Destruction Feature Extraction of Prefabricated Residential Building Components Based on BIM

In order to improve the damage feature extraction effect of prefabricated residential building components and improve the structural stability of prefabricated residential components, this paper applies BIM technology to the structural feature analysis of prefabricated residential components. Moreover, this paper adopts the simple superposition method and combines the first strength theory of material mechanics to derive the formula for calculating the cracking torque of prefabricated residential building components under compound torsion. In addition, based on the variable-angle space truss model, this paper uses a simple superposition method to derive the calculation formula for the ultimate torque of composite torsion of fabricated residential building components and applies it to the BIM fabricated residential model. Finally, this paper constructs an intelligent BIM prefabricated residential building construction damage characteristic monitoring system. Through experimental research, it can be seen that the intelligent BIM prefabricated residential building construction damage feature monitoring system proposed in this paper can monitor the damage characteristics of prefabricated residential building construction and can predict the evolution of subsequent building features.

A numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance

This paper presents a numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance. The arm angle is used to represent the self-motion manifold within a global arm configuration. The joint limits are analytically mapped to the arm angle space for joint limit avoidance. To profile the relation between the joint angle and arm angle, it is critical to characterize the singular arm angle for each joint. In the-state-of-the art methods, the existence of the singular arm angle is triggered by comparing a discriminant with zero given a threshold. We will show this leads to numerical issues since the threshold is inconsistent among different target poses, leading to incorrect range of the arm angle. These issues are overcome by associating indeterminate joint angles of tangent joints with angles of 0 or pi of cosine joints, rather than using an independent threshold for each joint. The closed-form algorithm in C++ code to perform numerically stable inverse kinematics of 7-DoF S-R-S manipulators with global arm configuration control and joint limit avoidance is also given.

Identifying resonances of the Galactic bar in Gaia DR2: II. Clues from angle space

The Milky Way disk exhibits intricate orbit substructure of still-debated dynamical origin. The angle variables (θφ, θR)—which are conjugates to the actions (Lz, JR), and describe a star’s location along its orbit—are a powerful diagnostic to identify l:m resonances via the orbit shape relation ΔθR/Δθφ = −m/l. In the past, angle signatures have been hidden by survey selection effects (SEs). Using test particle simulations of a barred galaxy, we demonstrate that Gaia should allow us to identify the Galactic bar’s Outer Lindblad Resonance (l = +1, m = 2, OLR) in angle space. We investigate strategies to overcome SEs. In the angle data of the Gaia DR2 RVS sample, we independently identify four candidates for the OLR and therefore for the pattern speed Ωbar. The strongest candidate, Ωbar ∼ 1.4Ω0, positions the OLR above the ‘Sirius’ moving group, agrees with measurements from the Galactic center, and might be supported by higher-order resonances around the ‘Hercules/Horn’. But it misses the classic orbit orientation flip, as discussed in the companion study on actions. The candidate Ωbar ∼ 1.2Ω0 was also suggested by the action-based study, has the OLR at the ‘Hat’, is consistent with slow bar models, but still affected by SEs. Weaker candidates are Ωbar = 1.6 and 1.74Ω0. In addition, we show that the stellar angles do not support the ‘Hercules/Horn’ being created by the OLR of a fast bar. We conclude that—to resolve if ‘Sirius’ or ‘Hat’ are related to the bar’s OLR—more complex dynamical explanations and more extended data with well-behaved SEs are required.

Conservation of radial actions in time-dependent spherical potentials

ABSTRACT In slowly evolving spherical potentials, Φ(r, t), radial actions are typically assumed to remain constant. Here, we construct dynamical invariants that allow us to derive the evolution of radial actions in spherical central potentials with an arbitrary time dependence. We show that to linear order, radial actions oscillate around a constant value with an amplitude $\propto \dot{\Phi }/\Phi \, P(E,L)$. Using this result, we develop a diffusion theory that describes the evolution of the radial action distributions of ensembles of tracer particles orbiting in generic time-dependent spherical potentials. Tests against restricted N-body simulations in a varying Kepler potential indicate that our linear theory is accurate in regions of phase-space in which the diffusion coefficient $\tilde {D}(J_r) \lt 0.01\, J_r^2$. For illustration, we apply our theory to two astrophysical processes. We show that the median mass accretion rate of a Milky Way (MW) dark matter (DM) halo leads to slow global time-variation of the gravitational potential, in which the evolution of radial actions is linear (i.e. either adiabatic or diffusive) for ∼84 per cent of the DM halo at redshift z = 0. This fraction grows considerably with look-back time, suggesting that diffusion may be relevant to the modelling of several Gyr old tidal streams in action-angle space. As a second application, we show that dynamical tracers in a dwarf-size self-interacting DM halo (with $\sigma /m_\chi = 1\, {\rm cm^2g^{-1}}$) have invariant radial actions during the formation of a cored density profile.

Long-term dynamics driven by resonant wave–particle interactions: from Hamiltonian resonance theory to phase space mapping

In this study we consider the Hamiltonian approach for the construction of a map for a system with nonlinear resonant interaction, including phase trapping and phase bunching effects. We derive basic equations for a single resonant trajectory analysis and then generalize them into a map in the energy/pitch-angle space. The main advances of this approach are the possibility of considering effects of many resonances and to simulate the evolution of the resonant particle ensemble on long time ranges. For illustrative purposes we consider the system with resonant relativistic electrons and field-aligned whistler-mode waves. The simulation results show that the electron phase space density within the resonant region is flattened with reduction of gradients. This evolution is much faster than the predictions of quasi-linear theory. We discuss further applications of the proposed approach and possible ways for its generalization.

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

Efficient and robust approaches for three-dimensional sound source recognition and localization using humanoid robots sensor arrays

Efficient and robust sound source recognition and localization is one of the basic techniques for humanoid robots in terms of reaction to environments. Due to the fixed sensor arrays and limited computation resources in humanoid robots, there comes challenge for sound source recognition and localization. This article proposes a sound source recognition and localization framework to realize real-time and precise sound source recognition and localization system using humanoid robots’ sensor arrays. The type of the audio is recognized according to the cross-correlation function. And steered response power-phase transform function in discrete angle space is used to search the sound source direction. The sound source recognition and localization framework presents a new multi-robots collaboration system to get the precise three-dimensional sound source position and introduces a distance weighting revision way to optimize the localization performance. Additionally, the experiment results carried out on humanoid robot NAO demonstrate that the proposed approaches can recognize and localize the sound source efficiently and robustly.

From birth associations to field stars: mapping the small-scale orbit distribution in the Galactic disc

ABSTRACT Stars born at the same time in the same place should have formed from gas of the same element composition. But most stars subsequently disperse from their birth siblings, in orbit and orbital phase, becoming ‘field stars’. Here, we explore and provide direct observational evidence for this process in the Milky Way disc, by quantifying the probability that orbit-similarity among stars implies indistinguishable metallicity. We define the orbit similarity among stars through their distance in action-angle space, Δ(J, θ), and their abundance similarity simply by Δ[Fe/H]. Analysing a sample of main-sequence stars from Gaia DR2 and LAMOST, we find an excess of pairs with the same metallicity (Δ[Fe/H] < 0.1) that extends to remarkably large separations in Δ(J, θ) that correspond to nearly 1 kpc distances. We assess the significance of this effect through a mock sample, drawn from a smooth and phase-mixed orbit distribution. Through grouping such star pairs into associations with a friend-of-friends algorithm linked by Δ(J,θ), we find 100s of mono-abundance groups with ≥3 (to ≳20) members; these groups – some clusters, some spread across the sky – are over an order-of-magnitude more abundant than expected for a smooth phase-space distribution, suggesting that we are witnessing the ‘dissolution’ of stellar birth associations into the field.