been thoroughly investigated in youth.Here,we discovered that the INT distribution in youth presents a distinct hierarchical structure along both posterioranterior and proximal-distal axes of the hippocampus.Strikingly,this hierarchical structure correlates signifi-cantly with the first principal gradient of the hippocampal-cortical functional connectome and the thickness of hippocampal grey matter.Furthermore,we observed notable changes in the hippocampal INT landscape during youth,characterized by a general narrowing of timescales,alongside dedifferentiation along the hippocampal organizational axes.These maturational changes significantly link to improvements in inhibitory control and working memory performance.Collectively,our findings reveal the developmental patterns of temporal integration and segregation hierarchies within hippocampus,and highlights the profound significance of INT as a neural underpinning that orchestrates cognitive growth.展开更多
Currently,the feedback control rate of most nonlinear systems is realised by the memoryless state feedback controller which cannot affect the impact of time delay on the systems,and the general processing method of th...Currently,the feedback control rate of most nonlinear systems is realised by the memoryless state feedback controller which cannot affect the impact of time delay on the systems,and the general processing method of the Lyapunov–Krasovskii functional for the time-varying delay switched fuzzy systems(SFS)is more conservative.Therefore,this paper addresses the problem of nonfragile robust and memory state feedback control for switched fuzzy systems with unknown nonlinear disturbance.Non-fragile memory state feedback robust controller which has two controller gains different from each other,and switching law are designed to keep the proposed systems asymptotically stable for all admissible parameter uncertainties.Delay-dependent less conservative sufficient conditions are obtained through using the Lyapunov–Krasovskii functional method and free-weighting matrices depending on Leibniz–Newton,guaranteeing that the SFS can be asymptotically stable.A numerical example is given to illustrate the proposed controller performs better than the classic memoryless state feedback controller.展开更多
Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle’s velocity time serie...Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle’s velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.展开更多
基金This work was supported by the National Natural Science Foundation of China under grant No.50177019by the Education Department of China under grant No.20040142004.
文摘The static and dynamic magnetic controlling characteristics of NiMnGa magnetically controlled shape memory alloy （MSMA） were experimentally studied. The results show that the characteristics of induced strain with respect to the magnetic field are nonlinear with saturation nature, and dependent on the temperature as well as the load applied to the MSMA. The magnetic shape memory effect can be observed only in complete martensite phase at room temperature. The magnetic permeability of MSMA is not constant and reduces with the increment of magnetic field. The relative saturation magnetic permeability of MSMA is about 1.5.
基金Supported by the Science and Technology Plan of Hunan Province under Grant No 2010FJ3148the National Natural Science Foundation of China under Grant No 11374096the Doctoral Science Foundation of Hunan Normal University
文摘Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncertainty can be controlled by the non-Markovian effect and the atom-cavity coupling. The results show that increasing the atom-cavity coupling can enlarge the oscillating frequencies of the entropic uncertainty and can decrease the minimal value of the entropic uncertainty. Enhancing the non-Markovian effect can reduce the minimal value of the entropic uncertainty. In particular, if the atom-cavity coupling or the non-Markovian effect is very strong, the entropic uncertainty will be very dose to zero at certain time points, thus Bob can minimize his uncertainty about Alice＇s measurement outcomes,
基金This work was supported by National Natural Science Foundation of PRC (No. 60574084)National 863 Project (No. 2006AA04Z428)the National 973 Program (No. 2002CB312200) of PRC.
文摘This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with timevarying delay. The system under consideration is more general than those in other existent works. The controller, which is dependent on the magnitudes and derivative of the delay, is proposed in terms of linear matrix inequality （LMI）. The closed-loop system is asymptotically stable for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration.
基金supported by Beijing Natural Science Foundation(Grant No.Z210006)National Key R&D Plan(2022YFA1405600)National Natural Science Foundation of China(Grant No.12104051).
文摘Floating gate memory devices based on two-dimensional materials hold tremendous potential for high-performance nonvolatile memory.However,the memory performance of the devices utilizing the same two-dimensional heterostructures exhibits significant differences from lab to lab,which is often attributed to variations in material thickness or interface quality without a detailed exploration.Such uncontrollable performance coupled with an insufficient understanding of the underlying working mechanism hinders the advancement of high-performance floating gate memory.Here,we report controllable and stable memory performance in floating gate memory devices through device structure design under precisely identical conditions.For the first time,the general differences in polarity and on/off ratio of the memory window caused by distinct structural features have been revealed and the underlying working mechanisms were clearly elucidated.Moreover,controllable tunneling paths that are responsible for two-terminal memory performance have also been demonstrated.The findings provide a general and reliable strategy for polarity control and performance optimization of two-dimensional floating gate memory devices.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.62033003,62373113,62203119)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023A1515011527,2023B1515120010)。
文摘This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN control.This method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity estimation.Subsequently,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control algorithm.The notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control parameters.Moreover,the DMETM is established to reduce the actuation frequency of the controller.Unlike the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering intervals.Utilizing a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed time.Finally,simulation results are presented to validate the effectiveness of the proposed algorithm.
基金Shuyu Li is supported by the National Natural Science Foundation of China(32271146)the Startup Funds for Top-notch Talents at Beijing Normal University。
文摘The intrinsic neural timescale(INT)provides temporal windows in brain activity that process information of different durations,crucial for the integration and segregation of external inputs and ultimately shaping cognition and behavior.Recent research has uncovered a pronounced INT hierarchy along the adult hippocampus's longaxis.Yet,the development of INT organization within the hippocampus—particularly the pattern of its hierarchical structure and its impact on cognitive development—has not been thoroughly investigated in youth.Here,we discovered that the INT distribution in youth presents a distinct hierarchical structure along both posterioranterior and proximal-distal axes of the hippocampus.Strikingly,this hierarchical structure correlates signifi-cantly with the first principal gradient of the hippocampal-cortical functional connectome and the thickness of hippocampal grey matter.Furthermore,we observed notable changes in the hippocampal INT landscape during youth,characterized by a general narrowing of timescales,alongside dedifferentiation along the hippocampal organizational axes.These maturational changes significantly link to improvements in inhibitory control and working memory performance.Collectively,our findings reveal the developmental patterns of temporal integration and segregation hierarchies within hippocampus,and highlights the profound significance of INT as a neural underpinning that orchestrates cognitive growth.
基金This work is supported by LiaoNing Revitalization Talents Program[grant number XLYC1807138]Program for Liaoning Excellent Talents in University[grant number LR2018062]Project of Natural Science Foundation of Liaoning Province[grant number 2019-MS-237].
文摘Currently,the feedback control rate of most nonlinear systems is realised by the memoryless state feedback controller which cannot affect the impact of time delay on the systems,and the general processing method of the Lyapunov–Krasovskii functional for the time-varying delay switched fuzzy systems(SFS)is more conservative.Therefore,this paper addresses the problem of nonfragile robust and memory state feedback control for switched fuzzy systems with unknown nonlinear disturbance.Non-fragile memory state feedback robust controller which has two controller gains different from each other,and switching law are designed to keep the proposed systems asymptotically stable for all admissible parameter uncertainties.Delay-dependent less conservative sufficient conditions are obtained through using the Lyapunov–Krasovskii functional method and free-weighting matrices depending on Leibniz–Newton,guaranteeing that the SFS can be asymptotically stable.A numerical example is given to illustrate the proposed controller performs better than the classic memoryless state feedback controller.
基金supported by the National Natural Science Foundation of China(Grant No.61703318)Natural Science Foundation of Hubei Province(Grant No.2017CFB130)
文摘Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle’s velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.
