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.

Finite Element Modelling of a Reflection Differential Split-D Eddy Current Probe Scanning Surface Notches.

Differential eddy current probes are commonly used to detect shallow surface cracks in conductive materials. In recent years, a growing number of research works on their numerical modelling was conducted since the development of analytical or semi-analytical models for such a sensor may be prone to intractable complications. In this paper finite element modelling (FEM) has been employed to simulate the interaction of a reflection differential split-D probe with surface electrical discharge machined (EDM) notches in 3-dimensional (3-D) half-space.

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.

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.

Changing Strabismus Surgery Distribution at Shanxi Province Eye Hospital in Central China.

Purpose: To investigate whether there has been a changing trend of strabismus surgery in a provincial eye hospital in the central part of China over the past decade.

Methods: This was a retrospective analysis of data on all strabismus surgery performed in Shanxi Province Eye Hospital in China during the past 10 years. Recorded characteristics included patient demographics, type of strabismus, age, and gender.

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.

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 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.