A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential forms.Nevertheless,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential stabilization.In comparison with autonomous cases,non-autonomous systems are of great interest and represent an important challenge.Accordingly,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are adopted.It should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem

A useful life prediction method based on the integration of the stochastic hybrid automata(SHA) model and the frame of the dynamic fault tree(DFT) is proposed. The SHA model can incorporate the orbit environment, work modes, system configuration, dynamic probabilities and degeneration of components,as well as spacecraft dynamics and kinematics. By introducing the frame of DFT, the system is classified into several layers, and the problem of state combination explosion is artfully overcome.An improved dynamic reliability model(DRM) based on the Nelson hypothesis is investigated to improve the defect of cumulative failure probability(CFP), which is used to address the failure probability of components in the SHA model. The simulation using the Monte-Carlo method is finally conducted on two satellites, which are deployed with the same multi-gyro subsystem but run on different orbits. The results show that the predicted useful life of the attitude control system(ACS) with consideration of abrupt failure,degradation, and running environment is quite different between the two satellites.

Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions

The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few liter- ature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive as- sumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example .

Hybrid Analysis Approach for Stochastic Response of Offshore Jacket Platforms

The dynamic response of offshore platforms is more serious in hostile sea environment than in shallow sea. In this paper, a hybrid solution combined with analytical and numerical method is proposed to compute the stochastic response of fixed offshore platforms to random waves, considering wave-structure interaction and non-linear drag force. The simulation program includes two steps: the first step is the eigenanalysis aspects associated the structure and the second step is response estimation based on spectral equations. The eigenanalysis could be done through conventional finite element method conveniently and its natural frequency and mode shapes obtained. In the second part of the process, the solution of the offshore structural response is obtained by iteration of a series of coupled spectral equations. Considering the third-order term in the drag force, the evaluation of the three-fold convolution should be demanded for nonlinear stochastic response analysis. To demonstrate this method, a numerical analysis is carried out for both linear and non-linear platform motions. The final response spectra have the typical two peaks in agreement with reality, indicating that the hybrid method is effective and can be applied to offshore engineering.

EXPONENTIAL STABILITY FOR NONLINEAR HYBRID STOCHASTIC PANTOGRAPH EQUATIONS AND NUMERICAL APPROXIMATION

The paper develops exponential stability of the analytic solution and convergence in probability of the numerical method for highly nonlinear hybrid stochastic pantograph equation. The classical linear growth condition is replaced by polynomial growth conditions, under which there exists a unique global solution and the solution is almost surely exponentially stable. On the basis of a series of lemmas, the paper establishes a new criterion on convergence in probability of the Euler-Maruyama approximate solution. The criterion is very general so that many highly nonlinear stochastic pantograph equations can obey these conditions. A highly nonlinear example is provided to illustrate the main theory.

An Uncertainty Analysis Method for Artillery Dynamics with Hybrid Stochastic and Interval Parameters

This paper proposes a non-intrusive uncertainty analysis method for artillery dynamics involving hybrid uncertainty using polynomial chaos expansion(PCE).The uncertainty parameters with sufficient information are regarded as stochastic variables,whereas the interval variables are used to treat the uncertainty parameters with limited stochastic knowledge.In this method,the PCE model is constructed through the Galerkin projection method,in which the sparse grid strategy is used to generate the integral points and the corresponding integral weights.Through the sampling in PCE,the original dynamic systems with hybrid stochastic and interval parameters can be transformed into deterministic dynamic systems,without changing their expressions.The yielded PCE model is utilized as a computationally efficient,surrogate model,and the supremum and infimum of the dynamic responses over all time iteration steps can be easily approximated through Monte Carlo simulation and percentile difference.A numerical example and an artillery exterior ballistic dynamics model are used to illustrate the feasibility and efficiency of this approach.The numerical results indicate that the dynamic response bounds obtained by the PCE approach almost match the results of the direct Monte Carlo simulation,but the computational efficiency of the PCE approach is much higher than direct Monte Carlo simulation.Moreover,the proposed method also exhibits fine precision even in high-dimensional uncertainty analysis problems.

Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed timevarying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.

OPTIMAL SMOOTHING FOR MICROPHONE ARRAY POST-FILTERING UNDER A COMBINED DETERMINISTIC-STOCHASTIC HYBRID MODEL

This paper shows the importance of the optimal smoothing scheme in Microphone Array Post-Filtering(MAPF) under a combined Deterministic-Stochastic Hybrid Model(DSHM).We reveal that some of the well-known MAPF algorithms may cause serious speech distortion without using the optimal smoothing scheme,which is resulted from oversmoothing the raw periodogram over time.Using a minimum conditional mean square error criterion,we derive the optimal smoothing factor under the DSHM,where the Deterministic-to-Stochastic-Ratio(DSR) and the stationarity determine the value of the optimal smoothing factor.The optimal smoothing scheme is applied to the Tran-sient-Beam-to-Reference-Ratio(TBRR)-based MAPF algorithm and experimental results show its better performance in terms of both the Log-Spectral Distance(LSD) and the Perceptual Evaluation of Speech Quality(PESQ).

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

Detection of Phaeocystis globosa using sandwich hybridization integrated with nuclease protection assay(NPA-SH)

Phaeocystis globosa Scherffel is one of the common harmful algae species in coastal waters of the southeastern China.In this study,sandwich hybridization integrated with nuclease protection assay(NPA-SH)was used to qualitatively and quantitatively detect P. globosa.Results showed that this method had good applicability and validity in analyzing the samples from laboratory cultures and from fields.The linear regression equation for P.globosa was obtained,and the lowest detection number of cells was 1.8×104 c...

Detection of Prorocentrum donghaiense using sandwich hybridization integrated with nuclease protection assay

Prorocentrum donghaiense is an important harmful algae bloom （HAB） causing creature in China＇s seas, and the conventional visual detection can not cope with long-term monitoring and highthroughput sampling projects. An assay for P. donghaiense with sandwich hybridization integrated with nuclease protection assay （NPA-SH） was established. Tests with mixed samples and spiked field ones confirmed its good specificity and sensitivity. The cell number of P. donghaiense correlated well with the optical density, and the regression equation is y=4× 10^- 6x＋ 0.694 9, in which x is the cell number, and y is the optical density, with r2=0.953 5. These results show that the NPA-SH method has good feasibility in the detection of P. donghaiense. Results of NPA-SH and microscopy are excellent for each sample. The NPA-SH method was a simple way in quantitative detection of P. donghaiense, and the whole process could be finished in about six hours, which provided a new approach in high-throughput sampling and long-term monitoring of P. donghaiense.

Global SH-wave propagation in a 2D whole Moon model using the parallel hybrid PSM/FDM method

We present numerical modeling of SH-wave propagation for the recently proposed whole Moon model and try to improve our understanding of lunar seismic wave propagation. We use a hybrid PSM/FDM method on staggered grids to solve the wave equations and implement the calculation on a parallel PC cluster to improve the computing efficiency. Features of global SH-wave propagation are firstly discussed for a 100-km shallow and900-km deep moonquakes, respectively. Effects of frequency range and lateral variation of crust thickness are then investigated with various models. Our synthetic waveforms are finally compared with observed Apollo data to show the features of wave propagation that were produced by our model and those not reproduced by our models. Our numerical modeling show that the low-velocity upper crust plays significant role in the development of reverberating wave trains. Increasing frequency enhances the strength and duration of the reverberations.Surface multiples dominate wavefields for shallow event.Core–mantle reflections can be clearly identified for deep event at low frequency. The layered whole Moon model and the low-velocity upper crust produce the reverberating wave trains following each phases consistent with observation. However, more realistic Moon model should be considered in order to explain the strong and slow decay scattering between various phases shown on observation data.

Coherent beam combining of hybrid phase control in master oscillator-power amplifier configuration

A novel scalable architecture for coherent beam combining with hybrid phase control involving passive phasing and active phasing in master oscillator-power amplifier configuration is presented. Wide-linewidth mutually injected passive phasing fibre laser arrays serve as master oscillators for the power amplifiers, and the active phasing using stochastic parallel gradient descent algorithm is induced. Wide-linewidth seed laser can suppress the stimulated Brillouin scattering effectively and improve the output power of the fibre laser amplifier, while hybrid phase control provides a robust way for in-phase mode coherent beam combining simultaneously. Experiment is performed by active phasing fibre laser amplifiers with passive phasing fibre ring laser array seed lasers. Power encircled in the main-lobe increases1.57 times and long-exposure fringe contrast is obtained to be 78% when the system evolves from passive phasing to hybrid phasing.

Dynamic responses of an energy harvesting system based on piezoelectric and electromagnetic mechanisms under colored noise

Because of the increasing demand for electrical energy,vibration energy harvesters(VEHs)that convert vibratory energy into electrical energy are a promising technology.In order to improve the efficiency of harvesting energy from environmental vibration,here we investigate a hybrid VEH.Unlike previous studies,this article analyzes the stochastic responses of the hybrid piezoelectric and electromagnetic energy harvesting system with viscoelastic material under narrow-band(colored)noise.Firstly,a mass-spring-damping system model coupled with piezoelectric and electromagnetic circuits under fundamental acceleration excitation is established,and analytical solutions to the dimensionless equations are derived.Then,the formula of the amplitude-frequency responses in the deterministic case and the first-order and secondorder steady-state moments of the amplitude in the stochastic case are obtained by using the multi-scales method.The amplitude-frequency analytical solutions are in good agreement with the numerical solutions obtained by the Monte Carlo method.Furthermore,the stochastic bifurcation diagram is plotted for the first-order steady-state moment of the amplitude with respect to the detuning frequency and viscoelastic parameter.Eventually,the influence of system parameters on mean-square electric voltage,mean-square electric current and mean output power is discussed.Results show that the electromechanical coupling coefficients,random excitation and viscoelastic parameter have a positive effect on the output power of the system.

Optimization of Critical Systems for Robustness in a Multistate World

Critical systems are typically complex systems that are required to perform reliably over a wide range of scenarios, or multistate world. Seldom does a single system exist that performs best for all plausible scenarios. A robust solution, one that performs relatively well over a wide range of scenarios, is often the preferred choice for reduced risk at an acceptable cost. The alternative with the maximum expected utility may possess vulnerabilities that could be exploited. The best strategy is likely to be a hybrid solution. The von Neumann-Morgenstern Expected Utility Theory (EUT) would never select such a solution because, given its linear functional form, the expected utility of a hybrid solution cannot be greater than that of every constituent alternative. The continuity axiom and the independence axiom are assessed to be unrealistic for the problem of interest. Several well-known decision models are analyzed and demonstrated to be potentially misleading. The linear disappointment model modifies EUT by adding a term proportional to downside risk;however, it does not provide a mathematical basis for determining preferred hybrid solutions. The paper proposes a portfolio allocation model with stochastic optimization as a flexible and transparent method for defining choice problems and determining hybrid solutions for critical systems with desirable properties such as diversification and robustness.

Global SH-wavefield calculation for a two-dimensional whole-Earth model with the parallel hybrid PSM/FDM algorithm

We present a parallel hybrid algorithm based on pseudospectral method （PSM） and finite difference method （FDM） for two-dimensional （2-D） global SH- wavefield simulation. The whole-Earth model is taken as a cross section of spherical Earth, and corresponding wave equations are defined in 2-D cylindrical coordinates. Spatial derivatives in the wave equations are approximated with efficient and high accuracy PSM in the lateral and high-order FDM in the radial direction on staggered grids. This algorithm allows us to divide the whole-Earth into sub-domains in radial direction and implement efficient parallel computing on PC cluster, while retains high accuracy and efficiency of PSM in lateral direction. A transformation of moment tensor between 3-D spherical Earth and our 2-D model was proposed to give corre- sponding moment tensor components used in 2-D modeling. Comparison of modeling results with those obtained by direct solution method shows very good accuracy of our algorithm. We also demonstrate its feasibility with a lateral heterogeneous whole-Earth model with localized velocity perturbation.

概率计算及混合概率计算

非位置概率数的计算机制已经成为边缘计算片上系统的新范式.本文介绍了概率计算(stochastic computing)的起源、发展和目前国内外的研究现状.针对传统概率计算存在诸如计算时延长、脉冲串信息携带效率低等问题,本文提出了二进制数-概率脉冲串混合编码的混合概率数概念,并从数的表示机理上阐释了二进制数、概率数和混合概率数的数理关系,进而揭示了混合概率计算所具备的低时延、高算力和高能效比的计算特点.本文基于40 nm CMOS工艺设计混合概率深度神经网络,该神经网络芯片在内核面积仅0.73 mm×0.73 mm的条件下,设计4544个乘累加(MAC)单元.在时钟频率400 MHz条件下,总功率为102.3 mW,其中动态功耗仅97μW.通过ASIC芯片的实验测试表明,混合概率计算作为一种全新的颠覆性计算范式,与其他确定性、可扩展和全并行等概率计算方案相比,其能效比分别提高了50倍、2.5倍和3.26倍.

基于HMFDE和t-SNE的旋转机械故障诊断方法

针对旋转机械的故障特征提取较难,以及单一类型的特征无法全面反映故障特性的问题,提出了一种基于混合多尺度波动散布熵(HMFDE)、t分布-随机邻域嵌入(t-SNE)和郊狼优化算法(COA)优化极限学习机(ELM)的旋转机械故障诊断方法。首先,采用特征加权提出了混合多尺度波动散布熵方法,并将其用于提取旋转机械振动信号的故障特征;随后,采用t-SNE方法对混合故障特征进行了特征降维,挑选出了最能够反映故障特性的特征子集,构建了敏感特征样本;最后,采用郊狼优化算法对极限学习机的输入权重和隐含层阈值进行了优化,完成了旋转机械的故障识别和分类;以齿轮箱和滚动轴承故障数据集为对象,对基于HMFDE、t-SNE和COA-ELM的故障诊断方法进行了实验,验证了方法的有效性。研究结果表明:采用HMFDE-t-SNE-CAO-ELM故障诊断方法可以取得100%的故障识别准确率,该方法能够有效地诊断旋转机械的不同故障类型和损伤;相较于基于单一类型特征的故障诊断方法,其准确率分别可以提高0.68%、22.42%、29.18%(齿轮箱)和1.43%、8.23%、23.67%(滚动轴承),虽然牺牲了一定的计算效率,但准确率得到了明显的提高;相较于其他常规故障分类器,COA-ELM的故障识别准确率具有明显的优势。

不确定环境下考虑多技能人力资源的复杂产品MRO服务资源调度

针对复杂产品维护、维修、大修(MRO)服务资源调度中存在人力资源多技能异质而导致任务实际执行工期随着指派方案的不同而变化的问题,将多技能工调度与人员学习效应相结合,建立“任务技能人员”的匹配关系。以最小化服务完成时间、最小化人员冗余和最大化资源性能成本比为目标,构建资源调度时间参数不确定条件下的随机机会约束规划数学模型。并提出一种基于随机模拟、BP神经网络和改进NSGA-II的混合智能算法进行求解。通过具有不同参数特征的对比实验案例和敏感性分析,验证了所提模型和算法的有效性和适用性。

我国沿海混合强风区典型桥址设计风速取值研究

为解决我国沿海典型强/台风混合风气候地区仅依赖实测样本难以准确估计不同风气候极值风速的问题,提出同时考虑台风与非台风的极值风速估计方法.对于非台风气候,利用最优概率密度分布拟合年极值风速样本,并考虑因有限样本和概率模型选择产生的不确定性,获取非台风极值风速区间,其中广义极值(GEV)分布与皮尔逊Ⅲ型(P3)分布估计不确定性约为3~8 m/s.对于台风气候,采用随机路径数值模拟方法,结合三维台风风场模型直接估计10 m高度、基准高度处的极值风速.由此将非台风和台风作为独立事件,采用两者极值风速曲线外包络作为推荐的极值风速设计曲线,并与现行规范进行比较.结果表明,以区间作为取值依据,可保证规范风速普遍低于上包络风速2~5 m/s,临近区间均值,落于区间内,提供了更全面的风速取值方法.