Fast B-spline Curve Fitting by L-BFGS

We propose a novel method for fitting planar B-spline curves to unorganized data points. In traditional methods, optimization of control points and foot points are performed in two very time-consuming steps in each iteration: 1) control points are updated by setting up and solving a linear system of equations; and 2) foot points are computed by projecting each data point onto a B-spline curve. Our method uses the L-BFGS optimization method to optimize control points and foot points simultaneously and therefore it does not need to perform either matrix computation or foot point projection in every iteration. As a result, our method is much faster than existing methods

An upper bound for the crossing number of augmented cubes

A {\it good drawing} of a graph $G$ is a drawing where the edges are non-self-intersecting and each two edges have at most one point in common, which is either a common end vertex or a crossing. The {\it crossing number} of a graph $G$ is the minimum number of pairwise intersections of edges in a good drawing of $G$ in the plane. The {\it $n$-dimensional augmented cube} $AQ_n$, proposed by S.A. Choudum and V. Sunitha, is an important interconnection network with good topological properties and applications. In this paper, we obtain an upper bound on the crossing number of $AQ_n$ less than $26/324^{n}-(2n^2+7/2n-6)2^{n-2}$.Comment: 39 page

The crossing number of locally twisted cubes

The {\it crossing number} of a graph $G$ is the minimum number of pairwise intersections of edges in a drawing of $G$. Motivated by the recent work [Faria, L., Figueiredo, C.M.H. de, Sykora, O., Vrt'o, I.: An improved upper bound on the crossing number of the hypercube. J. Graph Theory {\bf 59}, 145--161 (2008)] which solves the upper bound conjecture on the crossing number of $n$-dimensional hypercube proposed by Erd\H{o}s and Guy, we give upper and lower bounds of the crossing number of locally twisted cube, which is one of variants of hypercube.Comment: 17 pages, 12 figure

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies

On the crossing numbers of Km□Cn and Km,l□Pn

AbstractRingeisen and Beineke have proved that cr(C3□Cn)=n and cr(K4□Cn)=3n. Bokal has proved that cr(K1,l□Pn)=(n-1)⌊l2⌋⌊l-12⌋. In this paper we study the crossing numbers of Km□Cn and Km,l□Pn, and show (i) cr(Km□Cn)⩾n·cr(Km+2) for n⩾3 and m⩾5; (ii) cr(Km□Cn)⩽n4⌊m+22⌋⌊m+12⌋⌊m2⌋⌊m-12⌋ for m=5,6,7 and for m⩾8 with even n⩾4, and equality holds for m=5,6,7 and for m=8,9,10 with even n⩾4 and (iii) cr(Km,l□Pn)⩽(n-1)(⌊m+22⌋⌊m+12⌋⌊l+22⌋⌊l+12⌋-ml)+2(⌊m+12⌋⌊m2⌋⌊l+12⌋⌊l2⌋-⌊m2⌋⌊l2⌋) for min(m,l)⩾2, and equality holds for min(m,l)=2

Application of the Variational Mode Decomposition for Power Quality Analysis

Harmonics and interharmonics in power systems distort the grid voltage, deteriorate the quality and stability of the power grid. Therefore, rapid and accurate harmonic separation from the grid voltage is crucial to power system. In this article, a variational mode decomposition-based method is proposed to separate harmonics and interharmonics in the grid voltage. The method decomposes the voltage signal into fundamental, harmonic, interharmonic components through the frequency spectrum. An empirical mode decomposition (EMD) and an ensemble empirical mode decomposition (EEMD) can be combined with the independent component analysis (ICA) to analyze the harmonics and intherharmonics. By comparing EMD-ICA, EEMD-ICA methods, the proposed method has several advantages: (1) a higher correlation coefficient of all the components is found; (2) it requires much less time to accomplish signal separation; (3) amplitude, frequency, and phase angle are all retained by this method. The results obtained from both synthetic and real-life signals demonstrate the good performance of the proposed method

Study on the mixing performance of static mixers in selective catalytic reduction (SCR) systems

Selective catalytic reduction (SCR) is a promising technique for reducing nitrogen oxide (NOx) emissions from diesel engines. Static mixers are widely used in SCR systems before reactors to promote the mixing of ammonia and exhaust streams. This work aims to investigate the effects of the location of static mixers and the volume ratio of two species on mixing quality using the computational fluid dynamics (CFD) method. The simulation results show that a more homogenous ammonia distribution can be achieved at the exit of the pipe if static mixers are placed close to the ammonia injection point or if more ammonia is injected. Another phenomenon found in the study is that the mixing performance of an identical static mixer may behave discrepantly under different flow conditions if using B and C as the evaluating indexes for mixing homogenization

Non-invasive load monitoring of induction motor drives using magnetic flux sensors

Existing load monitoring methods for induction machines are generally effective, but suffer from sensitivity problems at low speeds and non-linearity problems at high supply frequencies. This study proposes a new noninvasive load monitoring method based on giant magnetoresistance flux sensors to trace stray flux leaking from induction motors. Finite element analysis is applied to analyse stray flux features of test machines. Contrary to the conventional methods of measuring stator and/or rotator rotor voltage and current, the proposed method measures the dynamic magnetic field at specific locations and provides time-spectrum features (e.g. spectrograms), response time load and stator/rotor characteristics. Three induction motors with different starting loading profiles are tested at two separate test benches and their results are analysed in the time-frequency domain. Their steady features and dynamic load response time through spectrograms under variable loads are extracted to correlate with load variations based on spectrogram information. In addition, the transient stray flux spectrogram and time information are more effective for load monitoring than steady state information from numerical and experimental studies. The proposed method is proven to be a low-cost and non-invasive method for induction machine load monitoring

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly