Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

Casteleiro‐Roca, José‐Luis; Calvo-Rolle, José Luís; Pérez, José Andrés Moreno; Gutiérrez, Nieves Roqueñí; Juez, Francisco Javier de Cos

doi:10.3390/s17010179

Cited by 33 publications

(9 citation statements)

References 48 publications

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“…Dividing the initial dataset into different groups before carrying out the regression could be a complementary solution [21,22]. With this objective in mind, the clustering algorithm -means is commonly used [23][24][25]. This method consists of splitting up the entire dataset into a number of clusters.…”

Section: Introductionmentioning

confidence: 99%

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

et al. 2017

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Batteries are one of the principal components in electric vehicles and mobile electronic devices. They operate based on electrochemical reactions, which are exhaustively tested to check their behavior and to determine their characteristics at each working point. One remarkable issue of batteries is their complex behavior. The power cell type under analysis in this research is a LFP (Lithium Iron Phosphate LiFePO4). The purpose of this research is to predict the power cell State of Charge (SOC) by creating a hybrid intelligent model. All the operating points measured from a real system during a capacity confirmation test make up the dataset used to obtain the model. This dataset is clustered to obtain different behavior groups, which are used to develop the final model. Different regression techniques such as polynomial regression, support vector regression (SVR), and artificial neural networks (ANN) have been implemented for each cluster. A combination of these methods is performed to achieve an intelligent model. The SOC of the power cell can be predicted by this hybrid intelligent model, and good results are achieved.

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Section: Introductionmentioning

confidence: 99%

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

et al. 2017

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“…where→ y is the dependent variable predicted by the MARS model, c 0 is a constant, B m (x) is the mathematical base function, which can be a simple spline base function, and c m is the coefficient of the mathematical base function. The MARS technique builds its model in two stages, the "step forward" and the "step back" [17][18][19]. In reference to the "step forward", MARS begins with a model containing only the intercept term, which is the mean of the response values.…”

Section: Multivariate Adaptive Regression Splines Methods (Mars)mentioning

confidence: 99%

Comparative Modeling of a Parabolic Trough Collectors Solar Power Plant with MARS Models

et al. 2017

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Power plants producing energy through solar fields use a heat transfer fluid that lends itself to be influenced and changed by different variables. In solar power plants, a heat transfer fluid (HTF) is used to transfer the thermal energy of solar radiation through parabolic collectors to a water vapor Rankine cycle. In this way, a turbine is driven that produces electricity when coupled to an electric generator. These plants have a heat transfer system that converts the solar radiation into heat through a HTF, and transfers that thermal energy to the water vapor heat exchangers. The best possible performance in the Rankine cycle, and therefore in the thermal plant, is obtained when the HTF reaches its maximum temperature when leaving the solar field (SF). In addition, it is necessary that the HTF does not exceed its own maximum operating temperature, above which it degrades. The optimum temperature of the HTF is difficult to obtain, since the working conditions of the plant can change abruptly from moment to moment. Guaranteeing that this HTF operates at its optimal temperature to produce electricity through a Rankine cycle is a priority. The oil flowing through the solar field has the disadvantage of having a thermal limit. Therefore, this research focuses on trying to make sure that this fluid comes out of the solar field with the highest possible temperature. Modeling using data mining is revealed as an important tool for forecasting the performance of this kind of power plant. The purpose of this document is to provide a model that can be used to optimize the temperature control of the fluid without interfering with the normal operation of the plant. The results obtained with this model should be necessarily contrasted with those obtained in a real plant. Initially, we compare the PID (proportional–integral–derivative) models used in previous studies for the optimization of this type of plant with modeling using the multivariate adaptive regression splines (MARS) model.

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“…However, despite the intelligent systems use, bad performance could be possible, if the nonlinearities are several. If it is the case, then, a hybrid system based on clustering techniques, previous to the regression step, frequently gives satisfactory results [28].…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

et al. 2019

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The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73 with the validation dataset.

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Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

Cited by 33 publications

References 48 publications

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

Comparative Modeling of a Parabolic Trough Collectors Solar Power Plant with MARS Models

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

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