Abstract: Except for simple problems of statically determinate structures, optimum design problems in structural engineering have implicit objective functions where structural analysis and design are essential within each searching loop. With these implicit functions, the structural engineer is usually enforced to write his/her own computer code for analysis, design, and searching for optimum design among many feasible candidates and cannot take advantage of available software for structural analysis, design, and searching for the optimum solution. The meta-model is a regression model used to transform an implicit objective function into objective one and leads in turn to decouple the structural analysis and design processes from the optimum searching process. With the meta-model, well-known software for structural analysis and design can be used in sequence with optimum searching software. In this paper, the meta-model has been used to develop an explicit objective function for plane steel frames subjected to dead, live, and seismic forces. Frame topology is assumed as predefined based on architectural and functional requirements. Columns and beams sections and different connections details are the main design variables in this study. Columns and beams are grouped to reduce the number of design variables and to make the problem similar to that adopted in engineering practice. Data for the implicit objective function have been generated based on analysis and assessment for many design proposals with CSI SAP software. These data have been used later in SPSS software to develop a pure quadratic nonlinear regression model for the explicit objective function. Good correlations with a coefficient, R2, in the range from 0.88 to 0.99 have been noted between the original implicit functions and the corresponding explicit functions generated with meta-model.
Abstract: This paper describes how to dimension the electric
components of a 48V hybrid system considering real customer use.
Furthermore, it provides information about savings in energy and
CO2 emissions by a customer-tailored 48V hybrid. Based on measured customer profiles, the electric units such as the
electric motor and the energy storage are dimensioned. Furthermore,
the CO2 reduction potential in real customer use is determined
compared to conventional vehicles. Finally, investigations are carried
out to specify the topology design and preliminary considerations in
order to hybridize a conventional vehicle with a 48V hybrid system.
The emission model results from an empiric approach also taking into
account the effects of engine dynamics on emissions. We analyzed
transient engine emissions during representative customer driving
profiles and created emission meta models. The investigation showed
a significant difference in emissions when simulating realistic
customer driving profiles using the created verified meta models
compared to static approaches which are commonly used for vehicle
simulation.
Abstract: This survey paper shows the recent state of model
comparison as it’s applies to Model Driven engineering. In Model
Driven Engineering to calculate the difference between the models is
a very important and challenging task. There are number of tasks
involved in model differencing that firstly starts with identifying and
matching the elements of the model. In this paper, we discuss how
model matching is accomplished, the strategies, techniques and the
types of the model. We also discuss the future direction. We found
out that many of the latest model comparison strategies are geared
near enabling Meta model and similarity based matching. Therefore
model versioning is the most dominant application of the model
comparison. Recently to work on comparison for versioning has
begun to deteriorate, giving way to different applications. Ultimately
there is wide change among the tools in the measure of client exertion
needed to perform model comparisons, as some require more push to
encourage more sweeping statement and expressive force.
Abstract: Through this paper we present a method for automatic
generation of ontological model from any data source using Model
Driven Architecture (MDA), this generation is dedicated to the
cooperation of the knowledge engineering and software engineering.
Indeed, reverse engineering of a data source generates a software
model (schema of data) that will undergo transformations to generate
the ontological model. This method uses the meta-models to validate
software and ontological models.
Abstract: The software evolution control requires a deep
understanding of the changes and their impact on different system
heterogeneous artifacts. And an understanding of descriptive
knowledge of the developed software artifacts is a prerequisite
condition for the success of the evolutionary process.
The implementation of an evolutionary process is to make changes
more or less important to many heterogeneous software artifacts such
as source code, analysis and design models, unit testing, XML
deployment descriptors, user guides, and others. These changes can
be a source of degradation in functional, qualitative or behavioral
terms of modified software. Hence the need for a unified approach
for extraction and representation of different heterogeneous artifacts
in order to ensure a unified and detailed description of heterogeneous
software artifacts, exploitable by several software tools and allowing
to responsible for the evolution of carry out the reasoning change
concerned.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency