Effect of Eccentricity and Surface Roughness On Probabilistic Performance of Two Axial Groove Bearing

This paper presents the effect of eccentricity and surface roughness on the probabilistic performance of two axial groove hydrodynamic journal bearing. In general, it is difficult to quantify experimentally the variabilities involved in dynamic responses of the hydrodynamic bearing due to the randomness involved in surface asperity and eccentricity ratio. The deterministic models available for the analysis of the bearings are not capable to include such uncertainties. These uncertainties arise from the manufacturing imperfections, misalignment of the bearing, frictional wear, uncertain operating condition, model inaccuracy. To simulate such variabilities, Monte Carlo simulation (MCS) is carried out. Stochastic steady-state and dynamic coefficients are obtained by solving the Reynolds equation using the surrogate-based finite difference method. Sensitivity analysis of the performance parameters with respect to stochastic input parameters is portrayed. The moving least square (MLS) model is constructed as the surrogate to increase the computational efficiency. The significant influences of stochastic input parameters such as surface roughness and eccentricity ratio are observed on the random hydrodynamic performance of two axial groove journal bearing.

CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements

This paper is submitted to IEEE TCAS2. In this paper, A stochastic input encoding scheme (CODEX) is presented that aims to relax the digital-to-analog converter (ADC) design requirements in memristor crossbar systems.

A domain decomposition method for the Navier–Stokes equations with stochastic input

The paper is committed to studying the domain decomposition method for the incompressible Navier–Stokes equations(NSEs) with stochastic input. The stochastic input is represented spectrally by employing orthogonal polynomial functionals from the Askey scheme as trial basis to represent the random space, and the stochastic NSEs system are transformed into deterministic ones via the polynomial chaos expansion. The corresponding deterministic equations are transformed into the constrained optimization problem by minimizing the cost function on the common interface after the whole domain decomposed into two sub-domains. The constrained optimization problems are transformed into unconstrained problems by the Lagrange multiplier rule. A gradient method-based approach to the solutions of domain decomposition problem is proposed to solve the unconstrained optimality system. Finally, one numerical simulation experiment for square cavity flow problem with the stochastic boundary conditions are performed to demonstrate the feasibility and applicability of the gradient method.

CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements

This paper is submitted to IEEE TCAS2. In this paper, A stochastic input encoding scheme (CODEX) is presented that aims to relax the digital-to-analog converter (ADC) design requirements in memristor crossbar systems.

CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements

This paper is submitted to IEEE TCAS2. In this paper, A stochastic input encoding scheme (CODEX) is presented that aims to relax the digital-to-analog converter (ADC) design requirements in memristor crossbar systems.

Adaptive control of free-floating manipulator in presence of stochastic input disturbances and unknown dynamical parameters

The trajectory tracking problem of a free-floating manipulator with dynamical uncertainties and stochastic input disturbances is solved in this study. First, the free-floating manipulator is mapped to a conventional fixed base dynamically equivalent manipulator. Then, by using the well-known properties of a revolute joint manipulator and taking into account the random disturbances with unknown power spectral density in control inputs, an adaptive controller scheme is developed. The proposed technique uses the exponential practical stability concept which guarantees that the tracking error and its derivative converge to an arbitrarily small neighborhood of zero by appropriate tuning of the controller’s parameters. It is noteworthy that the proposed controller does not need any physical parameters of the robot. Simulation studies demonstrate the effectiveness and capability of the proposed method for trajectory tracking in the presence of unknown stochastic input disturbances and dynamical uncertainties.

SPIKING ACTIVITY OF LIFH NEURON MODEL WITH VARIABLE INPUT STIMULUS

Neuronal information processing occurs in term of spikes. A neuron can emits various kinds of spiking patterns based on the applied input stimulus. In this article, we study the spiking pattern of LIFH neuron model in the presence of four different kinds of applied input stimulus, namely, constant input stimulus, uniformly distributed input stimulus, Gaussian distributed input stimulus and stochastic input stimulus. Here, we notice the tonic and semi-tonic spiking pattern for Gaussian distributed input stimulus and stochastic input stimulus.