Search

FuzzyPred is an unsupervised-learning data-mining algorithm, which allows extracting fuzzy predicates in normal forms from the data. This method is used to solve a descriptive task where it is not known the kind of relationships that are to be found. The goal is to find patterns that describe the data and their relationships. Due to the large set of solutions or search space that may have, it was modeled as an optimization problem, where metaheuristics are applied to find good solutions. FuzzyPred provides as a result a set of predicates, evaluated in each of the quality measures, in spite of the fact that only one of the measures is optimized (truth value). This paper introduces a multiobjective approach for FuzzyPred by using two of the most known multi-objective optimization techniques: Pareto technique (or pure multi-objective) and weighted factors. An experimental study is presented in order to compare the efficacy of both techniques in this problem. The results in several international databases show that better results are obtained by the pure multi-objective technique. [ABSTRACT FROM AUTHOR]

The development of autonomous vehicles is a subject of great interest in the international scientific community. For the accomplishment of missions without human intervention it is vitally to have an algorithm or guiding law. In this paper we present the results obtained by simulation with the guidance algorithm based on distance L1, during the tracking of straight and circular paths by an autonomous aerial vehicle (UAV). The algorithm produces a lateral acceleration command that is interpreted by the balancing controller to achieve convergence to the path. The results of the simulation demonstrate the good performance of the algorithm, for both straight and circular paths. [ABSTRACT FROM AUTHOR]

This paper describes the use of model-based design flow in the development of self-configurable image processing blocks over FPGAs. For this, some generic hardware architectures has been created and modified using a novel software proceeding using the specific characteristics of the different processing algorithms. This aspect, in addition to others configuration options of the blocks, allows the adjustment the FPGA resources consumption for a specific application. Also the automatic reconfiguration process is analyzed for a generic convolution block making some architectural comparisons over a Spartan-6 LX45 FPGA. [ABSTRACT FROM AUTHOR]

In this paper the technology object constitutes the fermenter R1 of the EPOVAC production plant of the Molecular Immunology Center; in which continuous fermentation of animal cells by perfusion techniques is performed. The overall objective of the research is to solve the problems of PID loop controller of the percent of dissolved oxygen through the design of automatic control system for this variable. Using experimental techniques by identifying the mathematical model of dynamics percent of dissolved oxygen is obtained. It was decided to use PID algorithms to resolve the problems in this loop. [ABSTRACT FROM AUTHOR]

This paper addresses a new algorithm for blind demodulation of BFSK signals by means of two techniques; the Early-Late Gate Synchronizer and the Quadratic Receiver. In order to implement the system, the cross-correlation factor between sections of the signal is obtained in order to estimate the transition between consecutives symbols. The performance of the proposed algorithm is described through several simulations with BFSK signals. [ABSTRACT FROM AUTHOR]

The monitoring of small magnitude faults is a specification that should be accomplished by data-driven fault diagnosis techniques in multimode industrial processes. This is a challenging problem because it is difficult to identifythedata of each stable operation mode considering the presence the transitions between them and the noise of the measurements. A method for the identification of stable operation modes is proposed in this paper. This is based in a modification of the clustering algorithm known as k-medians and is named incremental k-medians. His main advantages are: is robust against the presence of transitions, outliers and noise in the dataset, it is not required to know the number of modes previously to parameterize it and it is not required to make multiple runs of it. The monitoring scheme is built upon the identified modes. The proposed strategy is tested using a continuous stirred tank heater (CSTH). Results obtained for a fault with different magnitude in each mode demonstrate the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]