Joint Constraints Research Articles

A joint acceleration planning method for inverse kinematics of manipulator based on second-order system with variable impedance

The paper presents an angular acceleration planning method for joint space of manipulator to achieve real-time tracking tasks under joint constraint. To improve the performance of the algorithm and ensure the joint limit, the proposed method establishes a Second-order System with Variable Impedance (SSVI). The Second-order System is introduced in the joint space through the joint angular acceleration in second derivative of kinematics. The impedance parameters vary with different states of joint and the variable impedance produced virtual repulsive force to ensure that the joint motions are within the specified ranges when they are close to limits. The damping impedance contained in the system can improve convergence speed and suppress system oscillation and vibration caused by factors such as the visual noise. Simulation and experiment results verify that the method is effective and has faster convergence speed and more smooth motion compared with other methods.

Point-based Models: A Unified Approach for Geometric Constraint Analysis of Planar N-Bar Mechanisms with Rotary, Sliding, and Rolling Joints

Abstract Kinematic simulation of planar n-bar mechanisms has been an intense topic of study for several decades now. However, a large majority of efforts have focused on position analysis of such mechanisms with limited links and joint types. This paper presents a novel, unified approach to analysis of geometric constraints of planar n-bar mechanisms with revolute joint (R-joint), prismatic joint (P-joint), and rolling joint. This work is motivated by a need to create and program a system of constraint equations, which deal with different types of joints in a unified way. A key feature of this work is that the rolling joint constraints are represented by four-point models, which enables us to use the well-established undirected graph rigidity analysis algorithms. As a result, mechanisms with an arbitrary combination of revolute-, prismatic-joints, and wheel/gear/wheel-belt chains without any limitations on their actuation, can be analyzed and simulated efficiently for potential implementation in an interactive computer software and large-scale data generation.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Ground-penetrating radar subsurface attenuation analysis based on a sparsity-promoting time-frequency transform

The attenuation characteristics of a medium involve the absorption of electromagnetic wave energy during its propagation and represent a crucial parameter for describing the properties of subsurface media. Due to the complex nature of signal attenuation, demanding consideration of numerous parameters, quantitative analysis is challenging. Time-frequency (TF) analysis is a promising technique that simultaneously analyzes nonstationary signals in the time and frequency domains. Although the quantitative characterization of attenuation factors using TF tools can be challenging, TF analysis retains substantial potential for qualitatively estimating attenuation from ground-penetrating radar (GPR) data. The sparsity-promoting TF (SPTF) transform method with [Formula: see text] and [Formula: see text] norms joint constraints has a significantly higher TF resolution than the linear TF transform in the time and frequency domains. We develop a method for high-resolution qualitative estimation of GPR attenuation based on the SPTF method. Synthetic experiments indicate that SPTF surpasses other methods regarding TF resolution, noise robustness, and fidelity to dominant frequencies. Subsequent qualitative attenuation results using synthetic and field data agree with synthetic model outcomes and actual leakage ranges. This alignment substantiates the superior resolution and accuracy of our approach compared with alternative methods, affirming its efficacy and superiority in GPR attenuation depiction.

CombineHarvesterFlow: Joint Probe Analysis Made Easy with Normalizing Flows

We show how to efficiently sample the joint posterior of two non-covariant experiments with a large set of nuisance parameters. Specifically, we train an ensemble of normalizing flows to learn the posterior distribution of both experiments. Once trained, we can use the flows to reweight 𝒪(109) samples from both measurements to compute the joint posterior in seconds – saving up to 𝒪(1) ton of CO2 per Monte Carlo run. Using this new technique we find joint constraints between the Dark Energy Survey 3×2 point measurement, South Pole Telescope and Planck CMB lensing and a BOSS direct fit full shape analyses, for the first time. We find and S8=0.79−0.01+0.01. We release a public package called {} (https://github.com/pltaylor16/CombineHarvesterFlow) which performs these calculations.

Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals

The Minkowski functionals (MFs), a set of topological summary statistics, have emerged as a powerful tool for extracting non-Gaussian information. We investigate the prospect of constraining the reionization parameters using the MFs of the 21 cm brightness temperature field from the epoch of reionization (EOR). Realistic effects, including thermal noise, synthesized beam, and foreground avoidance, are applied to the mock observations from radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). We demonstrate that the MFs of the 21 cm signal measured with SKA-Low can be used to distinguish different reionization models, whereas the MF measurement with a HERA-like array cannot be made accurately enough. We further forecast the accuracies with which the MF measurements can place constraints on reionization parameters, using the standard Markov Chain Monte Carlo analysis for parameter inference based on forward modeling. We find that for SKA-Low observation, MFs provide unbiased estimations of the reionization parameters with accuracies comparable to the power spectrum (PS) analysis. Furthermore, joint constraints using both MFs and PS can improve the constraint accuracies by up to 30% compared to those with the PS alone. Nevertheless, the constraint accuracies can be degraded if the EOR window is shrunk with strong foreground avoidance. Our analysis demonstrates the promise of MFs as a set of summary statistics that extract complementary information from the 21 cm EOR field to the two-point statistics, which suggests a strong motivation for incorporating the MFs into the data analysis of future 21 cm observations.

Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme

Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme

Upper Limit of Sound Speed in Nuclear Matter: A Harmonious Interplay of Transport Calculation and Perturbative Quantum Chromodynamic Constraint

Very recently, it has been shown that there is an upper bound on the squared sound speed of nuclear matter from the transport, which reads cs2≤0.781 . In this work, we demonstrate that this upper bound is corroborated by the reconstructed equation of state (EOS; modeled with a nonparametric method) for ultradense matter. The reconstruction integrates multimessenger observation for neutron stars, in particular, the latest radius measurements for PSR J0437–4715 ( 11.36−0.63+0.95 km), PSR J0030+0451 ( 11.71−0.83+0.88 km, in the ST+PDT model), and PSR J0740+6620 ( 12.49−0.88+1.28 km) by NICER have been adopted. The result shows in all cases, the cs2≤0.781 upper limit for EOS will naturally yield the properties of matter near the center of the massive neutron star consistent with the causality-driven constraint from pQCD, where, in practice, the density in implementing the pQCD likelihood (n L) is applied at 10ns (where ns is the nuclear saturation density). We also note that there is a strong correlation for the maximum c s 2 with n L, and cs2≤0.781 is somehow violated when n L = n c,TOV. The result indicates that a higher n L, even considering the uncertainties from statistics, is more natural. Moreover, the remarkable agreement between the outcomes derived from these two distinct and independent constraints (i.e., the transport calculation and pQCD boundary) lends strong support to their validity. In addition, the latest joint constraint for R 1.4, R 2.0, R 1.4 − R 2.0, and M TOV are 11.94−0.68+0.77 km, 11.99−0.67+0.88 km, −0.1−0.27+0.42 km, and 2.24−0.10+0.13M⊙ (at 90% credible level), respectively.

Research and application of joint-constrained inversion of transient electromagnetic multivariate parameter

Due to the phenomena of stratigraphic inclination, complex structure, and lateral discontinuity of resistivity or layer thickness in most of the coal seams, the traditional one-dimensional transient electromagnetic inversion method has limitations in interpretation accuracy. In addition, two- and three-dimensional inversion and artificial intelligence inversion have problems of large computation and large sample size, respectively, which limit their application in small- and medium-sized engineering exploration. To improve the inversion effect, this study proposes a method of joint-constrained inversion of transient electromagnetic multivariate parameters. This method achieves the joint constraint inversion of the transient electromagnetic multi-parameter by making full use of the geological data and a priori information to construct the initial model and adding the constraints such as the resistivity, the thickness, and the layer interface of each layer in the inversion objective function, and at the same time, taking into account the spatial correlation of the stratigraphic structure between the neighboring measurement points, as well as the transverse and vertical constraints between the measurement points along the direction of the survey line and perpendicular to the survey line. First, a series of typical geoelectric models are established and numerically simulated, and the results are compared with those of the traditional inversion method to verify the applicability and effectiveness of the method. Then, the constrained inversion is carried out on the physical simulation and measured data, and the results are in good agreement with the actual geological conditions. The numerical simulation, physical simulation and measured data inversion results consistently prove that this method can effectively reduce the uncertainty of the inversion at the isolated measuring points, improve the spatial continuity of the formation boundary, and better reflect the actual geoelectric characteristics of the formation.

A Multiple Targets ISAR Imaging Method with Removal of Micro-Motion Connection Based on Joint Constraints

Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation method. ISAR is typically utilized to monitor both military and civilian ships due to its all-day and all-weather superiority. However, in complex scenarios, multiple targets may exist within the same radar antenna beam, resulting in severe defocusing due to different motion conditions. Therefore, this paper proposes a multiple-target ISAR imaging method with the removal of micro-motion connections based on the integration of joint constraints. The fully motion-compensated targets exhibit low rank and local similarity in the high-resolution range profile (HRRP) domain, while the micro-motion components possess sparsity. Additionally, targets display sparsity in the image domain. Inspired by this, we formulate a novel optimization by promoting the low-rank, the Laplacian, and the sparsity constraints of targets and the sparsity constraints of the micro-motion components. This optimization problem is solved by the linearized alternative direction method with adaptive penalty (LADMAP). Furthermore, the different motions of various targets degrade their inherent characteristics. Therefore, we integrate motion compensation transformation into the optimization, accordingly achieving the separation of rigid bodies and the micro-motion components of different targets. Experiments based on simulated data demonstrate the effectiveness of the proposed method.

Image-Based Visual Servoing of Manipulators With Unknown Depth: A Recurrent Neural Network Approach.

The image-based visual servoing (IBVS) of manipulators is important for intelligent manipulation using visual feedbacks. While the traditional IBVS methods for manipulators require the knowledge of the depth information in the interaction matrix, in this article, we propose a novel IBVS method for manipulators without depth estimation by leveraging the property of the associated image Jacobian. Because of a novel transformation, the IBVS problem is converted into a convex optimization problem subject to the kinematic constraint, joint constraints, and other constraints that are not explicitly related to the depth information. The problem is then solved by developing a recurrent neural network of global asymptotic convergence, and a dynamic neural control law without depth estimation emerges for the IBVS of manipulators. The theoretical guarantee and simulation results are provided to show the efficacy of the proposed method.

Constraining gravitational wave velocities using gravitational and electromagnetic wave observations of white dwarf binaries

ABSTRACT Although the general theory of relativity (GR) predicts that gravitational waves (GWs) have exactly the same propagation velocity as electromagnetic (EM) waves, many theories of gravity beyond GR expect otherwise. Accurate measurement of the difference in their propagation speed, or a tight constraint on it, could be crucial to validate or put limits on theories beyond GR. The proposed future space-borne GW detectors are poised to detect a substantial number of Galactic white dwarf binaries (GWDBs), which emit the GW as semimonochromatic signals. Concurrently, these GWDBs can also be identified as optical variable sources. Here we proposed that allocating a GWDB’s optical light curve and contemporaneous GW signal can be used to trace the difference between the velocity of GW and EM waves. Simulating GW and EM wave data from 14 verification binaries (VBs), our method constrains propagation-originated phase differences, limiting the discrepancy between the speed of light (c) and GW ($c_{GW}$). Through the utilization of LISA’s design sensitivity and the current precision in optical observation on GWDB, our study reveals that a four-year observation of the 14 recognized VBs results in a joint constraint that confines $\Delta c/c$ ($\Delta c = c_{\mathrm{GW}} - c$) to the range of $-2.1\times 10^{-12}$ and $4.8\times 10^{-12}$. Additionally, by incorporating this constraint on $c_{\mathrm{GW}}$, we are able to establish boundaries for the mass of the graviton, limiting it to $m_{\mathrm{g}}\le 3\times 10^{-23}\, e\mathrm{V}\,c^{-2}$, and for the parameter associated with local Lorentz violation, $\bar{s}_{00}$, constrained within the range of $-3.4\times 10^{-11}\le \bar{s}_{00}\le 1.5\times 10^{-11}$.

Analysis and synthesis of interconnected hybrid mechanisms using Freedom and Constraint Topologies (FACT)

We introduce visualization-based methods to analyze and synthesize interconnected hybrid (ICH) systems. These systems consist of joints that are not strictly parallel nor serial to one another. We include two types of analysis methods – one that uses joint freedom spaces and another that uses joint constraint spaces. These methods leverage intuition to determine the mobility, under-constraint, over-constraint, transmission, and load paths of any mechanism. The analysis methods are an interpretation of previously established numerical approaches for ICH systems. To adapt the complexities of screw theory and matrix algebra, this work builds off the geometries and systematic rules of the Freedom and Constraint Topologies (FACT) design approach. The synthesis approach is a reversal of the freedom-based analysis method. This synthesis approach takes a designer from any set of desired transmissions to an ICH system capable of achieving them. Both the analysis and synthesis approaches are accompanied by an example compliant mechanism to illustrate their utility and limitations. These methods seek to facilitate the design of emerging mechanism classes, such as metamaterials, and extend the FACT design approach to include ICH systems.

Deep conditional adversarial subdomain adaptation network for unsupervised mechanical fault diagnosis

In recent years, intelligent fault diagnosis technology has rapidly advanced, achieving remarkable results. Most of them assume that the source and target domains have similar distributions. However, the actual working conditions of rotating machinery are variable, leading to a significant distribution discrepancy between the source and target domains. The majority of existing domain adaptation methods mainly consider either the marginal distribution or the conditional distribution between the source and target domains, which limits the improvement of diagnostic performance in the target domain. Considering the discrepancy in domain distributions, a deep conditional adversarial subdomain adaptation network (DCASAN) is proposed in this paper for unsupervised mechanical fault diagnosis under unknown working conditions. In DCASAN, a backbone network based on the feature fusion of convolutional neural network (CNN) and graph convolutional network (GCN) is designed for feature extraction. Furthermore, a conditional adversarial subdomain adaptation strategy is proposed, which simultaneously considers both the marginal distribution and the conditional distribution. Under the joint constraints of marginal and conditional distribution alignment, the extracted features exhibit improved inter-class discriminability and intra-class consistency, effectively reducing the distribution discrepancy between different domains. Results from multiple transfer tasks on three rotating machinery datasets demonstrate that the proposed method can learn rich transferable features. The overall average accuracy of the proposed method on these three datasets is 88.07%, 89.17%, and 96.63%, respectively, significantly outperforming other methods in terms of robustness and generalization.

Seismic wavelet shape-oriented reflectivity inversion method

Abstract Reflectivity inversion plays a pivotal role in reservoir prediction. Conventional sparse-spike deconvolution assumes that the reflectivity (reflection coefficient) is sparse, which is solved based on the l1 norm. However, the restricted isometry property (RIP) of wavelet matrix and seismic effective bandwidth limits the accuracy of the sparse-spike reflectivity inversion. Consequently, we investigate the connection between seismic amplitude shape and reflectivity. When the reflectivity contains more non-zero values, the wavelet bandwidth within the effective seismic data bandwidth approaches a limit corresponding to the Sinc wavelet, where the main-lobe amplitude closely approximates the reflectivity. Conversely, when the reflectivity has fewer non-zero values, a wavelet with a smaller sidelobe provides a more accurate approximation of the reflectivity. In this paper, we propose a high-resolution inversion optimization method based on joint l2 norm and l1 norm constraints. By parameter tuning, we construct the Sinc wavelet or the wavelet with a weak-sidelobe corresponding to the seismic spectrum. Subsequently, we determine the extremum to approximate the reflectivity. To mitigate the RIP condition's constraints, we employ the l2 norm to balance the l1 norm (joint constraint) by introducing l2 norm with low-pass filtering characteristics. This approach yields more accurate reflectivity estimates. By taking the extremum, this approach yields more accurate reflectivity estimates. The synthetic test demonstrates that our method achieves better reflectivity inversion accuracy compared to sparse-spike inversion with l1–l2 norm constraint. Furthermore, field tests indicate that the proposed reflectivity inversion method not only can better match the well curve, but also exhibits excellent resolution.

Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error

In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, galaxy-galaxy lensing and galaxy clustering data, contaminated with a range of IA scenarios. Using a simple 2-parameter IA model (NLA) in a DES Y3 like analysis, we show that the galaxy-galaxy lensing + galaxy clustering combination ( 2×2pt) is significantly more robust to IA mismodelling than cosmic shear. IA scenarios that produce up to 5σ biases for shear are seen to be unbiased at the level of ∼1σ for 2×2pt. We demonstrate that this robustness can be largely attributed to the redshift separation in galaxy-galaxy lensing, which provides a cleaner separation of lensing and IA contributions. We identify secondary factors which may also contribute, including the possibility of cancellation of higher-order IA terms in 2×2pt and differences in sensitivity to physical scales. Unfortunately this does not typically correspond to equally effective self-calibration in a 3×2pt analysis of the same data, which can show significant biases driven by the cosmic shear part of the data vector. If we increase the precision of our mock analyses to a level roughly equivalent to LSST Y1, we find a similar pattern, with considerably more bias in a cosmic shear analysis than a 2×2pt one, and significant bias in a joint analysis of the two. Our findings suggest that IA model error can manifest itself as internal tension between ξ± and γt+w data vectors. We thus propose that such tension (or the lack thereof) can be employed as a test of model sufficiency or insufficiency when choosing a fiducial IA model, alongside other data-driven methods.

Supermassive black holes are growing slowly by z∼5

ABSTRACT We investigate the black hole mass function at z ∼ 5 using XQz5, our recent sample of the most luminous quasars between the redshifts 4.5 < z < 5.3. We include 72 quasars with black hole masses estimated from velocity-broadened emission-line measurements and single-epoch virial prescriptions in the footprint of a highly complete parent survey. The sample mean Eddington ratio and standard deviation is log λ ≈ −0.20 ± 0.24. The completeness-corrected mass function is modelled as a double power law, and we constrain its evolution across redshift assuming accretion-dominated mass growth. We estimate the evolution of the mass function from z = 5–4, presenting joint constraints on accretion properties through a measured dimensionless e-folding parameter, kef ≡ 〈λ〉U(1 − ϵ)/ϵ = 1.79 ± 0.06, where 〈λ〉 is the mean Eddington ratio, U is the duty cycle, and ϵ is the radiative efficiency. If these supermassive black holes were to form from seeds smaller than $10^8\, {\rm M}_{\odot }$, the growth rate must have been considerably faster at z ≫ 5 than observed from z = 5–4. A growth rate exceeding 3 × the observed rate would reduce the initial heavy seed mass to $10^{5-6}\, {\rm M}_{\odot }$, aligning with supermassive star and/or direct collapse seed masses. Stellar mass ($10^2\, {\rm M}_{\odot }$) black hole seeds would require ≳4.5 × the observed growth rate at z ≫ 5 to reproduce the measured active black hole mass function. A possible pathway to produce the most extreme quasars is radiatively inefficient accretion flow, suggesting black holes with low angular momentum or photon trapping in supercritically accreting thick discs.

Joint enhancement and classification constraints for noisy speech emotion recognition

In the natural environment, the received speech signal is often interfered by noise, which reduces the performance of speech emotion recognition (SER) system. To this end, a noisy SER method based on joint constraints, including enhancement constraint and arousal-valence classification constraint (EC-AVCC), is proposed. This method introduces speech enhancement features and constraints into emotion recognition for the first time, which helps to improve the performance of noisy SER. It does not require denoising pre-processing for noisy speech, thus saving testing time. Specifically, it uses multi-domain statistical features (MDSF) composed of emotional and enhanced features as the input of the SER model based on convolution neural network (CNN) and long short-term memory-attention (ALSTM). In addition, the model is constrained jointly by speech enhancement and arousal-valence classification to obtain the robust and discriminative deep emotion features. Furthermore, in the auxiliary speech enhancement task, a joint loss function that simultaneously constrains the error of the ideal ratio mask and the error of the corresponding MDSF is introduced to obtain more robust features. The experimental results on the CASIA Chinese emotional dataset and the EMO-DB German emotional database show that compared with the CNN baseline, the proposed method improves the accuracy of SER in white noise and babble noise by 4.7%-9.9%.

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Constraining multi-field inflation using the SPHEREx all-sky survey power spectra

We investigate how well the SPHEREx all-sky survey can constrain local primordial non-Gaussianity beyond the parameter f NL using galaxy power spectra. We forecast joint constraints on the parameters f NL, g NL and τ NL obtained assuming a simple two-field curvaton model of inflation. The parameters f NL and g NL characterise the squeezed limits of the primordial bispectrum and trispectrum respectively, and lead to a characteristic scale-dependence of the galaxy bias that increases out to arbitrarily large scales. Values of the parameter τ NL > (6/5f NL)2 cause the galaxy power spectrum to have a stochastic component which also increases out to arbitrarily large scales. Our MCMC forecasts indicate that SPHEREx can provide joint constraints on any two of the three parameters f NL, g NL and τ NL. Due to strong degeneracies among these parameters, measurements of the galaxy power spectra alone may not be sufficient to jointly constrain all three. Constraints on f NL, g NL and τ NL obtained from galaxy power spectrum observations depend on the modelling of underlying nuisance parameters. We study the robustness of our forecast constraints to modelling choices and note that even with relatively conservative modelling assumptions, SPHEREx galaxy power spectra can provide strong evidence of local non-Gaussianity, even if the particular values of f NL and g NL cannot be measured precisely.