Abstract

Abstract
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

Abstract
The multi-agent systems technology is a proper approach to implement distributed manufacturing systems exhibiting adaptation and flexibility. This paper proposes a multi-agent based solution for the adaptation of the functional test plan in a production line producing washing machines, aiming to increase the process productivity and product quality. The global adaptation mechanism is embedded on the multi-agent system infrastructure, allowing the optimized selection of tests based on the correlation of the quality data gathered along the production line. The proposed approach was developed and installed on a real production line producing washing machines under the European Seventh Framework Programme GRACE project.

Abstract

Abstract

Abstract
Segmentation is a vital step for pattern recognition systems used in in-body imaging scenarios. In this paper we compare the performance of three popular segmentation algorithms (mean shift, normalized cuts, level-sets) when applied to two distinct in-body imaging scenarios: chromoendoscopy and narrow-band imaging. Observation shows that the model-based algorithm did not perform well, when compared to its segmentation by clustering alternatives. Normalized cuts obtained the best performance although future work hints that texture similarity should be further explored in order to increase segmentation performance in this type of scenarios.