41 research outputs found
Using the Sharp Operator for edge detection and nonlinear diffusion
In this paper we investigate the use of the sharp function known from functional analysis in image processing. The sharp function gives a measure of the variations of a function and can be used as an edge detector. We extend the classical notion of the sharp function for measuring anisotropic behaviour and give a fast anisotropic edge detection variant inspired by the sharp function. We show that these edge detection results are useful to steer isotropic and anisotropic nonlinear diffusion filters for image enhancement
Design of parameter-scheduled state-feedback controllers using shifting specifications
In this paper,the problem of designing aparameter-scheduled state-feedback controller is investigated. The paper presents an extension of the classical regional pole placement, H2 control and H1 control problems, so as to satisfy new specifications, that will be referred to as shifting pole placement control, shifting H2 control and shifting H1 control, respectively. By introducing some parameters, or using the existing ones, the controller can be designed in such away that different values of the separameters imply different regions where the closed-loop poles are situated, or different performances in the H2 or H1 sense. The proposed approach is derived within the so-called Lyapunov Shaping Paradigm, where a single quadratic Lyapunov function is used for ensuring stability and desired performances in spite of arbitrary parameter time variation. The problem is analyzed in the continuous-time LPV case, oventhough the developed theory could be applied to LTI systems in cases when it is desired to vary the control system performances online. Results obtained in simulation demonstrate the effectiveness and the relevant features of the proposed approach.Peer ReviewedPostprint (published version
A neuro-fuzzy self built system for prognostics : a way to ensure good prediction accuracy by balancing complexity and generalization.
International audienceIn maintenance field, prognostics is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. However, it can be a non trivial task to develop and implement effective prognostics models including the inherent uncertainty of prognostics. Moreover, there is no systematic way to construct a prognostics tool since the user can make some assumptions: choice of a structure, initialization of parameters... This last problem is addressed in the paper: how to build a prognostics system with no human intervention, neither a priori knowledge? The proposition is based on the use of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based on the Akaike information criterion. It consists in using a cost function in the learning phase in order to automatically generate an accurate prediction system that reaches a compromise between complexity and generalization capability. The proposition is illustrated and discussed
Structure and pressure drop of real and virtual metal wire meshes
An efficient mathematical model to virtually generate woven metal wire meshes is
presented. The accuracy of this model is verified by the comparison of virtual structures with three-dimensional
images of real meshes, which are produced via computer tomography. Virtual structures
are generated for three types of metal wire meshes using only easy to measure parameters. For these
geometries the velocity-dependent pressure drop is simulated and compared with measurements
performed by the GKD - Gebr. Kufferath AG. The simulation results lie within the tolerances of
the measurements. The generation of the structures and the numerical simulations were done at
GKD using the Fraunhofer GeoDict software
A variational model for innovation diffusion under fuzzy uncertainty
We propose a variational approach to study the market penetration of new technologies under conditions of spatial heterogeneity in the economic factors influencing the process and imprecise knowledge about their intensities. Differently from other methodologies that describe the adoption process in terms of partial differential equations, we formulate the model as a minimization problem of an appropriate variational functional with fuzzy coefficients. This approach permits to consider in the analysis both the attractive and diffusive forces that drive the process and the subjective opinions of policy makers about the actual influence exerted by these determinants on the adoption decision. Interestingly, our results show that different degrees of uncertainty lead to significantly different predictions about the diffusion process and, therefore, our methodology could be applied to support strategic decisions concerning innovation diffusion plans. An application to the digital transition in agriculture is also provided to study the effectiveness of government policies
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Efficient Thermal Image Segmentation through Integration of Nonlinear Enhancement with Unsupervised Active Contour Model
Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions.
Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks.
The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance
"Applications of Intelligent Systems in Tourism: Relevant Methods"
"This article presents a literature review of Intelligent Systems applications in Tourism in different
parts of the world. The review focuses on the most relevant methods in free and paid databases, in
English and Spanish. Most of the works deal with methodologies based on artificial intelligence,
such as expert systems, fuzzy logic, machine learning, data mining, neural networks, genetic
algorithms. In the review, several characteristics of the systems have been taken into account, such
as, knowledge base, actors in the operation of the system, types of tourists, usefulness or not in space
and time. According to the review it was found that most of the researches are deterministic models,
the most used methodology in tourism are the expert systems based on rules, observing an emerging
innovation in metaheuristics, mainly genetic algorithms and intelligent systems with knowledge
base based on optimization methods for route choice or optimal visit plan. It is expected that this
work serves to give a general perspective on the application of intelligent systems in the area of
tourism, as well as a work that consolidates background for work in this area of research.