Improving predictive quality of kriging metamodel by variogram adaptation

Application of interpolation/approximation techniques (metamodels, for brevity) is commonly adopted in numerical optimization, typically to reduce the overall execution time of the optimization process. A limited number of trial solution are computed, cov- ering the design variable space: those trial points are then used for the determination of an estimate of the objective function in any desired location of the design space. The behaviour of the prediction of the objective function in between two trial points depends on the structure of the adopted metamodel, and there is no possibility, in principle, to determine a priori if one method is preferable to another. Nevertheless, some metamodels require the adjustment of a set of tuning parameters, and this operation is critical for the prevision qualities of the metamodel. In this paper, some base parameters of the kernel of the kriging metamodel are tuned in order to improve the overall quality of the prediction

Regularization of Kriging interpolation on irregularly spaced data

Interpolation models are critical for a wide range of applications, from numerical optimization to artificial intelligence. The reliability of the provided interpolated value is of utmost importance, and it is crucial to avoid the insurgence of spurious noise. Noise sources can be prevented using proper countermeasures when the training set is designed, but the data sparsity is inevitable in some cases. A typical example is represented by the application of an optimization algorithm: the area where the minimum or maximum of the objective function is assumed to be present is where new data is abundantly added, but other areas of design variable space are significantly neglected. In these cases, a regularization of the interpolation model becomes absolutely crucial. In this paper we are presenting an approach for the regularization of an interpolator based on the control of its kernel function via the condition number of the self-correlation matrix

Sequential quadrature methods for RDO

Abstract This paper presents a comparative study between a large number of different existing sequential quadrature schemes suitable for Robust Design Optimization (RDO), with the inclusion of two partly original approaches. Efficiency of the different integration strategies is evaluated in terms of accuracy and computational effort: main goal of this paper is the identification of an integration strategy able to provide the integral value with a prescribed accuracy using a limited number of function samples. Identification of the different qualities of the various integration schemes is obtained utilizing both algebraic and practical test cases. Differences in the computational effort needed by the different schemes is evidenced, and the implications on their application to practical RDO problems is highlighted

Multidisciplinary design optimization of a sailplan

In this paper, multi-disciplinary optimization techniques are applied to sail design. Two different mathematical models, providing the solution of the fluid-dynamic and the structural problems governing the behaviour of a complete sailplan, are coupled in a fluid-structure interaction (FSI) scheme, in order to determine the real flying shape of the sails and the forces acting on them. A numerical optimization algorithm is then applied, optimizing the structural pattern of the sailplan in order to maximize the driving force or other significant quantities

Automatic generation of user interfaces using the set description language

We present a paradigm to generate automatically graphical user interfaces from a formal description of the data model following the well-known model-view-control paradigm. This paradigm provide complete separation between data model and interface description, setting the programmer free from the low-level aspects of programming interfaces, letting him take care of higher level aspects. The interface along with the data model is described by means of a formal language, the Set Description Language. We also describe the infrastructure based on this paradigm we implemented to generate graphical user interfaces for generic applications. Moreover, it can adapt the user interface of a program to the needs derived from the type of data managed by the user from time to time

GAIML: A New Language for Verbal and Graphical Interaction in Chatbots

Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical) to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML) an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue

Three-dimensional geometrical models of the inguinal region. Towards a new stereology

In this work we studied the inguinal-abdominal region and the inguinal canal using three-dimensional geometrical models. We built the models through computer aided geometric modeling techniques on the basis of observations during real dissections, operations and diagnostic medical imaging. The obtained models show in a complete modular synthesis and with a schematic iconology the structural organization of the anatomical districts in a logic sequence of layers and topographic and spatial relationships among its components. The models represent an amazing support to anatomy and clinical anatomy for teaching and research purposes on organogenesis, surgery and diagnosis