Ultrasonic level sensors for flowmetering of non-Newtonian fluids in open Venturi channels: Using data fusion based on Artificial Neural Network and Support Vector Machines

Chhantyal, Khim; Viumdal, Håkon; Mylvaganam, Saba; Elseth, Geir

doi:10.1109/sas.2016.7479829

Cited by 13 publications

(15 citation statements)

References 8 publications

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“…The basic process is to map the original data input space into the higher dimensional feature through non-linear mapping functions (Scholkopf and Smola, 2005). We have implemented SVM to solve regression problem as Support Vector Regression in our work (Chhantyal et al, 2016). Table 1.…”

Section: Svr Modelingmentioning

confidence: 99%

“…Table 1. (Chhantyal et al, 2016) The linear regression model in feature space for SVR is represented as,…”

Section: Svr Modelingmentioning

confidence: 99%

“…This primal optimization problem is then transformed into a dual problem (Chhantyal et al, 2016). The solution is defined in (4)…”

Section: Svr Modelingmentioning

confidence: 99%

“…Support Vectors are those data within the training set, which are very close to the ε-insensitive tube. The number of support vectors is equal to the number of non-zero (α i − α * i ), which will determine the number of mapping vectors, determining the complexity of the model (Chhantyal et al, 2016). Therefore, the architecture of SVR depends on the number of support vectors as described in Fig.…”

Section: Svr Modelingmentioning

confidence: 99%

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Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression

Kawan¹,

Wang²,

Li³

et al. 2017

Linköping Electronic Conference Proceedings

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This paper presents a flexible system structure to analyze and model for the potential use of huge ship sensor data to generate efficient ship motion prediction model. The noisy raw data is cleaned using noise reduction, resampling and data continuity techniques. For modeling, a flexible Support Vector Regression (SVR) is proposed to solve regression problem. In the data set, sensitivity analysis is performed to find the strength of input attributes for prediction target. The highly significant attributes are considered for input feature which are mapped into higher dimensional feature using non-linear function, thus SVR model for ship motion prediction is achieved. The prediction results for trajectory and pitch show that the proposed system structure is efficient for the prediction of different ship motion attributes.

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Section: Svr Modelingmentioning

confidence: 99%

“…Table 1. (Chhantyal et al, 2016) The linear regression model in feature space for SVR is represented as,…”

Section: Svr Modelingmentioning

confidence: 99%

“…This primal optimization problem is then transformed into a dual problem (Chhantyal et al, 2016). The solution is defined in (4)…”

Section: Svr Modelingmentioning

confidence: 99%

Section: Svr Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression

Kawan¹,

Wang²,

Li³

et al. 2017

Linköping Electronic Conference Proceedings

Self Cite

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“…However, the developed numerical model is not applicable for real-time monitoring and controlling purpose due to the high computational cost. The study presented in (Chhantyal et al, 2016b) shows the successful implementation of static Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques for flow measurement in the test loop. The present study is a continuation, where, Dynamic Artificial Neural Networks (DANN) are investigated and implemented in the software used in running, monitoring and controlling the flow loop.…”

Section: Introductionmentioning

confidence: 99%

Flow Rate Estimation using Dynamic Arti?cial Neural Networks with Ultrasonic Level Measurements

Chhantyal

Hoang

Viumdal

et al. 2018

Linköping Electronic Conference Proceedings

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Accurate estimation of flow in drilling operations at inflow and outflow positions can lead to increased safety, optimized production and improved cost efficiency. In this paper, Dynamic Artificial Neural Network (DANN) is used to estimate the flow rate of non-Newtonian drilling fluids in an open channel Venturi-rig that may be used for estimating outflow. Flow in the Venturi-rig is estimated using ultrasonic level measurements. Simulation study looks into fully connected Recurrent Neural Network (RNN) with three different learning algorithms: Back Propagation Through Time (BPTT), Real-Time Recurrent Learning (RTRL) and Extended Kalman Filter (EKF). The simulation results show that BPTT and EKF algorithms converge very quickly as compared to RTRL. However, RTRL gives more accurate results, is less complex and computationally fastest among these three algorithms. Hence, in the experimental study RTRL is chosen as the learning algorithm for implementing Dynamic Artificial Neural Network (DANN). DANN with RTRL learning algorithm is compared with Support Vector Regression (SVR) and static ANN models to assess their performance in estimating flow rates. The comparisons show that the proposed DANN is the most accurate model among three models as it uses previous inputs and outputs for the estimation of current output.

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Failure Modes and Mechanisms of Sensors Used in Oil&Gas Applications

Catelani

Ciani

Venzi

2019

Lecture Notes in Electrical Engineering

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Ultrasonic level sensors for flowmetering of non-Newtonian fluids in open Venturi channels: Using data fusion based on Artificial Neural Network and Support Vector Machines

Cited by 13 publications

References 8 publications

Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression

Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression

Flow Rate Estimation using Dynamic Arti?cial Neural Networks with Ultrasonic Level Measurements

Failure Modes and Mechanisms of Sensors Used in Oil&Gas Applications

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