The presented work reports on the progress of our methodology and framework for automated image processing and analysis systém design for industrial vision application. We focus on the important task of automated texture analysis, which is an essential component of automated quality-control systems. In this context, the portfolio of texture operators and assessment methods has been enlarged. Optimized operator parameterization is investigated using particle swarm optimization (PSO). A particular goal of this work is the investigation of support vector machines (SVM) as alternative assessment method for the operator parameter optimization, incorporating the efficient inclusion of SVM parameter settings in this optimization. Methods of the enhanced portfolio were applied employing benchmark textures, real application data from leather inspection, and synthetic textures including defects, specially designed to industrial needs. The key results of our work are that SVM is a highly esteemed and powerful assessment and classification method and parameter optimization, based, e.g., on SVM/PSO of standard and proprietary texture operators boosted performance in all cases. However, the appropriateness of a certain operator proved to be highly data-dependent, which advocates our methodology even more. Thus, the operator selection has been included and investigated for the synthetic textures. Summarising, our work provides a generic texture analysis system, even for unskilled users, that is automatically configured to the application. The method portfolio will be enlarged in future work.
This work presents a hybridized neuro-genetic control solution for R³ workspace application. The solution is based on a multi-objective genetic algorithm reference generator and an adaptive predictive neural network strategy. The trajectory calculation between two points in an R3 workspace is a complex optimization problem considering the fact that there are multiple objectives, restrictions and constraint functions which can play an important role in the problem and be in competition. We solve this problem using genetic algorithms, in a multi objective optimization strategy. Subsequently, we enhance a training algorithm in order to achieve the best adaptation of the neural network parameters in the controller which is responsible for generating the control action for a nonlinear system. As an application of the proposed hybridized control scheme, a crane tracking control is presented.
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in Computational Grids. An efficient scheduling of jobs to Grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of Grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics.
In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources.
The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.
We develope a kinematic model based on a dynamical model of stellar population synthesis. We compare the model predictions with two proper motion catalogues.
In this paper, an expert system that performs route planning using dynamic traffic data is introduced. Also an algorithmic approach is introduced to find the shortest path in a three-dimensional. Using both implementations, a comparison is made between the expert system approach and the algorithmic approach. It is concluded that the expert system shows great potential. The expert system indeed finds the best routes, and it outperforms the algorithm approach in computation time, too.
This paper presents a Komlós theorem that extends to the case of the set-valued Henstock-Kurzweil-Pettis integral a result obtained by Balder and Hess (in the integrably bounded case) and also a result of Hess and Ziat (in the Pettis integrability setting). As applications, a solution to a best approximation problem is given, weak compactness results are deduced and, finally, an existence theorem for an integral inclusion involving the Henstock-Kurzweil-Pettis set-valued integral is obtained.
Using the concept of $\mathcal {I}$-convergence we provide a Korovkin type approximation theorem by means of positive linear operators defined on an appropriate weighted space given with any interval of the real line. We also study rates of convergence by means of the modulus of continuity and the elements of the Lipschitz class.
Mean leaf inclination of the arctic and alpine shrub Dryas octopetala is a function of latitude and this functional relationship is consistent with a model that maximizes photosynthesis of the total plant canopy.
In this paper, a learning algorithm for a novel neural network architecture motivated by Integrate-and-Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that inclusion of a few more biological phenomenon in the formulation of artificial neural networks make them more prevailing. Several benchmark and real-life problems of classification and function-approximation are illustrated.