Marine measurement and real-time control systems with case studies

Ertogan, Melek; Tayyar, Gökhan Tansel; Wilson, P.A.; Ertuğrul, Şeniz

doi:10.1016/j.oceaneng.2018.02.001

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…It is generally known that ocean covers more than 70% of the earth, thus it is crucially important to the survival and development of humanity. Ocean environmental monitoring is regarded as the only way of oceanic information acquirement, including meteorological and hydrological conditions, sediments, life, ocean dynamics [8]- [10]. These information can be accessed…”

Section: Introductionmentioning

confidence: 99%

Multivariate Seawater Quality Prediction Based on PCA-RVM Supported by Edge Computing Towards Smart Ocean

Sun

Wang

et al. 2020

IEEE Access

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Seawater quality prediction has a tremendous potential of enabling future smart ocean. However, this time-sensitive application puts forward a strict delay requirement, thus easily leading to overwhelmed networks. Edge computing is emerging as an effective means of solving network overload, due to its edge-based distributed processing. Therefore, we develop a hybrid multivariate prediction model for seawater quality assessment in an edge computing environment, considering the combination of principal component analysis (PCA) and relevance vector machine (RVM). The PCA method is employed for dimension reduction of ten seawater quality factors in advance. Six principal components are extracted from multiple features, used as input variables of the subsequent predictor. Finally, a RVM is developed to predict the future trends of dissolved oxygen and pH, measuring seawater quality. Experimental results on the real-world ocean sensor data show that our PCA-RVM based multivariate prediction model outperforms single RVM, SVM and its extended version in prediction accuracy and efficiency, meanwhile statistical testings confirm this finding. INDEX TERMS Edge computing, PCA-RVM, water quality, multivariate prediction, smart ocean.

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Section: Introductionmentioning

confidence: 99%

Multivariate Seawater Quality Prediction Based on PCA-RVM Supported by Edge Computing Towards Smart Ocean

Sun

Wang

et al. 2020

IEEE Access

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“…Therefore, many researchers are trying to obtain UMV model parameters by system parameter identification method. [7][8][9] A self-oscillation-based methodology is used to identify single degree of freedom nonlinear model parameters of UMV, extensive experiments have been carried out to prove that the method gives consistent results by Miskovic et al 10 And then, a method of modelling and identification of yaw motion dynamic system model of an open-frame underwater vehicle is presented by Avila et al, and the integral method used is the classical least squares algorithm applied to the integral form of the dynamic system equations. 11 A planar motion modelling for an unmanned surface vessel (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planning conditions, is proposed by Sonnenburg et al 12 To gain insight into hydrodynamic parameter experimental identification of open frame underwater vehicles, an experimental identification procedure is proposed by Avila et al to determine parameters of uncoupled and coupled models.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, many researchers are trying to obtain UMV model parameters by system parameter identification method. 7 –9…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

Dong

Yang

Zheng

et al. 2019

International Journal of Advanced Robotic Systems

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To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle's manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the secondorder nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments' results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle's motion control algorithm.

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A Review on Intelligent Modelling of Complex and Large Scale Systems for Unmanned Underwater Vehicles with Uncertain Disturbances

Liu

Tao

Hou

et al. 2020

Communications in Computer and Information Science

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Marine measurement and real-time control systems with case studies

Cited by 11 publications

References 17 publications

Multivariate Seawater Quality Prediction Based on PCA-RVM Supported by Edge Computing Towards Smart Ocean

Multivariate Seawater Quality Prediction Based on PCA-RVM Supported by Edge Computing Towards Smart Ocean

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

A Review on Intelligent Modelling of Complex and Large Scale Systems for Unmanned Underwater Vehicles with Uncertain Disturbances

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