Modeling the Electromyogram (EMG) of Patients Undergoing Anesthesia During Surgery

Casteleiro‐Roca, José‐Luis; Pérez, José Andrés Moreno; Piñón-Pazos, A.; Calvo-Rolle, José Luís; Corchado, Emilio

doi:10.1007/978-3-319-19719-7_24

Cited by 27 publications

(7 citation statements)

References 33 publications

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“…Some previous works have shown the use of these methods despite its low performance [33,[38][39][40]. In this paper, to overcome this limitation, we propose to use hybrid intelligent system to accomplish the regression task, more specifically, an artificial neural network (ANN) hybrid system as the ones used in [41][42][43][44][45][46], since ANNs allow obtaining simple and very accurate nonlinear models [47][48][49][50].…”

Section: Fuel Outputmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.

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Section: Fuel Outputmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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“…Now, with IoT, a new era begins with a post-cloud name, where there will be a large amount of data generated by things that are immersed in our daily lives, and many applications will also be implemented on the edge of these devices to consume these data [21][22][23][24][25][26][27]. By 2015-2020, 26.3 billion of these nodes will be connected to the Internet [27][28][29][30][31][32][33][34][35][36][37][38]. This will cause problems with latency and network bandwith.…”

Section: Introductionmentioning

confidence: 99%

A deep tech architecture for intelligent IoT systems

Öztürk

2020

The Role of Artificial Intelligence and Distributed Computing in IoT Applications

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The increase in the number of connected devices on the Internet of Things (IoT), interactions and the amount of data raises a number of issues. Two major problems are limitations in terms of network latency and bandwidth. While cloud-based infrastructures give us access to scalable, on-demand storage and processing services that can scale to the requirements of the Internet of Things (IoT), these centralized resources can create unacceptable delays and performance problems for devices that have latency-sensitive applications, such as health monitoring and emergency response applications. This article has been created for the PhD thesis that aims to create a deep tech architecture for intelligent IoT systems.

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“…The well‐known support vector machine algorithm employed in many different applications (Schölkopf et al, ; Calvo‐Rolle et al, ; Casteleiro‐Roca et al, ; Jove et al, ) is frequently used to solve the one‐class classification problem as well as support vector data description (Tax, ). To solve anomalies issues in different parts of industrial plants, the use of virtual sensors or missing data imputation techniques is very common (Casteleiro‐Roca et al, ; Fernández‐Serantes et al, ; Jove et al, ; Casteleiro‐Roca et al, ; Gonzalez‐Cava et al, ).…”

Section: Introductionmentioning

confidence: 99%

A fault detection system based on unsupervised techniques for industrial control loops

et al. 2019

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This research describes a novel approach for fault detection in industrial processes, by means of unsupervised and projectionist techniques. The proposed method includes a visual tool for the detection of faults, its final aim is to optimize system performance and consequently obtaining increased economic savings, in terms of energy, material, and maintenance. To validate the new proposal, two datasets with different levels of complexity (in terms of quantity and quality of information) have been used to evaluate five well‐known unsupervised intelligent techniques. The obtained results show the effectiveness of the proposed method, especially when the complexity of the dataset is high.

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Modeling the Electromyogram (EMG) of Patients Undergoing Anesthesia During Surgery

Cited by 27 publications

References 33 publications

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

A deep tech architecture for intelligent IoT systems

A fault detection system based on unsupervised techniques for industrial control loops

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