1,720,994 research outputs found
A Multiple Neural Network System to Classify Solder Joints on Integrated Circuits
No full textThe following paper introduces a diagnostic
process to detect solder joint defects on Printed Circuit Boards
assembled in Surface Mounting Technology. The diagnosis is
accomplished by a Neural Network System which processes the
images of the solder joints of the integrated circuits mounted on
the board. The board images are acquired and then preprocessed
to extract the regions of interest for the diagnosis
which are the solder joints of the integrated circuits. Five
different levels of solder quality in respect to the amount of
solder paste have been defined. Two feature vectors have been
extracted from each region of interest, the “geometric” feature
vector and the “wavelet” feature vector. Both vectors feed the
neural network system constituted by two Multi Layer
Perceptron neural networks and a Linear Vector Quantization
network for the classification. The experimental results are
devoted to comparing the performances of a Multi Layer
Perceptron network, of a Linear Vector Quantization network,
and of the overall neural network system, considering both
geometric and wavelet features. The results prove that the
overall classifier is the best compromise in terms of recognition
rate and time required for the diagnosis in respect to the single
classifiers
Graph Adjacency Matrix Associated with a Data Partition
No full textA frequently recurring problem in several applications is to compare two or more data sets and evaluate the level of similarity. In this paper we describe a technique to compare two data partitions of different data sets. The comparison is obtained by means of matrices called Graph Adjacency Matrices which represent the data sets. Then, a match coefficient returns an estimation of the level of similarity between the data sets
Genetic Feature Selection and Statistical Classification of Voids in Concrete Structure
No full textIn this work simulated ultrasonic waveforms in a concrete specimen obtained by a software based on finite element method were used to develop an automatic inspection method. A piezoelectric transducer is used to generate stress waves that are reflected by voids. Then the waves are received by another transducer set at a fixed distance from the first one on the same specimen surface. Time and frequency features has been extracted from the waveforms, the most significant features have been chosen by a genetic feature selection and the classification performances were estimated referring to a k-NN classifier
A New Technique for the Formulation of the Constraint Equation for Time-Variant Topology Electrical Circuits
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Ultrasonic Wave Feature Extraction for Neural Network Classification in not Accessibile Pipes
No full textClassification of Defects on Pipes by Ultrasonic Waveform Using a Neural Network Approach
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