Domain Decomposition and Matching for Time-Domain Analysis of Motions of Ships Advancing in Head Sea

A domain decomposition and matching method in the time-domain is outlined for simulating the motions of ships advancing in waves. The flow field is decomposed into inner and outer domains by an imaginary control surface, and the Rankine source method is applied to the inner domain while the transient Green function method is used in the outer domain. Two initial boundary value problems are matched on the control surface. The corresponding numerical codes are developed, and the added masses, wave exciting forces and ship motions advancing in head sea for Series 60 ship and S175 containership, are presented and verified. A good agreement has been obtained when the numerical results are compared with the experimental data and other references. It shows that the present method is more efficient because of the panel discretization only in the inner domain during the numerical calculation, and good numerical stability is proved to avoid divergence problem regarding ships with flare.

The gas reservoir response in PP- and PS-wave frequency sections

Spectral decomposition using the method of Matching Pursuit Decomposition （MPD） for PP- and PS-wave data has higher resolution and higher consistency over the entire time-frequency plane. The MPD algorithm avoids the problems of inaccurate analytic time point and the time window size choice that may occur during a Fourier transform. The PP-wave attenuation is greater than the PS-wave attenuation while propagating through gas reservoirs. There are some stronger amplitude low-frequency shadows on the PP-wave single frequency sections beneath gas reservoirs which are not seen on corresponding PS- wave single frequency sections. Therefore, hydrocarbons are predicted from comparing the behavior on both frequency sections. The time-frequency analysis for multi-component data is decomposed by MPD for data from northeast China containing rich gas reservoirs. The gas response character is analyzed on different wave mode single frequency sections. We describe the MPD algorithm, compare it to other spectral decomposition methods, and show some examples of detecting low-frequency shadows beneath gas reservoirs.

Movement Primitives as a Robotic Tool to Interpret Trajectories Through Learning-by-doing

Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.