REVISIÓN DE TÉCNICAS DE SISTEMAS DE VISIÓN ARTIFICIAL PARA LA INSPECCIÓN DE PROCESOS DE SOLDADURA TIPO GMAW

El proceso de soldadura GMAW es ampliamente estudiado debido a su alta productividad y bajo costo. En este trabajo se han revisado las investigaciones orientadas a la inspección del proceso de GMAW a través de sistemas de visión artificial con el objetivo de establecer los principales elementos utilizados en estos sistemas destacando dos categorías: métodos computacionales (software y algoritmos generales), materiales y modelos matemáticos (métodos estadísticos y numéricos). Estas categorías se traslapan en el estudio y se han utilizado para evaluar el costo en términos de recursos humanos y recursos económicos. Las investigaciones revisadas se desarrollaron en la última década, con la excepción de algunas investigaciones que desempeñaron un papel principal en el desarrollo de los sistemas de inspección de los procesos GMAW. Finalmente, se han destacado los posibles campos de investigación para aquellos que intentan explorar sistemas de visión artificial para inspección de procesos GMAW.Palabras clave: GMAW, soldadura, visión artificial, inspección

A review of Kalman filter with artificial intelligence techniques

Kalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called "black box". In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance. Based on the review, we categorised papers into four groups according to the role of AI as follows: 1) Methods tuning parameters of KF, 2) Methods compensating errors in KF, 3) Methods updating state vector or measurements of KF, and 4) Methods estimating pseudo-measurements of KF. In the concluding section of this paper, we pointed out the directions for future research that suggestion to focus on more research for combining the categorised groups. In addition, we presented the suggestion of beneficial approaches for representative applications

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets

Navigation with Artificial Neural Networks

The objective of this dissertation is to explore the applications for Artificial Neural Networks (ANNs) in the field of Navigation. The state of the art for ANNs has improved significantly so now they can rival and even surpass humans in problems once thought impossible. We present different methods to augment, combine, or replace existing Navigation filters with ANN. The main focus of these methods is to use as much existing knowledge as possible then use ANNs to extend the current knowledge base. Next, improvements are made for a class of Artificial Neural Network (ANN)s which provide covariance called Mixture Density Network (MDN)s. MDNs are necessary since covariance is required for navigation problems. Finally the improvements and framework are demonstrated in a Very Low Frequency (VLF) signals navigation problem. Without ANNs, our VLF signals navigation problem would be very difficult. We conduct two VLF navigation experiments with an indoor and outdoor environment. The ANNs used for these problems provide confidence with probabilistic estimates of position either through class probabilities or probability distributions parameterized by the output of MDNs. ANNs need a measure of confidence in their estimates to work with the filters since navigation filters require a confidence of their estimates. In our problems we achieve an indoor localization accuracy of 86.7% for 50 discrete locations, and a 2D RMS error of 63m for a 1km2 area of navigation