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Modeling Methodology (modeling + methodology)
Selected AbstractsProfit Maximizing Warranty Period with Sales Expressed by a Demand FunctionQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2007Shaul P. Ladany Abstract The problem of determining the optimal warranty period, assumed to coincide with the manufacturer's lower specification limit for the lifetime of the product, is addressed. It is assumed that the quantity sold depends via a Cobb,Douglas-type demand function on the sale price and on the warranty period, and that both the cost incurred for a non-conforming item and the sale price increase with the warranty period. A general solution is derived using Response Modeling Methodology (RMM) and a new approximation for the standard normal cumulative distribution function. The general solution is compared with the exact optimal solutions derived under various distributional scenarios. Relative to the exact optimal solutions, RMM-based solutions are accurate to at least the first three significant digits. Some exact results are derived for the uniform and the exponential distributions. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Methodological Overview of Network Vulnerability AnalysisGROWTH AND CHANGE, Issue 4 2008ALAN T. MURRAY ABSTRACT Evaluating network infrastructures for potential vulnerabilities is an important component of strategic planning, particularly in the context of managing and mitigating service disruptions. Many methods have been proposed to facilitate such analysis, providing different interpretations of infrastructure vulnerability. The primary approaches that have been employed for network vulnerability analysis can be broadly classified as scenario-specific, strategy-specific, simulation, and mathematical modeling methodologies. Research on network vulnerability assessment has traditionally focused on one of these methodologies without consideration of the others. This article highlights the important implications of methodology for both infrastructure planning and policy development. To better understand the theoretical and practical trade-offs associated with methodology selection, this article provides a review of these categories of analysis, examining benefits and shortcomings with regard to practical planning issues and policy interpretation. [source] Modeling of microwave devices with space mapping and radial basis functionsINTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS, Issue 3 2008Slawomir Koziel Abstract We review recent developments in space mapping techniques for modeling of microwave devices. We present a surrogate modeling methodology that utilizes space mapping combined with radial basis function interpolation. The method has advantages both over the standard space mapping modeling methodology and the recently published space mapping modeling with variable weight coefficients. In particular, it provides accuracy comparable or better than the latter method and computational efficiency as good as the standard space mapping modeling procedure. A comparison between the space mapping modeling methodologies as well as application examples of optimization and statistical analysis of microwave structures is presented. Copyright © 2007 John Wiley & Sons, Ltd. [source] A Simulation Model for Life Cycle Project ManagementCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2002Ali Jaafari This paper puts forward a simulation model specifically designed for holistic evaluation of project functionality within a life cycle project management framework. The authors describe a methodology for development of the aforementioned tool, referred to as a dynamic simulation modeling system (DSMS). The DSMS is geared toward modeling of service and manufacturing processes with hierarchical and modular modeling methodology; however, the underlying philosophy can be adopted for modeling any generic system. The enhanced modeling features and logical division of large systems into small process components and their internal linkage are the key contributions of this work. The aim of this development is to apply the simulation technique in order to evaluate the overall project functionalities from the dynamic business perspective. A set of business objective functions (i.e., life cycle objective function [LCOF]) has been employed as a basis for decision making throughout the project's life. Object-oriented programming language with the object-oriented database technology facilitates the necessary model capability. A brief case study has been used to demonstrate and discuss the model capability. [source] Precise disturbance modeling for improvement of positioning performanceELECTRICAL ENGINEERING IN JAPAN, Issue 2 2010Masafumi Yamamoto Abstract This paper presents a modeling methodology for unknown disturbances in mechatronics systems, based on disturbance estimation using an iterative learning process. In disturbance modeling, nonlinear frictions are specially handled as disturbances in the mechanisms, which mainly degrade trajectory control performance. Friction can be mathematically modeled by using learned estimation data as a function of the displacement, velocity. acceleration, and jerk of the actuator. This model has the distinctive feature that friction compensation can be achieved with a generalization capability for different conditions. The proposed positioning control approach with disturbance modeling and compensation has been verified by experiments using a table drive system on a machine stand. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(2): 31,39, 2010; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/eej.20928 [source] Modeling of microwave devices with space mapping and radial basis functionsINTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS, Issue 3 2008Slawomir Koziel Abstract We review recent developments in space mapping techniques for modeling of microwave devices. We present a surrogate modeling methodology that utilizes space mapping combined with radial basis function interpolation. The method has advantages both over the standard space mapping modeling methodology and the recently published space mapping modeling with variable weight coefficients. In particular, it provides accuracy comparable or better than the latter method and computational efficiency as good as the standard space mapping modeling procedure. A comparison between the space mapping modeling methodologies as well as application examples of optimization and statistical analysis of microwave structures is presented. Copyright © 2007 John Wiley & Sons, Ltd. [source] Modeling the electrophoresis of oligoglycinesJOURNAL OF SEPARATION SCIENCE, JSS, Issue 16 2010Stuart A. Allison Abstract The electrophoretic mobility of low molecular mass oligoglycines is examined in this study using a "coarse-grained" bead modeling methodology [Pei, H., Allison, S. A., J. Chromatogr. A 2009, 1216, 1908,1916]. The advantage of focusing on these peptides is that their charge state is well known [Plasson, R., Cottet, H., Anal. Chem. 2006, 78, 5394,5402] and extensive electrophoretic mobility data are also available in different buffers [Survay, M. A., Goodall, D. M., Wren, S. A. C., Rowe, R. C., J. Chromatogr. A 1996, 741, 99,113] and over a broad range of temperatures [Plasson, R., Cottet, H., Anal Chem. 2005, 77, 6047,6054]. Except for assumptions about peptide secondary structure, the B model has no adjustable parameters. It is concluded that the oligoglycines adopt a random configuration at high temperature (50°C and higher), but more compact conformations at lower temperature. It is proposed that triglycine through pentaglycine adopt compact cyclic structures at low temperature (up to about 25°C) in aqueous solution. At 25°C, buffer interactions are also examined and may or may not influence peptide conformation depending on the buffer species. In a borate buffer at high pH, the mobility data are consistent with complex formation between the oligoglycine and borate anion. [source] An Empirical Investigation of Global Sourcing Strategy EffectivenessJOURNAL OF SUPPLY CHAIN MANAGEMENT, Issue 2 2000Kenneth J Petersen SUMMARY This study was undertaken to address the need for empirical research on global sourcing strategy effectiveness. This article establishes the importance of and relationships between several factors that drive the effectiveness of global sourcing strategies. Companies are increasingly viewing global sourcing strategies as a means of reducing cost, increasing quality, and enhancing a firm's overall competitive position. This article uses a structural equation modeling methodology to test an explanatory model of global sourcing strategy effectiveness. Results indicate that global sourcing structures and processes, global sourcing business capabilities, international language capabilities, and top management commitment to global sourcing are critical to the effectiveness of a global sourcing strategy. [source] Distributed inductance and resistance per-unit-length formulas for VLSI interconnects on silicon substrateMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2001H. Ymeri Abstract A new analytic model is presented (the model is based on the induced current density distribution inside silicon substrate) to calculate the frequency-dependent distributed inductance and the associated distributed series resistance of silicon semiconducting VLSI interconnects. The validity of the proposed model has been checked by a comparison with CAD-oriented modeling methodology in conjunction with a quasi-TEM spectral-domain approach. It is found that the silicon semiconducting substrate skin effect must be considered for the accurate prediction of the high-frequency characteristics of VLSI interconnects. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 30: 302,304, 2001. [source] Comparison of linear predictors obtained by data transformation, generalized linear models (GLM) and response modeling methodology (RMM)QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2008Haim Shore Abstract The data-transformation approach and generalized linear modeling both require specification of a transformation prior to deriving the linear predictor (LP). By contrast, response modeling methodology (RMM) requires no such specifications. Furthermore, RMM effectively decouples modeling of the LP from modeling its relationship to the response. It may therefore be of interest to compare LPs obtained by the three approaches. Based on numerical quality problems that have appeared in the literature, these approaches are compared in terms of both the derived structure of the LPs and goodness-of-fit statistics. The relative advantages of RMM are discussed. Copyright © 2007 John Wiley & Sons, Ltd. [source] A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival DataBIOMETRICS, Issue 2 2003Elizabeth R. Brown Summary This article proposes a new semiparametric Bayesian hierarchical model for the joint modeling of longitudinal and survival data. We relax the distributional assumptions for the longitudinal model using Dirichlet process priors on the parameters defining the longitudinal model. The resulting posterior distribution of the longitudinal parameters is free of parametric constraints, resulting in more robust estimates. This type of approach is becoming increasingly essential in many applications, such as HIV and cancer vaccine trials, where patients' responses are highly diverse and may not be easily modeled with known distributions. An example will be presented from a clinical trial of a cancer vaccine where the survival outcome is time to recurrence of a tumor. Immunologic measures believed to be predictive of tumor recurrence were taken repeatedly during follow-up. We will present an analysis of this data using our new semiparametric Bayesian hierarchical joint modeling methodology to determine the association of these longitudinal immunologic measures with time to tumor recurrence. [source] A novel assay based on fluorescence microscopy and image processing for determining phenotypic distributions of rod-shaped bacteriaBIOTECHNOLOGY & BIOENGINEERING, Issue 2 2009Konstantinos Spetsieris Abstract Cell population balance (CPB) models can account for the phenotypic heterogeneity that characterizes isogenic cell populations. To utilize the predictive power of these models, however, we must determine the single-cell reaction and division rates as well as the partition probability density function of the cell population. These functions can be obtained through the Collins,Richmond inverse CPB modeling methodology, if we know the phenotypic distributions of (a) the overall cell population, (b) the dividing cell subpopulation, and (c) the newborn cell subpopulation. This study presents the development of a novel assay that combines fluorescence microscopy and image processing to determine these distributions. The method is generally applicable to rod-shaped cells dividing through the formation of a characteristic constriction. Morphological criteria were developed for the automatic identification of dividing cells and validated through direct comparison with manually obtained measurements. The newborn cell subpopulation was obtained from the corresponding dividing cell subpopulation by collecting information from the two compartments separated by the constriction. The method was applied to E. coli cells carrying the genetic toggle network with a green fluorescent marker. Our measurements for the overall cell population were in excellent agreement with the distributions obtained via flow cytometry. The new assay constitutes a powerful tool that can be used in conjunction with inverse CPB modeling to rigorously quantify single-cell behavior from data collected from highly heterogeneous cell populations. Biotechnol. Bioeng. 2009;102: 598,615. © 2008 Wiley Periodicals, Inc. [source] | ||||||||||