An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this dat...

Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder framework. To make encoder-decoder models more flexible, attention mechanism was introduced to machine translation and also other tasks like speech recognition and image captioning. We observe that the quality of translation by attention-based encoder-decoder can be significantly damag...

In this paper we study the performance and domain scalability of two different Neural Network architectures for Natural Language Generation in Spoken Dialogue Systems. We found that by imposing a sigmoid gate on the dialogue act vector, the Semantically Conditioned Long Short-term Memory generator can prevent semantic repetitions and achieve better performance across all domains compared to an ...

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder (DCAE) for small negative samples. The proposed DCAE combines the advantages of supervised learning and unsupervised learning. In order to reduce high cost training Neural Networks, this pre-trained Networks (CNN) through open labelled datasets. Through transferring learning, encoder part trad...

In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks a...

abstract infiltration process is one of the most important components of the hydrological cycle. on the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. in this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and artificial neural networks (anns) was investigated. for...

introduction to extract and combine information from different modalities, fusion techniques are commonly applied to promote system performance. in this study, we aimed to examine the effectiveness of fusion techniques in emotion recognition. materials and methods electrocardiogram (ecg) and galvanic skin responses (gsr) of 11 healthy female students (mean age: 22.73±1.68 years) were collected ...

abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

abstract-manual harvesting of saffron as a laborious and exhausting job; it not only raises production costs, but also reduces the quality due to contaminations. saffron quality could be enhanced if automated harvesting is substituted. as the main step towards designing a saffron harvester robot, an appropriate algorithm was developed in this study based on image processing techniques to recogn...

we establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. a corresponding novel neural network model, which is globally convergent and stable in the sense of lyapunov, is proposed. both theoretical and numerical approaches are considered. numerical simulations for three constrained nonlinear optimization problems a...