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This paper presents the study of time series in gravitational lensing to solve the time delay problem in astrophysics. The time series are irregularly sampled and noisy. There are several methods to estimate the time delay between this kind of time series, and this paper proposes a new method based on artificial neural networks, in particular, General Regression Neural Networks (GRNN), which is based...
We present a method to reduce noise on signals applying complex auto-associative neural networks. Experimental results using the sine, triangular and sawtooth signals are performed to validate our results. This method is based on the use of complex neural networks learning and is capable of eliminating or reducing noise on learned signals. First, a training set consisting of signal values without...
Learning how to count in different bases has been seen as a trivial task in almost all introductory mathematics courses. However, the low performance shown by many students, while performing this task, is appalling. This situation has motivated serious research in this matter. In order to study a model of count learning, we analyze the performance of a multilayer perceptron that learns to count in...
This paper studies different vehicle fault prediction techniques, using artificial neural network and fuzzy logic based model. With increasing demands for efficiency and product quality as well as progressing integration of automatic control systems in high-cost mechatronics and safety-critical processes, monitoring is necessary to detect and diagnose faults using symptoms and related data. However,...
Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple and conflicting objectives (makespan and minimizing waiting time for instance) to satisfy. In this study, we propose an effective approach based an artificial neural network technique embedded in a multi-objective optimization loop for multi-decision scheduling problems in a semiconductor wafer...
The present study describes the design of an Artificial Neural Network to synthesize the Approximation Function of a Pedometer for the Healthy Life Style Promotion. Experimentally, the approximation function is synthesized using three basic digital pedometers of low cost, these pedometers were calibrated with an advanced pedometer that calculates calories consumed and computes distance travelled with...
This work presents an infant cry automatic recognizer development, with the objective of classifying two kinds of infant cries, normal and pathological, from recently born babies. Extraction of acoustic features is used such as MFCC (Mel Frequency Cepstral Coefficients), obtained from Infant Cry Units sound waves, and a genetic feature selection system combined with a feed forward input delay neural...
Leaks on pipelines can cause strong economic losses and environmental problems if these are not detected on time. The problem of detecting leaks is even more complicated when the pipelines are too large, difficult to reach by maintenance personnel, and equipped with minimum instrumentation. A comparison of four fault diagnosis approaches based on Output Observers, Artificial Neural Networks, Particle...
The compressive strength of mixtures made with scrap tire id presented. In this study, neural network modeling was applied for predicting compressive strength of mixtures containing variable size of tire scrap. This modeling allows avoiding a large number of trial mixtures tests and provides a new alternative for designing new constructive components at lower costs. Results shown that neural model...
Machining empirical models for surface roughness based on statistical or artificial intelligence (AI) approaches have been intensively studied during last decades. However, there is no industrial methodology to be applied in industry due to the time consuming and costly experimental procedures required. Furthermore, continuous changes in the cutting process such as cutting-tool replacements or changes...
In this paper a method for the prediction of vehicle velocities is described that can be used for any point of time in the future. The approach is based on a two step clustering which uses toll transaction data of the training of the model. The results are different clusters for each road segment, containing the velocity value and its probability of occurrences. Furthermore the results of the method...
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