Hoang Pham

Bio: Hoang Pham is an academic researcher from Rutgers University. The author has contributed to research in topics: Software quality & Reliability (statistics). The author has an hindex of 47, co-authored 210 publications receiving 8688 citations. Previous affiliations of Hoang Pham include State University of New York System.

Software Reliability

Abstract: From the Publisher: this book presents detailed analytical models, state-of-the-art techniques, methodologies, and tools used to assess the reliability of software systems.

Handbook of reliability engineering

Abstract: PART I. System Reliability and Optimization 1 Multi-state k-out-of-n Systems Ming J. Zuo, Jinsheng Huang and Way Kuo 1.1 Introduction 1.2 Relevant Concepts in Binary Reliability Theory 1.3 Binary k-out-of-n Models 1.3.1 The k-out-of-n:G System with Independently and Identically Distributed Components 1.3.2 Reliability Evaluation Using Minimal Path or Cut Sets 1.3.3 Recursive Algorithms 1.3.4 Equivalence Between a k-out-of-n:G System and an (n - k + 1)-out-of-n:F System 1.3.5 The Dual Relationship Between the k-out-of-n G and F Systems 1.4 Relevant Concepts in Multi-state Reliability Theory 1.5 A Simple Multi-state k-out-of-n: G Model 1.6 A Generalized Multi-state k-out-of-n:G System Model 1.7 Properties of Generalized Multi-state k-out-of-n:G Systems 1.8 Equivalence and Duality in Generalized Multi-state k-out-of-n Systems 2 Reliability of Systems with Multiple Failure Modes Hoang Pham 2.1 Introduction 2.2 The Series System 2.3 The Parallel System 2.3.1 Cost Optimization 2.4 The Parallel-Series System 2.4.1 The Profit Maximization Problem 2.4.2 Optimization Problem 2.5 The Series-Parallel System 2.5.1 Maximizing the Average System Profit 2.5.2 Consideration of Type I Design Error 2.6 The k-out-of-n Systems 2.6.1 Minimizing the Average System Cost 2.7 Fault-tolerant Systems 2.7.1 Reliability Evaluation 2.7.2 Redundancy Optimization 2.8 Weighted Systems with Three Failure Modes 3 Reliabilities of Consecutive-k Systems Jen-Chun Chang and Frank K. Hwang 3.1 Introduction 3.1.1 Background 3.1.2 Notation 3.2 Computation of Reliability 3.2.1 The Recursive Equation Approach 3.2.2 The Markov Chain Approach 3.2.3 Asymptotic Analysis 3.3 Invariant Consecutive Systems 3.3.1 Invariant Consecutive-2Systems 3.3.2 Invariant Consecutive-k Systems 3.3.3 Invariant Consecutive-kG System. 3.4 Component Importance and the Component Replacement Problem 3.4.1 The Birnbaum Importance 3.4.2 Partial Birnbaum Importance 3.4.3 The Optimal Component Replacement 3.5 The Weighted-consecutive-k-out-of-n System. 3.5.1 The Linear Weighted-consecutive-k-out-of-n System 3.5.2 The Circular Weighted-consecutive-k-out-of-n System 3.6 Window Systems 3.6.1 The f -within-consecutive-k-out-of-n System 3.6.2 The 2-within-consecutive-k-out-of-n System 3.6.3 The b-fold-window System 3.7 Network Systems 3.7.1 The Linear Consecutive-2 Network System 3.7.2 The Linear Consecutive-k Network System 3.7.3 The Linear Consecutive-k Flow Network System 3.8 Conclusion 4 Multi-state System Reliability Analysis and Optimization G. Levitin and A. Lisnianski 4.1 Introduction 4.1.1 Notation 4.2 Multi-state System Reliability Measures 4.3 Multi-state System Reliability Indices Evaluation Based on the Universal Generating Function 4.4 Determination of u-function of Complex Multi-state System Using Composition Operators 4.5 Importance and Sensitivity Analysis of Multi-state Systems 4.6 Multi-state System Structure Optimization Problems 4.6.1 Optimization Technique 4.6.1.1 Genetic Algorithm 4.6.1.2 Solution Representation and Decoding Procedure 4.6.2 Structure Optimization of Series-Parallel System with Capacity-based Performance Measure 4.6.2.1 Problem Formulation 4.6.2.2 Solution Quality Evaluation 4.6.3 Structure Optimization of Multi-state System with Two Failure Modes 4.6.3.1 Problem Formulation 4.6.3.2 Solution Quality Evaluation 4.6.4 Structure Optimization for Multi-state System with Fixed Resource Requirements and Unreliable Sources 4.6.4.1 Problem Formulation 4.6.4.2 Solution Quality Evaluation 4.6.4.3 The Output Performance Distribution of a System Containing Identical Elements in the Main Producing Subsystem 4.6.4.4 The Output Performance Distribution of a System Containing Different Elements in the Main Producing Subsystem<

System Software Reliability

Abstract: Computer software reliability has never been so important. Computers are used in areas as diverse as air traffic control, nuclear reactors, real-time military, industrial process control, security system control, biometric scan-systems, automotive, mechanical and safety control, and hospital patient monitoring systems. Many of these applications require critical functionality as software applications increase in size and complexity. This book is an introduction to software reliability engineering and a survey of the state-of-the-art techniques, methodologies and tools used to assess the reliability of software and combined software-hardware systems. Current research results are reported and future directions are signposted. This text will interest: graduate students as a course textbook introducing reliability engineering software; reliability engineers as a broad, up-to-date survey of the field; and researchers and lecturers in universities and research institutions as a one-volume reference.

A general imperfect-software-debugging model with S-shaped fault-detection rate

Abstract: A general software reliability model based on the nonhomogeneous Poisson process (NHPP) is used to derive a model that integrates imperfect debugging with the learning phenomenon. Learning occurs if testing appears to improve dynamically in efficiency as one progresses through a testing phase. Learning usually manifests itself as a changing fault-detection rate. Published models and empirical data suggest that efficiency growth due to learning can follow many growth-curves, from linear to that described by the logistic function. On the other hand, some recent work indicates that in a real industrial resource-constrained environment, very little actual learning might occur because nonoperational profiles used to generate test and business models can prevent the learning. When that happens, the testing efficiency can still change when an explicit change in testing strategy occurs, or it can change as a result of the structural profile of the code under test and test-case ordering.

Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks

Abstract: In this paper, we develop a generalized multi-state degraded system reliability model subject to multiple competing failure processes, including two degradation processes, and random shocks. The operating condition of the multi-state systems is characterized by a finite number of states. We also present a methodology to generate the system states when there are multi-failure processes. The model can be used not only to determine the reliability of the degraded systems in the context of multi-state functions, but also to obtain the states of the systems by calculating the system state probabilities. Several numerical examples are given to illustrate the concepts.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Classification and regression trees

Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

Reliability Engineering and System Safety

Abstract: This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].