scholarly journals Direct Estimation Method for the Ship Motion Parameters based on Time Series Analysis

2009 ◽  
Vol 9 (0) ◽  
pp. 127-137 ◽  
Author(s):  
Daisuke Terada ◽  
Genshiro Kitagawa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Estimation Method ◽  
Ship Motion ◽  
Direct Estimation ◽  
Series Analysis ◽  
Motion Parameters

Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 1550-1554
Author(s):  
Xin Dong Yang ◽  
Zuo Chao Wang ◽  
Ai Guo Shi ◽  
Bo Liu ◽  
Li Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Chaotic Time Series ◽  
Ship Motion ◽  
Ship Motions ◽  
Sea State ◽  
Time Prediction ◽  
Series Analysis ◽  
Short Time ◽  
Chaotic Time Series Analysis

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

Study on a Stability Judgment System Based on Time Series Analysis

2010 ◽  
Author(s):  
Daisuke Terada ◽  
Genshiro Kitagawa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Ship Motion ◽  
Safety Index ◽  
Online Identification ◽  
Series Analysis ◽  
Space Modeling ◽  
Free Running ◽  
The Stability ◽  
Ship Safety

In this study, from the viewpoint of ship safety, a stability judgment system based on Time Series Analysis is proposed in order to protect large amplitude rolling in rough seas. The examination of the system is carried out using model experimental data obtained with a free-running test involving a container ship. In this system, the data is analyzed with a self-organizing state space modeling procedure, and ship motion parameters that characterize the ship motion are identified at every moment. Moreover, a safety index is defined by using the identified parameters, and the stability of the ship is determined. It is confirmed that the proposed system is a powerful tool for determining the stability of the ship. Some future problems with respect to the online identification are also considered.

scholarly journals Reliability Estimation Based on the Degradation Amount Distribution Using Composite Time Series Analysis and Grey Theory

2013 ◽  
Vol 13 (3) ◽  
pp. 3-14
Author(s):  
Li Wang, ◽  
Zaiwen Liu ◽  
Xuebo Jin ◽  
Yan Shi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Estimation Method ◽  
Reliability Evaluation ◽  
Reliability Estimation ◽  
Grey Theory ◽  
Series Analysis ◽  
Electronic Product ◽  
Random Failure ◽  
Random Failure Threshold

Abstract This paper puts forward a reliability estimation method by the Degradation Amount Distribution (DAD) of products, using a composite time series modeling procedure and grey theory based on a random failure threshold. Product DAD data are treated as a composite time series and described using a composite time series model to predict a long-term trend of degradation. The degradation test is processed for a certain electronic product and the degradation data is collected for reliability estimation. Comparison among the reliability evaluation by DAD composite time series analysis and grey theory, based on a constant and a random failure threshold, reliability evaluation by DAD regression analysis based on a random failure threshold, reliability evaluation by degradation path time series analysis, and real reliability of the electronic product is done. The results show that the reliability evaluation of the product using the method proposed is the most creditable of all.

scholarly journals OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION

2019 ◽  
Vol 8 (2) ◽  
pp. 208-219
Author(s):  
Setyoko Prismanu Ramadhan ◽  
Hasbi Yasin ◽  
Suparti Suparti
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Arima Model ◽  
Estimation Method ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Ar Model ◽  
Series Data ◽  
Series Analysis

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO

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