An Improved Real Time AR Method for Double Vessel Motion Prediction

2014 ◽  
Vol 945-949 ◽  
pp. 494-497
Author(s):  
Qiang Yang ◽  
Chao Wu ◽  
Jin Zou ◽  
Hong Sen Chen
Keyword(s):  
Real Time ◽  
Hydrodynamic Interactions ◽  
Motion Prediction ◽  
Multiple Method ◽  
Vessel Motion ◽  
Method Show

It is significant and valuable to predict the motion of ships in the underway replenishment statement. Thispaper proposes a new Double ships autoregressive-multiple method ( DARm ),which can determine the orders and parameters of model in areal-time. Meanwhile, the hydrodynamic interactions between the ships has beentaken into consideration and be reflected in the method. Then the method is applied to forecast ships’ roll attitudesin different speed. The simulative results of autoregressive- multiple method show the validity and veracity.

An Application of the Improved Autoregressive Method for Two Vessels Roll Motion Prediction

2014 ◽  
Vol 488-489 ◽  
pp. 881-885
Author(s):  
He Cheng Gao ◽  
Zhuang Lin ◽  
Dong Mei Yang ◽  
Ping Li ◽  
Zhi Qun Guo
Keyword(s):  
Ship Motion ◽  
The Other ◽  
Recursive Least Squares ◽  
Hydrodynamic Interactions ◽  
Motion Prediction ◽  
Roll Motion ◽  
Analysis Method ◽  
Autoregressive Time Series ◽  
Real Value ◽  
Method Show

It is of great significance and value to predict the roll of two ships, especially when one is replenishing for the other or in some other conditions. Autoregressive time series analysis method (AR) with Recursive least squares (RLS) theory is the mainstream currently and the effectiveness for the prediction of the motion attitudes have been fully validated. However, there are some differences between the prediction for the motion of two vessels and for that of one. The hydrodynamic interactions between two ships should be taken into consideration and be reflected in the application of the method. In order to solve this issue, this work firstly proposed a double ships autoregressive (DAR) method, which can determine the orders and the parameters of model in real-time with consideration of the interference between two ships and the DAR method was applied to forecast the roll motion between two ships. The simulative results of DAR method show the validity and veracity compared to real value and the advancement compared to the autoregressive (AR) method for single ship motion prediction.

Generic Source Parameter Determination and Ground-Motion Prediction for Earthquake Early Warning

2020 ◽  
Vol 110 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Itzhak Lior ◽  
Alon Ziv
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Seismic Moment ◽  
Source Parameter ◽  
Parameter Determination ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Empirical Relations

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.

A multiple model approach to respiratory motion prediction for real-time IGRT

2008 ◽  
Vol 53 (6) ◽  
pp. 1651-1663 ◽  
Author(s):  
Devi Putra ◽  
Olivier C L Haas ◽  
John A Mills ◽  
Keith J Burnham
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Motion Prediction ◽  
Multiple Model ◽  
Model Approach

Design and application of a real-time monitoring system for vessel motion and sea environment

2020 ◽  
pp. 1-10
Author(s):  
Duanfeng Han ◽  
Kuo Huang ◽  
Yingfei Zan ◽  
Lihao Yuan ◽  
Zhaohui Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
Vessel Motion ◽  
Design And Application

scholarly journals Synthesis of random parameter identification algorithms and estimates of current vessel motion parameters

2020 ◽  
Vol 164 ◽  
pp. 03004
Author(s):  
Nikolay Ivanovskiy ◽  
Ivan Gorychev ◽  
Aleksandr Yashin ◽  
Sergey Bidenko
Keyword(s):  
Angular Velocity ◽  
Parameter Identification ◽  
Real Time ◽  
Angular Position ◽  
Random Parameter ◽  
Moment Of Inertia ◽  
Position Vector ◽  
Random Parameters ◽  
Motion Parameters ◽  
Vessel Motion

The paper considers the task of synthesis of algorithms for identifying random parameters of a vessel, such as attached masses, moment of inertia, and estimating the current parameters of the vessel's motion from real-time measurements of onboard sensors. The task of the synthesis of algorithms for identifying random parameters of the vessel and evaluating the characteristics of the vessel’s movement is to determine (evaluate) the current parameters (attached masses, moment of inertia) and the characteristics of the vessel’s motion (position vector, speed) from the measurements of the vessel’s motion, angular position and angular velocity of the vessel rotation).

Real-time motion prediction using the chromatic offset of line scan cameras

2017 ◽  
Vol 65 (6) ◽  
Author(s):  
Florian Pfaff ◽  
Georg Maier ◽  
Mikhail Aristov ◽  
Benjamin Noack ◽  
Robin Gruna ◽  
...  
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Bulk Material ◽  
Separation Process ◽  
Motion Prediction ◽  
Line Scan ◽  
Time Motion ◽  
Sorting Process

AbstractState-of-the-art optical belt sorters commonly employ line scan cameras and use simple assumptions to predict each particle's movement, which is required for the separation process. Previously, we have equipped an experimental optical belt sorter with an area scan camera and were able to show that tracking the particles of the bulk material results in an improvement of the predictions and thus also the sorting process. In this paper, we use the slight gap between the sensor lines of an RGB line scan camera to derive information about the particles' movements in real-time. This approach allows improving the predictions in optical belt sorters without necessitating any hardware modifications.

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