Modeling of Autonomous Hydrofoil Craft Tracking a Moving Target

The interest in autonomous marine vessels has been continuously growing in the recent years. Most platforms of the autonomous surface watercraft involve traditional mono- or multi-hulls. Advanced marine vehicle concepts, such as hydrofoils, can provide high-speed and high seakeeping capabilities. In this study, a modeling effort is initiated for a small autonomous hydrofoil boat intended for intercepting operations. A 3-DOF model, including surge, sway and yaw, is applied for simulating maneuvering motions of the boat in the foilborne state. Forces generated by the propulsor, rudder and struts are accounted for in the simulations of the horizontal-plane boat dynamics. Two scenarios of a hydrofoil boat pursuing a moving target are investigated. In the pure pursuit, the interceptor always attempts to aim at the target and uses full thrust to quickly reach the target at a high speed. In the constant-bearing scenario, the interceptor approaches the target with diminishing speed trying to achieve a rendezvous. The presented models and results can help engineers to design more effective control methods for fast boats intended for intercepting operations.

Typical event sequences as licensors of direct object ellipsis in Russian

This paper extends the computationally-oriented theory of ellipsis presented in McShane’s A Theory of Ellipsis (2005) by introducing the feature typical event sequence. It is argued that, in Russian, the presence of a typical sequence of events in a pair of clauses can be the key feature licensing the ellipsis of the latter’s direct object. The linguistic analysis contributes to a larger cognitive modeling effort aimed at configuring language-endowed intelligent agents with human-level language understanding capabilities.

A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition

Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either conceptual understanding of relationships (Köhler, 1938) as nonconceptual effects of neural memory gradients (as in Spence's stimulus discrimination theory, 1937 ). Most behavioral evidence can be explained by the gradient account, but a key finding unexplained by gradients is intermediate transposition, where a central (of three) stimulus, “relationally correct response,” is generalized from training to test. Here, we introduce a dynamic neural field (DNF) model that captures intermediate transposition effects while using neural mechanisms closely resembling those of Spence's original proposal. The DNF model operates on dynamic rather than linear neural relationships, but it still functions by way of gradient interactions, and it does not invoke relational conceptual understanding in order to explain transposition behaviors. In addition to intermediate transposition, the DNF model also replicates the predictions of stimulus discrimination theory with respect to basic two-stimulus transposition. Effects of wider test item spacing were additionally captured. Overall, the DNF model captures a wider range of effects in transposition than stimulus discrimination theory, uses more fully specified neural mechanics, and integrates transposition into a wider modeling effort across cognitive tasks and phenomena. At the same time, the model features a similar low-level focus and emphasis on gradient interactions as Spence's, serving as a conceptual continuation and updating of Spence's work in the field of transposition.

Probing sunspots with two-skip time–distance helioseismology

Context. Previous helioseismology of sunspots has been sensitive to both the structural and magnetic aspects of sunspot structure. Aims. We aim to develop a technique that is insensitive to the magnetic component so the two aspects can be more readily separated. Methods. We study waves reflected almost vertically from the underside of a sunspot. Time–distance helioseismology was used to measure travel times for the waves. Ray theory and a detailed sunspot model were used to calculate travel times for comparison. Results. It is shown that these large distance waves are insensitive to the magnetic field in the sunspot. The largest travel time differences for any solar phenomena are observed. Conclusions. With sufficient modeling effort, these should lead to better understanding of sunspot structure.

Determining a reasonable range of relative numerical tolerance values for simulating deterministic models of biochemical reactions

ABSTRACTWhat values of relative numerical tolerance should be chosen in simulation of a deterministic model of a biochemical reaction is unclear, which impairs the modeling effort since the simulation outcomes of a model may depend on the relative numerical tolerance values. In an attempt to provide a guideline to selecting appropriate numerical tolerance values in simulation of in vivo biochemical reactions, reasonable numerical tolerance values were estimated based on the uncertainty principle and assumptions of related cellular parameters. The calculations indicate that relative numerical tolerance values can be reasonably set at or around 10−4 for the concentrations expressed in ng/L. This work also suggests that further reducing relative numerical values may result in erroneous simulation results.