Position-based visual servoing of a 6-RSS parallel robot using adaptive sliding mode control.

The trajectory tracking control of parallel robots is challenging due to their complicated dynamics and kinematics. This paper proposes a position-based visual servoing (PBVS) approach for a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot using adaptive sliding mode control in Cartesian space. A photogrammetry sensor C-Track 780 in the eye-to-hand configuration is adopted to measure the real-time pose of the robot end-effector, which can avoid the calculation of robot forward kinematics and provide more flexibility for controller design.

Deep neural network-based robotic visual servoing for satellite target tracking.

In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in current image moment-based visual servoing. The proposed method uses DNNs to estimate the manipulator's pose, resulting in a significant reduction of coupling effects, which enhances control performance and increases tracking precision.

Predictive value of macular ganglion cell-inner plexiform layer thickness in visual field defect of pituitary adenoma patients: a case-control study.

Objective: The present study explored the association between preoperative macular ganglion cell-inner plexiform layer thickness (GCIPL) and retinal nerve fiber layer thickness (RNFL) measured by optical coherence tomography (OCT) and the recovery of visual field (VF) defect after surgery in pituitary adenoma patients.

Methods: This case-control study included patients with pituitary adenoma in the Neurosurgery Department of Shanxi Provincial People's Hospital between October 2019 and June 2021. Cranial MRI examination, three-dimensional OCT, and VF testing (Humphrey Field Analyzer II750) were performed before and at 6months after the surgery.

Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents.

The present study explores the capability of COMSOL Multiphysics, as a finite element modelling (FEM) tool, to model the interaction between a split-D differential surface eddy current (ECT) probe and semi-elliptical surface electrical discharge machined (EDM) notches. The effect of the small probe's lift-off and tilt on its signal is investigated through modelling and subsequently, the simulation outcomes are validated using the probe's impedance measurements. In the next stage, an adaptive neuro-fuzzy inference system (ANFIS) is designed to take the signal features as inputs and consequently, provide the length of the scanned notch as the system's output.

Operation of the Collaborative Composite Manufacturing (CCM) System.

The automated tape placement and the automated fiber placement (AFP) machines provide a safer working environment and reduce the labor intensity of workers than the traditional manual fiber placement does. Thus, the production accuracy, repeatability and efficiency of composite manufacturing are significantly improved. However, the current AFP systems can only produce the composite components with large open surface or simple revolution parts, which cannot meet the growing interest in small complex or closed structures from industry.

Diurnal Rhythms Spatially and Temporally Organize Autophagy.

Circadian rhythms are a hallmark of physiology, but how such daily rhythms organize cellular catabolism is poorly understood. Here, we used proteomics to map daily oscillations in autophagic flux in mouse liver and related these rhythms to proteasome activity. We also explored how systemic inflammation affects the temporal structure of autophagy.

Effect of combined ursodeoxycholic acid and glucocorticoid on the outcome of Kasai procedure: A systematic review and meta-analysis.

Introduction: Multiple studies have investigated the effect of ursodeoxycholic acid (UDCA) or glucocorticoid (GC) on the outcome of the hepatoportoenterostomy (Kasai procedure) in patients with biliary atresia (BA). However, the combined effect of these drugs (UDCA + GC) is little understood.

Methods: This meta-analysis specifically evaluated the effect of UDCA + GC after the Kasai procedure in patients with BA.

Bacterial community composition in the gut content of Lampetra japonica revealed by 16S rRNA gene pyrosequencing.

The composition of the bacterial communities in the hindgut contents of Lampetrs japonica was surveyed by Illumina MiSeq of the 16S rRNA gene. An average of 32385 optimized reads was obtained from three samples. The rarefaction curve based on the operational taxonomic units tended to approach the asymptote.

Characterisation of the bacterial community structures in the intestine of Lampetra morii.

The metagenomic analysis and 16S rDNA sequencing method were used to investigate the bacterial community in the intestines of Lampetra morii. The bacterial community structure in L. morii intestine was relatively simple.

Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks.

Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algorithm, which is originally designed for discrete-time systems, a novel weight updating law is developed for continuous-time NNs identification process.

Nonlinear systems identification and control via dynamic multitime scales neural networks.

This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification.

Sliding mode fault tolerant control dealing with modeling uncertainties and actuator faults.

In this paper, two sliding mode control algorithms are developed for nonlinear systems with both modeling uncertainties and actuator faults. The first algorithm is developed under an assumption that the uncertainty bounds are known. Different design parameters are utilized to deal with modeling uncertainties and actuator faults, respectively.

Nonlinear optical properties in a nanoring: quantum size and magnetic field effect.

We have studied the nonlinear optical absorption and the nonlinear optical rectification of an exciton in a nanoring in the presence of magnetic flux. The calculation results show that one can control the properties of nonlinear optical absorption and nonlinear optical rectification of a nanoring by tuning the outer and inner radius. Moreover, we find that the nonlinear optical properties of a nanoring can be modulated by the magnetic flux through the nanoring.

Green's function method applied to the paramagnetic properties of R(1-x)X(x)MnO(3).

The magnetic properties of the isotropic manganites R(1-x)X(x)MnO(3) are studied in the paramagnetic regime using the Green's function method. The Curie-Weiss and critical temperatures, Θ and T(c), are obtained within the random phase approximation, as well as the high-temperature susceptibility. Our results are in agreement with other theoretical and experimental results.

A study of two confined electrons using the Woods-Saxon potential.

In this paper, we studied two electrons confined in a quantum dot with the Woods-Saxon potential by using the method of numerical diagonalization of the Hamiltonian matrix within the effective-mass approximation. The great advantage of our methodology is that it enables confinement regimes by varying two parameters in the model potential. A ground-state behavior (singlet [Formula: see text] triplet state transitions) as a function of the strength of a magnetic field has been investigated.

A novel sliding-mode control of induction motor using space vector modulation technique.

This paper presents a novel sliding-mode control method for torque control of induction motors. The control principle is based on sliding-mode control combined with space vector modulation technique. The sliding-mode control contributes to the robustness of induction motor drives, and the space vector modulation improves the torque, flux, and current steady-state performance by reducing the ripple.