Comparative Study of Data-Driven Models in Motor RUL Estimation

Ahin Banerjee ◽  
Sanjay K. Gupta ◽  
Chandrasekhar Putcha
2021 ◽  
Vol 12 ◽  
Amel Karoui ◽  
Mostafa Bendahmane ◽  
Nejib Zemzemi

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

2014 ◽  
Vol 28 ◽  
pp. 1-12 ◽  
Zhongliang Li ◽  
Rachid Outbib ◽  
Daniel Hissel ◽  
Stefan Giurgea

2019 ◽  
Vol 12 (18) ◽  
Kamal Ghaderi ◽  
Baharak Motamedvaziri ◽  
Mehdi Vafakhah ◽  
Amir Ahmad Dehghani

2013 ◽  
Vol 1 ◽  
pp. 301-314 ◽  
Weiwei Sun ◽  
Xiaojun Wan

We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.

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