Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Design and Fabrication of Concentration‐Gradient Generators with Two and Three Inlets in Microfluidic Chips

A simple and low‐cost method for designing and fabricating concentration‐gradient generators with two and three inlets is proposed which can generate different concentration gradients at varying flow velocities. The microchannel structure was designed in S‐shape and left‐right symmetry. The concentration‐gradient generator was simulated based on the finite element method. The microchannels were processed on a computer numerical control (CNC) engraving and milling machine on poly(methylmethacrylate) substrate, and then two concentration‐gradient generators were fabricated by hot bonding technology. The results of experiment and simulation were compared to prove the feasibility of the method. Flow velocity was an important factor for generating different concentration gradients. The concentration‐gradient profiles of the generators with two and three inlets present approximately linear and quadratic curves.     

Nature of heterophase inclusions in high-purity optical fiber materials as studied with 3D laser ultramicroscopy

3D laser ultramicroscopy (3D LUM) is intended specially for determining the concentration and size distribution of submicron inclusions in the bulk samples of high-purity materials for visible and IR fiber optics. In this work the 3D LUM technique is shown to be able to identify the nature of individual inclusions detected. The measurement of the light scattered by an inclusion at a varied probe beam wavelength and polarization and at a varied scattered light collection angle makes it possible to determine the inclusion refractive index. The 3D LUM possibilities are illustrated by the example of studying the inclusion nature in the As₂S₃ glass samples prepared by the direct synthesis from elements in a quartz container at elevated temperatures.     

Al2O3/Yttria‐Stabilized Zirconia Hollow‐Fiber Membrane Incorporated with Iron Oxide for Pb(II) Removal

Removal by absorptive ceramic membranes can simultaneously absorb and separate metal ions from water. Alumina/yttria‐stabilized zirconia (Al₂O₃/YSZ) hollow‐fiber membranes, fabricated using phase inversion and sintering process, were deposited with iron oxide by an in‐situ hydrothermal process. The results showed that α‐Fe₂O₃ was produced and incorporated across the membranes. A reduction in flux was recorded with the deposition of α‐Fe₂O_3. However, it improved the adsorption capacity for heavy metal adsorption. The adsorption‐separation test demonstrated that the optimized membrane is able to completely remove Pb(II) ions after two hours.     

Electrical Breakdown Properties and Space Charge Formation in High Temperature Region in Ultraviolet Ray Irradiated PVC

Polyvinyl chloride (PVC) is the most popular insulating material for electric wiring instruments. However, an exothermic reaction above 150 °C may cause deterioration of the insulating properties of PVC. Therefore, it is important to clarify the heat degradation in PVC, not only to investigate the ignition of electrical wiring products but also to use electrical products safely. It is known that ultraviolet (UV) irradiation causes chemical deterioration of PVC and an increase in its conductivity. Generally, it has been thought that the electrical breakdown properties, electrical conduction, and insulating performance are affected by space charge accumulation in an insulating material. A high temperature pulsed electroacoustic (PEA) system usable up to 250 °C has been developed, and the PEA system can measure the space charge distribution and conduction current in the high temperature range simultaneously. In this investigation, the space charge distribution and conduction current were measured up to electrical breakdown in a non‐UV irradiated sample (normal PVC) and in 353 nm and 253 nm UV‐irradiated PVC samples in the range from room temperature to 200 °C in a DC electric field. In the short wavelength UV irradiated PVC sample (253 nm, 300 h), a deterioration of breakdown strength at 90 °C to 150 °C and negative packet‐like charges were observed at 60 °C and 100 °C, a positive charge accumulated in front of both the anode and cathode above 90 °C, and a higher electric field near the cathode side because the positive charge of the cathode side was greater.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.