Sharif Digital Repository

Using the original form of Capacitance-Resistance Model (CRM), as a waterflooding performance prediction tool, for modeling real reservoirs makes some unavoidable errors. Combination of this model with available data assimilation methods yields more powerful simulation tool with updating parameters over time. However, the inherent uncertainty arisen by modeling complex reservoirs with only a limited number of CRM parameters is not addressed yet. In this study, the model error behavior has been simulated through a physically-based dynamical system in which it has been correlated with the original model parameters. The ensemble-based Kalman filter (EnKF) data assimilation method has been... 

Coordinate Measuring Machines (CMMs) are designed for precision inspection of complex industrial products. The mechanical accuracy of CMMs depends on both static and dynamic sources of error. In automated CMMs, one of the dynamic error sources is vibration of the probe, due to inertia forces resulting from parts acceleration and deceleration. Modeling of a gantry type CMM, based on the Timoshenko beam theory with moving mass effects, is developed and the dynamic errors of the probe resulting from the acceleration and deceleration of moving parts, are calculated. Findings from analytical solution and dynamic modeling software indicate high accuracy and good agreement between the results. ©... 

An efficient and reliable Multi-Level Cell (MLC) Spin-Transfer Torque Random Access Memory (STT-RAM) is proposed based on a Drop-And-Rearrange Approach, called DARA. Since CNN models are rather robust, less important bits are dropped, allowing important bits to be written in safe and reliable Single-Level Cell mode. Also, bits are rearranged to make the representation better aligned with memory cell characteristics. Bits with higher impact on the features value are stored in safer bit positions reducing the chance of read/write circuits to malfunction. Experimental results show that our approach provides comparable to error-free scenario reliability level, while doubling the bandwidth and... 

Many quantum systems are being investigated in the hope of building a large-scale quantum computer. All of these systems suffer from decoherence, resulting in errors during the execution of quantum gates. Quantum error correction enables reliable quantum computation given unreliable hardware. Unoptimized topological quantum error correction (TQEC), while still effective, performs very suboptimally, especially at low error rates. Hand optimizing the classical processing associated with a TQEC scheme for a specific system to achieve better error tolerance can be extremely laborious. We describe a tool, AUTOTUNE, capable of performing this optimization automatically, and give two highly... 

In this paper, we enhance previously suggested vulnerability estimation techniques by presenting a detailed modeling technique based on Input-to-Output Masking (IOM). Moreover we use our model to compute the System-level Vulnerability Factor (SVF) for data-path components in a highperformance processor. As we show, recent suggested estimation techniques overlook the issue of error masking, mainly focusing on time periods in which an error could potentially propagate in the system. In this work we show that this is incomplete as it ignores the masking impact. Our results show that including the IOM factor can significantly affect the system-level vulnerability for data-path components. As a... 

Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are... 

The increasing rate of transient faults necessitates the use of on-chip fault-tolerant techniques in embedded microprocessors. Performance overhead is a challenging problem in on-chip fault-tolerant techniques used in the random logic of the embedded microprocessors. This paper presents a signature-based error detection and roll-back recovery technique for the control logic with much lower performance overhead as compared to many previous techniques. The low performance overhead is achieved by eliminating the fault masking overhead cycles in the previous techniques. The performance overhead is analytically studied, and the analytical results recommend at which fault rate the use of the... 

In this paper a general method for system level prediction of INL in pipeline analog to digital converters is presented. For each stage of the ADC, a new error model consisting of an input referred gain error and a nonlinear term is introduced. An analytic method to calculate INL from all error sources is presented. INL model for a switched-capacitor implementation is also presented. ©2006 IEEE  

A dc-dc resonant converter has the advantage of overcoming switching losses and electromagnetic interference which are the main limitations of high frequency power converters. Nevertheless, the modeling and stability analysis of dc-dc resonant converters are considerably more complex than pulsewidth modulation counterparts. The conventional averaged linearized model of the resonant converter has limitations due to averaging and linearization. First of all, the linearized model has large modeling error in presence of large variations of reference voltage and input voltage. Furthermore, Converging area for stabilizing controllers is smaller in the averaged model. In order to overcome these... 

A gyroless deterministic attitude determination algorithm based on simulation of sun in eclipse is stated in this paper and has been compared to stochastic filters like extended Kalman filter and unscented Kalman filter. Attitude determination with low cost sensors such as magnometer and sun sensor results in usage of recursive algorithms such as Kalman filter which has the probability of divergence, but with deterministic point to point algorithm such as the one introduced in this paper we can be sure to have an attitude determination with a fixed maximum error. Proposed method has been compared with Extended Kalman Filter and Unscented Kalman filter due to its modeling error, robustness... 

IN this paper the effect of different channel estimation approaches in OFDMA uplink system which are based on Basis Expansion Model (BEM) and widely used to consider time varying channels are discussed. It has been shown in previous works that modeling error will be reduced by applying oversampled BEM with cost of increasing sensitivity to noise. This problem will be solved by combining oversampled BEM and MMSE channel estimator with cost of increasing computational complexity. In this paper a novel channel estimation approach is represented in which by combining oversampled BEM and low rank MMSE in frequency domain, almost the same performance as the full rank MMSE has been achieved while... 

High reliability against noise, high performance, and low energy consumption are key objectives in the design of on-chip networks. Recently some researchers have considered the impact of various error-control schemes on these objectives and on the tradeoff between them. In all these works performance and reliability are measured separately. However, we will argue in this paper that the use of error-control schemes in on-chip networks results in degradable systems, hence, performance and reliability must be measured jointly using a unified measure, i.e., performability. Based on the traditional concept of performability, we provide a definition for the "Interconnect Performability".... 

Capacitance-Resistive Model (CRM), as a fast yet efficient proxy model, suffers from some limitations in modeling relatively complex reservoirs. Some current improvements on this proxy made it a more powerful simulator with updating parameters over time. However, the model's intrinsic uncertainty arisen from simplifying fluid-flow modeling by some limited number of constant parameters is not addressed yet. In this study, this structural limitation of CRM has been addressed by introducing a time-correlated model error, including stochastic and non-stochastic parameters, embedded into this proxy's formulation. The error term's non-stochastic parameters have been tuned to be used in forecasting... 

This work develops a systematic uncertainty quantification framework to assess the reliability of prediction delivered by physics-based material models in the presence of incomplete measurement data and modeling error. The framework consists of global sensitivity analysis, Bayesian inference, and forward propagation of uncertainty through the computational model. The implementation of this framework on a new multiphase model of novel porous silica aerogel materials is demonstrated to predict the thermomechanical performances of a building envelope insulation component. The uncertainty analyses rely on sampling methods, including Markov-chain Monte Carlo and a mixed finite element solution of...