任务图模型中实时任务设计

仿真技术的进步推动了实时系统(如核反应堆、军事战场和fly-by-wire系统)的深入研究.用仿真方法解决实时问题,必须首先给出实时系统描述模型.TGM(task graph model)模型是实时系统描述模型之一,其模型最重要的成员是实时任务.使用量化仿真方法定义了实时任务的4种参数,并细分任务为子任务.此方法更易描述实时应用系统,更易构造TGM的执行模型,其层次化的结构更容易进行研究和软件实现.TGM模型及其任务设计已成功地应用于分布式三星实时侦察仿真系统.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Dynamics of Entanglement of Qutrit-Qutrit States with Stochastic Dephasing

The effect of stochastic dephasing on the dynamics of entanglement of qutrit-qutrit states is investigated by using negativity and bound entanglement defined with realignment criterion, From the analysis, we, find that the time evolution of quantum free entanglement and bound entanglement depends on the fluctuations of the stochastic variables and the parameters of the particular initial states of concern. Our results imply that some qutrits states display both distillability sudden death and entanglement sudden death, while some states do not display distillability sudden death but only entanglement sudden death.

Real-time parameters monitoring system for underground equipment based on panoramic images

Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.

Stabilization of a class of nonlinear discrete time systems with time varying delay

The stability and stabilization of a class of nonlinear discrete time delayed systems(NDTDS) with time-varying delay and norm-bounded nonlinearity are investigated. Based on discrete time Lyapunov–Krasovskii functional method, a sufficient delaydependent condition for asymptotic stability of nonlinear systems is offered. Then, this condition is used to design a new efficient delayed state feedback controller(DSFC) for stabilization of such systems. These conditions are in the linear matrix inequality(LMI) framework. Illustrative examples confirm the improvement of the proposed approach over the similar cases. Furthermore, the obtained stability and stabilization conditions will be extended to uncertain discrete time delayed systems(UDTDS) with polytopic parameter uncertainties and also with norm-bounded parameter uncertainties.

Adaptive Feedback Stabilization with Quantized State Measurements for a Class of Chaotic Systems

We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed at the input terminal of the quantizer,and another zoomer is located at the output terminal of the quantizer.The zoomers possess a common adjustable time-varying parameter.By using the adaptive laws for the time-varying parameter and estimating boundary error of values of quantization,the stabilization feedback controller with the quantized state measurements is proposed for a class of chaotic systems.Finally,some numerical examples are given to demonstrate the validity of the proposed methods.

Holographic p-Wave Superconductors in Quintessence AdS Black Hole Spacetime

We construct a holographic p-wave superconductor model in the background of quintessence AdS black hole with an SU（2） Yang-Mills gauge field and then probe the effects of quintessence on the holographic p-wave superconduc- tor. We investigate the relation between the critical temperature and the state parameter of quintessence, and present the numerical results for electric conductivity. It is shown that the condensation of the vector field becomes harder as the absolute value of the state parameter increases. Unlike the scalar condensate in the s-wave model, the condensation of the vector field in p-wave model can occur in the total value range of the state parameter wq of quintessence. These results could help us know more about holographic superconductor and dark energy.