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Geometry images parameterise a mesh with a square domain and store the information in a single chart. A one-to-one correspondence between the 2D plane and the 3D model is convenient for processing 3D models. However, the parameterised vertices are not all located at the intersection of the gridlines the existing geometry images. Thus, errors are unavoidable when a 3D mesh is reconstructed from the chart. In this paper, we propose parameterise surface onto a novel geometry image that preserves the constraint of topological neighbourhood information at integer coordinate points on a 2D grid and ensures that the shape of the reconstructed 3D mesh does not change from supplemented image data. We find a collection of edges that opens the mesh into simply connected surface with a single boundary. The point distribution with approximate blue noise spectral characteristics is computed by capacity-constrained delaunay triangulation without retriangulation. We move the vertices to the constrained mesh intersection, adjust the degenerate triangles on a regular grid, and fill the blank part by performing a local affine transformation between each triangle in the mesh and image. Unlike other geometry images, the proposed method results in no error in the reconstructed surface model when floating-point data are stored in the image. High reconstruction accuracy is achieved when the xyz positions are in a 16-bit data format in each image channel because only rounding errors exist in the topology-preserving geometry images, there are no sampling errors. This method performs one-to-one mapping between the 3D surface mesh and the points in the 2D image, while foldovers do not appear in the 2D triangular mesh, maintaining the topological structure. This also shows the potential of using a 2D image processing algorithm to process 3D models. [ABSTRACT FROM AUTHOR]

Due to the unbalanced three-phase currents and the torque ripples and speed fluctuations to the drive caused by the current sampling errors of three-phase current sensors and corresponding signal processing circuits, the estimation on current sampling errors is of great importance to eliminate these adverse effects. The observer-based methods, however, present two defects: first, they could increase the computational burden to the drives, thus limiting their practical applications; second, their high dependence on the system speed or position feedback information might degrade the estimation accuracy. To solve these problems, this article proposed a fixed-point sampling method of lower branch flows and three phase-currents, which adopts the relations between the measured currents, rather than any complex algorithm, to estimate the current sampling errors. To coordinate with the proposed strategy, a fixed-point sampling scheme was accordingly rolled out. Compared with the observer-based methods, as a fixed-point one, much applicable to actual digital controllers, it could reduce the computational burden of the drive. Meanwhile, its effectiveness was verified by experimental results on a 1.5-kW IPMSM prototype, which showed accurate estimated current sampling errors and the effectively eliminated torque ripples and speed fluctuations. [ABSTRACT FROM AUTHOR]

This paper proposes an effective sliding mode controller (SMC) for a grid-connected 7-level packed U-cell (PUC7) inverter. The aim is to design a simple controller that deals effectively with the complex control problem of the PUC7 inverter (multiobjective control problem). The selection of the control actions is achieved according to the system state error at every sampling time, regardless of the previous values, which makes the control technique model-independent. The control algorithm evaluates online two cost functions (one for each state error), which are derived on the basis of sliding mode theory, and it selects the optimal control input in order to satisfy the reaching conditions of the two cost functions. Compared with the existing solutions, the proposed SMC technique ensures lower average switching frequency by tuning the hysteresis bandwidth of the capacitor-voltage error. The fast implementation, needless of gains tuning, and simple design procedure are the main features of the proposed algorithm. Simulation and experimental results are presented to prove the effectiveness of the proposed technique in controlling the PUC7 inverter with high dynamic performance and robustness against disturbances and parameters mismatch. [ABSTRACT FROM AUTHOR]

Abstract Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily fluctuates in practical applications. To address this gap, a new training approach of the OS-ELM using Kalman filter called KFOS-ELM is proposed, and state propagation is combined into extreme learning process to obtain the OS-ELM's topological parameters. Besides, an adaptive-weighted ensemble mechanism is developed and used to dynamically tune the weight coefficients of each KFOS-ELM in the learning network. The regression performance of the proposed methodology is evaluated using benchmark datasets. The simulation results show that proposed methods are superior to the OS-ELM and EOS-ELM in terms of the regression accuracy and stability without additional computational efforts. Furthermore, an enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine. The experimental results confirm our viewpoints. [ABSTRACT FROM AUTHOR]

Fail-safe robustness is an important design philosophy for critical structural systems such as airframes. The basic idea of fail-safe design is that a structure should be designed to survive normal loading conditions when local damage occurs. In the context of topology optimization, fail-safe consideration innovates the morphogenesis for structural design in addition to the parametric safety margin. Existing fail-safe topology optimization methods usually enumerate all/predefined possible damages among the design domain and optimize the worst-damage scenario. However, virtually all damages can occur and cause the structural system to stop functioning. This work proposes the Stochastic Fail-Safe Topology Optimization (S-FSTO) for computationally viable fail-safe topology optimization which considers all possible damages according to the associated severity, including the worst-damage scenario. Critical damages for density value updates are sampled through the proposed two-level sampling strategy, which allows arbitrary size and location of damages. A design domain segmentation strategy is proposed to improve the sampling efficiency. The required number of FEA runs could be reduced by multiple orders of magnitudes. The proposed S-FSTO could be conveniently equivalent to a variety of existing schemes by customizing the proposal distributions of the two-level sampling strategy. The dependence on subjective decisions for selecting local damages could be minimized. The proposed framework has been evaluated for the design of cantilever beams and airplane bearing brackets. The generated conceptual designs are significantly more robust than the deterministic ones when local damage occurs. For the design of cantilever beams, S-FSTO produced designs with similar performance as that from the original FSTO and saved 99.4% FEA runs. • A unifying and computationally viable framework for Fail-Safe design. • A two-level sampling technique considering both location and severity of damage. • An integrated parallelization scheme of U-FSTO through design domain clustering. • Innovative fail-safe designs for beams, airplane bearing brackets and bridges. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms. [ABSTRACT FROM PUBLISHER]