Advanced Autopilot Systems

Ohtsu, Kohei; Peng, Hui; Kitagawa, Genshiro

doi:10.1007/978-4-431-55303-8_4

Cited by 3 publications

(1 citation statement)

References 37 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying Householder Transformation in a procedure to estimate an LSAR model was coined by Takanami and Kitagawa [21] and was given in [10] [25], [31]. Hereafter, in this paper, the algorithm is called as Kitagawa et al Algorithm.…”

Section: Procedures Of Estimating An Lsar Parameter Using Householder mentioning

confidence: 99%

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

Nurhaida¹,

Subanar²,

Abdurakhman³

et al. 2017

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.

show abstract

Section: Procedures Of Estimating An Lsar Parameter Using Householder mentioning

confidence: 99%

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

Nurhaida¹,

Subanar²,

Abdurakhman³

et al. 2017

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

Power Contribution Analysis of a Multivariate Feedback System

Tanokura

Kitagawa

2015

Indexation and Causation of Financial Markets

View full text Add to dashboard Cite

Spatio-temporal variation in sea state parameters along virtual ship route paths

Nielsen

2021

Journal of Operational Oceanography

View full text Add to dashboard Cite

The article presents a study investigating the level of variation in sea state parameters encountered by sailing ships crossing the oceans. The sea state parameters have been obtained from a reanalysis, in this case the ERA5. The study is based on the use of different interpolation schemes to compute parameters in geographical positions off the fixed grid. It is shown that the variation in sea state parameters can be significant. Consequently, in case of sailing ships, covering relatively long distances in a short time (30-60 minutes), it is recommended to rely on bilinear interpolation rather than nearest neighbour. The variation in the sea state parameters is, in fact, at a level which means that the normal assumption of a stationary seaway in periods up to 3 hours likely is violated for ships sailing the typical service speed (15 -20+ knots).

show abstract

Advanced Autopilot Systems

Cited by 3 publications

References 37 publications

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

Power Contribution Analysis of a Multivariate Feedback System

Spatio-temporal variation in sea state parameters along virtual ship route paths

Contact Info

Product

Resources

About