Time-Domain Forecast Based on the Real Ship Sway Data

2012 ◽  
Vol 503-504 ◽  
pp. 1397-1400 ◽  
Author(s):  
Ai Guo Shi ◽  
Bo Zhou ◽  
Bo Liu ◽  
Zuo Chao Wang ◽  
Chao Li
Keyword(s):  
Prediction Model ◽  
Time Domain ◽  
The Real ◽  
Time Prediction ◽  
Model Based ◽  
Prediction Result ◽  
Measuring Accuracy ◽  
Data Prediction ◽  
Short Time

The equipment with higher measuring accuracy is installed at a suitable place in fore and aft to get ship sway data during sailing in wind and wave. The data is classified. The ship sway data prediction model based on PSO-SVM method is provided, and the extreme short time prediction result which meets the demand can be get after multi-steps calculation.

An optimal ablation time prediction model based on minimizing the relapse risk

2021 ◽  
pp. 106438
Author(s):  
Xutao Weng ◽  
Hong Song ◽  
Tianyu Fu ◽  
Yuanjin Gao ◽  
Jingfan Fan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Relapse Risk ◽  
Ablation Time ◽  
Time Prediction ◽  
Model Based

The Error Correction Model on Kalman Filter

2014 ◽  
Vol 644-650 ◽  
pp. 3968-3971
Author(s):  
Ya Qiu Hao
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Arrival Time ◽  
Running Speed ◽  
Correction Model ◽  
The Real ◽  
Time Prediction ◽  
Speed Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

In this paper, authors extracted the data from the GPS equipment on the bus and established the real-time bus arrival time prediction model and bus running speed prediction model based on Kalman filtering technique. Analyse the error and build the error correction model. Firstly the bus running speed was predicted in the next section with the bus running speed prediction model, and then the bus arrival time was predicted with the real-time bus arrival time prediction model. Applying the newest information of bus running speed and bus arrival time, we were able to predict the real-time bus arrival time dynamically. The bus running speed prediction model and the real-time bus arrival time prediction model were assessed with the data of transit route NO.300 in Beijing. Lastly we assessed the real-time bus arrival time with the error between bus arrival time and real-time bus arrival time so that the prediction error was improved to 10 seconds which has higher prediction accuracy.

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