Refinement and variability techniques in model transformation of software requirements

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Dependence of the crossover exponent with the diffusion rate in the generalized contact process model

We study how the crossover exponent, phi, between the directed percolation (DP) and compact directed percolation (CDP) behaves as a function of the diffusion rate in a model that generalizes the contact process. Our conclusions are based in results pointed by perturbative series expansions and numerical simulations, and are consistent with a value phi = 2 for finite diffusion rates and phi = 1 in the limit of infinite diffusion rate.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

SMPI model : a stepwise process model to facilitate software measurement process improvement along the measurement paradigms

Software engineering, software measurement, software process engineering, capability, maturity

Developing A Knowledge Management Process Model for Internal IT Organization, Case: Stora Enso Oyj, Corporate IT

The objectives of this Master’s Thesis were to find out what kind of knowledge management strategy would fit best an IT organization that uses ITIL (Information Technology Infrastructure Library) framework for IT Service Management and to create a knowledge management process model to support chosen strategy. The empirical material for this research was collected through qualitative semi-structured interviews of a case organization Stora Enso Corporate IT. The results of the qualitative interviews indicate that codification knowledge management strategy would fit best for the case organization. The knowledge management process model was created based on earlier studies and a literature of knowledge management. The model was evaluated in the interview research and the results showed that the created process model is realistic, useful, and it responds to a real life phenomenon.

Creating a Branding Process Model for Living Area Brands - Developing and managing a new living area brand

The objective of this thesis is to understand how to create and develop a successful place brand and how to manage it systematically. The thesis thoroughly explains the phenomenon of place brands and place branding and presents different sub-categories of place branding. The theoretical part of the thesis provides a wide overview on the prevailing literature of place branding, place brand development and place brand management, which form the basis of the thesis’ theoretical framework. The theoretical evidence is gathered from a case living area. The living area is developed by one construction company, which has a significant role in the construction industry in Finland. The empirical evidence is gathered through semi-structured in-depth interviews by interviewing the new living area’s carefully selected stakeholder groups. Afterwards the empirical data is analyzed and reflected to the theoretical findings. After examining the case living area, the thesis will present a new living area branding process model based on prevailing theories and empirical findings.

EnvironalPlanning: an analytical network process model for environmentally conscious construction planning

Although the construction pollution index has been put forward and proved to be an efficient approach to reducing or mitigating pollution level during the construction planning stage, the problem of how to select the best construction plan based on distinguishing the degree of its potential adverse environmental impacts is still a research task. This paper first reviews environmental issues and their characteristics in construction, which are critical factors in evaluating potential adverse impacts of a construction plan. These environmental characteristics are then used to structure two decision models for environmental-conscious construction planning by using an analytic network process (ANP), including a complicated model and a simplified model. The two ANP models are combined and called the EnvironalPlanning system, which is applied to evaluate potential adverse environmental impacts of alternative construction plans.

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Double sampling (x)over-bar control chart for a first-order autoregressive moving average process model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A software development process model for gesture-based interface

Gesture-based applications have particularities, since users interact in a natural way, much as they interact in the non-digital world. Hence, new requirements are needed on the software design process. This paper shows a software development process model for these applications, including requirement specification, design, implementation, and testing procedures. The steps and activities of the proposed model were tested through a game case study, which is a puzzle game. The puzzle is completed when all pieces of a painting are correctly positioned by the drag and drop action of users hand gesture. It also shows the results obtained of applying a heuristic evaluation on this game. © 2012 IEEE.

Development of a process model for the automatic miniload warehouse expansion

This paper provides an insight to the development of a process model for the essential expansion of the automatic miniload warehouse. The model is based on the literature research and covers four phases of a warehouse expansion: the preparatory phase, the current state analysis, the design phase and the decision making phase. In addition to the literature research, the presented model is based on a reliable data set and can be applicable with a reasonable effort to ensure the informed decision on the warehouse layout. The model is addressed to users who are usually employees of logistics department, and is oriented on the improvement of the daily business organization combined with the warehouse expansion planning.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.