Real Time Wave Forecasting for Real Time Ship Motion Predictions

2008 ◽  
Author(s):  
Peter Naaijen ◽  
Rene´ Huijsmans
Keyword(s):  
Real Time ◽  
Ship Motion ◽  
Wave System ◽  
Theoretical Relation ◽  
Short Term ◽  
Spectral Content ◽  
Wave Elevation ◽  
Prediction Time ◽  
Wave Maker ◽  
Wave Forecasting

This paper presents results of a validation study into a linear short term wave and ship motion prediction model for long crested waves. Model experiments have been carried out during which wave elevations were measured at various distances down stream of the wave maker simultaneously. Comparison between predicted and measured wave elevation are presented for 6 different wave conditions. The theoretical relation between spectral content of an irregular long crested wave system and optimal prediction distance for a desired prediction time is explained and validated. It appears that predictions can be extended further into the future than expected based on this theoretical relation.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Nowcasting global trade in goods and services

2020 ◽  
pp. 1-19
Author(s):  
Fernando Cantú-Bazaldúa
Keyword(s):  
Real Time ◽  
Relevant Information ◽  
Global Trade ◽  
Dynamic Factor ◽  
Timely Manner ◽  
Short Term ◽  
Dynamic Factor Models ◽  
Significant Information ◽  
Goods And Services ◽  
World Economic

World economic aggregates are compiled infrequently and released after considerable lags. There are, however, many potentially relevant series released in a timely manner and at a higher frequency that could provide significant information about the evolution of global aggregates. The challenge is then to extract the relevant information from this multitude of indicators and combine it to track the real-time evolution of the target variables. We develop a methodology based on dynamic factor models adapted for variables with heterogeneous frequencies, ragged ends and missing data. We apply this methodology to nowcast global trade in goods in goods and services. In addition to monitoring these variables in real time, this method can also be used to obtain short-term forecasts based on the most up-to-date values of the underlying indicators.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

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