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Woochul Lim - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis using bootstrap information criterion for small sample size response functions
    Structural and Multidisciplinary Optimization, 2020
    Co-Authors: Eshan Amalnerkar, Tae Hee Lee, Woochul Lim
    Abstract:

    Statistical model selection and evaluation methods like Akaike information criteria (AIC) and Monte Carlo simulation (MCS) have often established efficient output for Reliability Analysis with large sample size. Information criterion can provide better model selection and evaluation in small sample sizes setup by considering the well-known measure of bootstrap resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion to check for uncertainty arising from model selection as well as statistics of interest for small sample size using Reliability Analysis. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection devised from efficient bootstrap simulation or variance reduced bootstrap information criterion to be combined with Reliability Analysis. It is beneficial to compute the spread of Reliability values as against solitary fixed values with desirable statistics of interest for uncertainty Analysis. The proposed simulation scheme is verified using a number of sample size focused response functions under repetitions-centred approach with AIC-based Reliability Analysis for comparison and MCS for accuracy. The results show that the proposed simulation scheme aids the statistics of interest by reducing the spread and hence the uncertainty in sample size-based Reliability Analysis when compared with conventional methods.

  • bootstrap guided information criterion for Reliability Analysis using small sample size information
    World Congress of Structural and Multidisciplinary Optimisation, 2017
    Co-Authors: Eshan Amalnerkar, Tae Hee Lee, Woochul Lim
    Abstract:

    Several methods for Reliability Analysis have been established and applied to engineering fields bearing in mind uncertainties as a major contributing factor. Small sample size based Reliability Analysis can be very beneficial when rising uncertainty from statistics of interest such as mean and standard deviation are considered. Model selection and evaluation methods like Akaike Information Criteria (AIC) have demonstrated efficient output for Reliability Analysis. However, information criterion based on maximum likelihood can provide better model selection and evaluation in small sample size scenario by considering the well-known measure of bootstrapping for curtailing uncertainty with resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion based Reliability Analysis to check for uncertainty arising from statistics of interest for small sample size problems. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection frequency devised from information criterion to be combined with Reliability Analysis. It is also beneficial to compute the spread of Reliability values as against solitary fixed values with desirable statistics of interest under replication based approach. The proposed simulation scheme is verified using a number of small and moderate sample size focused mathematical example with AIC based Reliability Analysis for comparison and Monte Carlo simulation (MCS) for accuracy. The results show that the proposed simulation scheme favors the statistics of interest by reducing the spread and hence the uncertainty in small sample size based Reliability Analysis when compared with conventional methods whereas moderate sample size based Reliability Analysis did not show any considerable favor.

H H Ammar - One of the best experts on this subject based on the ideXlab platform.

  • a scenario based Reliability Analysis approach for component based software
    IEEE Transactions on Reliability, 2004
    Co-Authors: S Yacoub, Bojan Cukic, H H Ammar
    Abstract:

    This paper introduces a Reliability model, and a Reliability Analysis technique for component-based software. The technique is named Scenario-Based Reliability Analysis (SBRA). Using scenarios of component interactions, we construct a probabilistic model named Component-Dependency Graph (CDG). Based on CDG, a Reliability Analysis algorithm is developed to analyze the Reliability of the system as a function of reliabilities of its architectural constituents. An extension of the proposed model and algorithm is also developed for distributed software systems. The proposed approach has the following benefits: 1) It is used to analyze the impact of variations and uncertainties in the Reliability of individual components, subsystems, and links between components on the overall Reliability estimate of the software system. This is particularly useful when the system is built partially or fully from existing off-the-shelf components; 2) It is suitable for analyzing the Reliability of distributed software systems because it incorporates link and delivery channel reliabilities; 3) The technique is used to identify critical components, interfaces, and subsystems; and to investigate the sensitivity of the application Reliability to these elements; 4) The approach is applicable early in the development lifecycle, at the architecture level. Early detection of critical architecture elements, those that affect the overall Reliability of the system the most, is useful in delegating resources in later development phases.

  • scenario based Reliability Analysis of component based software
    International Symposium on Software Reliability Engineering, 1999
    Co-Authors: S M Yacoub, Bojan Cukic, H H Ammar
    Abstract:

    Software designers are motivated to utilize off-the-shelf software components for rapid application development. Such applications are expected to have high Reliability as a result of deploying trusted components. The claims of high Reliability need further investigation based on Reliability Analysis techniques that are applicable to component-based applications. This paper introduces a probabilistic model and a Reliability Analysis technique that is applicable to high-level designs. The technique is named scenario-based Reliability Analysis (SBRA). SBRA is specific to component-based software whose Analysis is strictly based on execution scenarios. Using scenarios, we construct a probabilistic model named a "component-dependency graph" (CDG). CDGs are directed graphs that represent components, component reliabilities, link and interface reliabilities, transitions and transition probabilities. In CDGs, component interfaces and link reliabilities are treated as first-class elements of the model. Based on CDGs, an algorithm is presented to analyze the Reliability of the application as the function of reliabilities of its components and interfaces. A case study illustrates the applicability of the algorithm. The SBRA is used to identify critical components and critical component interfaces, and to investigate the sensitivity of the application Reliability to changes in the reliabilities of components and their interfaces.

Mehmet Aksit - One of the best experts on this subject based on the ideXlab platform.

  • software architecture Reliability Analysis using failure scenarios
    Journal of Systems and Software, 2008
    Co-Authors: Bedir Tekinerdogan, Hasan Sözer, Mehmet Aksit
    Abstract:

    With the increasing size and complexity of software in embedded systems, software has now become a primary threat for the Reliability. Several mature conventional Reliability engineering techniques exist in literature but traditionally these have primarily addressed failures in hardware components and usually assume the availability of a running system. Software architecture Analysis methods aim to analyze the quality of software-intensive system early at the software architecture design level and before a system is implemented. We propose a Software Architecture Reliability Analysis Approach (SARAH) that benefits from mature Reliability engineering techniques and scenario-based software architecture Analysis to provide an early software Reliability Analysis at the architecture design level. SARAH defines the notion of failure scenario model that is based on the Failure Modes and Effects Analysis method (FMEA) in the Reliability engineering domain. The failure scenario model is applied to represent so-called failure scenarios that are utilized to derive fault tree sets (FTS). Fault tree sets are utilized to provide a severity Analysis for the overall software architecture and the individual architectural elements. Despite conventional Reliability Analysis techniques which prioritize failures based on criteria such as safety concerns, in SARAH failure scenarios are prioritized based on severity from the end-user perspective. SARAH results in a failure Analysis report that can be utilized to identify architectural tactics for improving the Reliability of the software architecture. The approach is illustrated using an industrial case for analyzing Reliability of the software architecture of the next release of a Digital TV.

  • software architecture Reliability Analysis using failure scenarios
    IEEE IFIP International Conference on Software Architecture, 2005
    Co-Authors: Bedir Tekinerdogan, Mehmet Aksit
    Abstract:

    We propose a Software Architecture Reliability Analysis (SARA) approach that benefits from both Reliability engineering and scenario-based software architecture Analysis to provide an early Reliability Analysis of the software architecture. SARA makes use of failure scenarios that are prioritized with respect to the user-perception in order to provide a severity Analysis for the software architecture and the individual components.

Eshan Amalnerkar - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis using bootstrap information criterion for small sample size response functions
    Structural and Multidisciplinary Optimization, 2020
    Co-Authors: Eshan Amalnerkar, Tae Hee Lee, Woochul Lim
    Abstract:

    Statistical model selection and evaluation methods like Akaike information criteria (AIC) and Monte Carlo simulation (MCS) have often established efficient output for Reliability Analysis with large sample size. Information criterion can provide better model selection and evaluation in small sample sizes setup by considering the well-known measure of bootstrap resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion to check for uncertainty arising from model selection as well as statistics of interest for small sample size using Reliability Analysis. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection devised from efficient bootstrap simulation or variance reduced bootstrap information criterion to be combined with Reliability Analysis. It is beneficial to compute the spread of Reliability values as against solitary fixed values with desirable statistics of interest for uncertainty Analysis. The proposed simulation scheme is verified using a number of sample size focused response functions under repetitions-centred approach with AIC-based Reliability Analysis for comparison and MCS for accuracy. The results show that the proposed simulation scheme aids the statistics of interest by reducing the spread and hence the uncertainty in sample size-based Reliability Analysis when compared with conventional methods.

  • bootstrap guided information criterion for Reliability Analysis using small sample size information
    World Congress of Structural and Multidisciplinary Optimisation, 2017
    Co-Authors: Eshan Amalnerkar, Tae Hee Lee, Woochul Lim
    Abstract:

    Several methods for Reliability Analysis have been established and applied to engineering fields bearing in mind uncertainties as a major contributing factor. Small sample size based Reliability Analysis can be very beneficial when rising uncertainty from statistics of interest such as mean and standard deviation are considered. Model selection and evaluation methods like Akaike Information Criteria (AIC) have demonstrated efficient output for Reliability Analysis. However, information criterion based on maximum likelihood can provide better model selection and evaluation in small sample size scenario by considering the well-known measure of bootstrapping for curtailing uncertainty with resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion based Reliability Analysis to check for uncertainty arising from statistics of interest for small sample size problems. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection frequency devised from information criterion to be combined with Reliability Analysis. It is also beneficial to compute the spread of Reliability values as against solitary fixed values with desirable statistics of interest under replication based approach. The proposed simulation scheme is verified using a number of small and moderate sample size focused mathematical example with AIC based Reliability Analysis for comparison and Monte Carlo simulation (MCS) for accuracy. The results show that the proposed simulation scheme favors the statistics of interest by reducing the spread and hence the uncertainty in small sample size based Reliability Analysis when compared with conventional methods whereas moderate sample size based Reliability Analysis did not show any considerable favor.

Jian Deng - One of the best experts on this subject based on the ideXlab platform.

  • structural Reliability Analysis for implicit performance function using radial basis function network
    International Journal of Solids and Structures, 2006
    Co-Authors: Jian Deng
    Abstract:

    Abstract This is the second paper of our work on structural Reliability Analysis for implicit performance function. The first paper proposed structural Reliability Analysis methods using multilayer perceptron artificial neural network [Deng, J., Gu, D.S., Li, X.B., Yue, Z.Q., 2005. Structural Reliability Analysis for implicit performance function using artificial neural network. Structural Safety 25 (1), 25–48]. This paper presents three radial basis function network (RBF) based Reliability Analysis methods, i.e. RBF based MCS, RBF based FORM, and RBF based SORM. In these methods, radial basis function network technique is adopted to model and approximate the implicit performance functions or partial derivatives. The RBF technique uses a small set of the actual data of the implicit performance functions, which are obtained via physical experiments or normal numerical Analysis such as finite element methods for the complicated structural system, and are used to develop a trained RBF generalization algorithm. Then a large number of the function values and partial derivatives of implicit performance functions can be readily obtained by simply extracting information from the established and successfully trained RBF network. These function values and derivatives are used in conventional MCS, FORM or SORM to constitute RBF based Reliability Analysis algorithms. Examples are presented in the paper to illustrate how the proposed RBF based methods are used in structural Reliability Analysis. The results are well compared with those obtained by the conventional Reliability methods such as the Monte-Carlo simulation, multilayer perceptrons networks, the response surface method, the FORM method 2, and so on. The examples showed the proposed approach is applicable to structural Reliability Analysis involving implicit performance functions.

  • structural Reliability Analysis for implicit performance functions using artificial neural network
    Structural Safety, 2005
    Co-Authors: Jian Deng, Desheng Gu, Xibing Li
    Abstract:

    Abstract The Monte-Carlo simulation (MCS), the first-order Reliability methods (FORM) and the second-order Reliability methods (SORM), are three Reliability Analysis methods that are commonly used for structural safety evaluation. The MCS requires the calculations of hundreds and thousands of performance function values. The FORM and SORM demand the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. Such implicit performance functions are normally encountered when the structural systems are complicated and numerical Analysis such as finite element methods has to be adopted for the prediction. To address this issue, this paper presents three artificial neural network (ANN)-based Reliability Analysis methods, i.e. ANN-based MCS, ANN-based FORM, and ANN-based SORM. These methods employ multi-layer feedforward ANN technique to approximate the implicit performance functions. The ANN technique uses a small set of the actual values of the implicit performance functions. Such a small set of actual data is obtained via normal numerical Analysis such as finite element methods for the complicated structural system. They are used to develop a trained ANN generalization algorithm. Then a large number of the values and partial derivatives of the implicit performance functions can be obtained for conventional Reliability Analysis using MCS, FORM or SORM. Examples are given in the paper to illustrate why and how the proposed ANN-based structural Reliability Analysis can be carried out. The results have shown the proposed approach is applicable to structural Reliability Analysis involving implicit performance functions. The present results are compared well with those obtained by the conventional Reliability methods such as the direct Monte-Carlo simulation, the response surface method and the FORM method 2.