We propose a novel kind of compound permanent magnet synchronous machine (CPMSM), which is applicable in low-speed and high-torque situations. We first explain the structure of the CPMSM. Based on theoretically deducing the calculation formulae of the CPMSM electromagnetic parameters, we analyze the operating characteristics of the CPMSM, and obtain the power-angle curves and working curves. The no-load magnetic field distribution and the cogging torque are analyzed by applying the finite element method of three-dimensional (3D) magnetic fields, to determine the no-load leakage coefficient and the waveform of the cogging torque. Furthermore, the optimal parameters of the permanent magnet for reducing the cogging torque are determined. An important application target of the CPMSM is in direct-drive pumping units. We have installed and tested a direct-drive pumping unit in an existing oil well. Test results show that the power consumption of the direct-drive pumping unit driven by CPMSM is 61.1% of that of the beam-pumping unit, and that the floor space and weight are only 50% of those of a beam-pumping unit. The noise output does not exceed 58 dB in a range of 1 m around the machine when the machine is 1.5 m from the ground.

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission system (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensators (TCSCs) and static var compensators (SVCs) are the utilized FACTS devices. Our objectives are active power loss reduction, newly introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, were considered in the optimum allocation. A goal attainment method based on the genetic algorithm (GA) was used to approach the global optimum. The estimated annual load profile was utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. Fars Regional Electric Network was selected as a practical system to validate the performance and effectiveness of the proposed method. The entire investment of the FACTS devices was paid off and an additional 2.4% savings was made. The cost reduction of peak point power generation implies that power plant expansion can be postponed.

This paper presents a control strategy of a hybrid fuel cell/battery distributed generation (HDG) system in distribution systems. The overall structure of the HDG system is given, dynamic models for the solid oxide fuel cell (SOFC) power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between the fuel cell power plant and the battery energy storage, a neuro-fuzzy controller has been developed. Also, for controlling the active and reactive power independently in distribution systems, the current control strategy based on two fuzzy logic controllers has been presented. A Matlab/Simulink simulation model is developed for the HDG system by combining the individual component models and their controllers. Simulation results show the overall system performance including load-following and power management of the HDG system.

Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.

In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

We extended an improved version of the discrete particle swarm optimization (DPSO) algorithm proposed by Liao et al.(2007) to solve the dynamic facility layout problem (DFLP). A computational study was performed with the existing heuristic algorithms, including the dynamic programming (DP), genetic algorithm (GA), simulated annealing (SA), hybrid ant system (HAS), hybrid simulated annealing (SA-EG), hybrid genetic algorithms (NLGA and CONGA). The proposed DPSO algorithm, SA, HAS, GA, DP, SA-EG, NLGA, and CONGA obtained the best solutions for 33, 24, 20, 10, 12, 20, 5, and 2 of the 48 problems from (Balakrishnan and Cheng, 2000), respectively. These results show that the DPSO is very effective in dealing with the DFLP. The extended DPSO also has very good computational efficiency when the problem size increases.

This letter proposes a categorization matrix to analyze the playing style of a computer game player for a shooting game genre. Our aim is to use human-centered modeling as a strategy for adaptive games based on entertainment measure to evaluate the playing experience. We utilized a self-organizing map (SOM) to cluster the player’s style with the data obtained while playing the game. We further argued that style-based adaptation contributes to higher enjoyment, and this is reflected in our experiment using a supervised multilayered perceptron (MLP) network.

We present a novel algorithm for adaptive triangular mesh coarsening. The algorithm has two stages. First, the input triangular mesh is refined by iteratively applying the adaptive subdivision operator that performs a so-called red-green split. Second, the refined mesh is simplified by a clustering algorithm based on centroidal Voronoi tessellations (CVTs). The accuracy and good quality of the output triangular mesh are achieved by combining adaptive subdivision and the CVTs technique. Test results showed the mesh coarsening scheme to be robust and effective. Examples are shown that validate the method.

We present a robust mesh sharpening approach to reconstructing sharp features from blended or chamfered features, even with noise and aliasing errors. Feature regions were first recognized via normal variation according to the user’s input, and then normal filtering was applied to faces of feature regions. Finally, the vertices of the feature region were gradually updated based on new face normals using a least-squares error criterion. Experimental results demonstrate that the method is effective and robust in sharpening meshes.

We present a novel approach for dealing with optimal approximate merging of two adjacent Bézier curves with G²-continuity. Instead of moving the control points, we minimize the distance between the original curves and the merged curve by taking advantage of matrix representation of Bézier curve’s discrete structure, where the approximation error is measured by L₂-norm. We use geometric information about the curves to generate the merged curve, and the approximation error is smaller. We can obtain control points of the merged curve regardless of the degrees of the two original curves. We also discuss the merged curve with point constraints. Numerical examples are provided to demonstrate the effectiveness of our algorithms.

We constructed a single C-Bézier curve with a shape parameter for G² joining two circular arcs. It was shown that an S-shaped transition curve, which is able to manage a broader scope about two circle radii than the Bézier curves, has no curvature extrema, while a C-shaped transition curve has a single curvature extremum. Regarding the two kinds of curves, specific algorithms were presented in detail, strict mathematical proofs were given, and the effectiveness of the method was shown by examples. This method has the following three advantages: (1) the pattern is unified; (2) the parameter able to adjust the shape of the transition curve is available; (3) the transition curve is only a single segment, and the algorithm can be formulated as a low order equation to be solved for its positive root. These advantages make the method simple and easy to implement.

Curve and surface blending is an important operation in CAD systems, in which a non-uniform rational B-spline (NURBS) has been used as the de facto standard. In local corner blending, two curves intersecting at that corner are first made disjoint, and then the third blending curve is added-in to smoothly join the two curves with G¹- or G²-continuity. In this paper we present a study to solve the joint problem based on curve extension. The following nice properties of this extension algorithm are exploited in depth: (1) The parameterization of the original shapes does not change; (2) No additional fragments are created. Various examples are presented to demonstrate that our solution is simple and efficient.

We decompose the problem of the optimal multi-degree reduction of Bézier curves with corners constraint into two simpler subproblems, namely making high order interpolations at the two endpoints without degree reduction, and doing optimal degree reduction without making high order interpolations at the two endpoints. Further, we convert the second subproblem into multi-degree reduction of Jacobi polynomials. Then, we can easily derive the optimal solution using orthonormality of Jacobi polynomials and the least square method of unequally accurate measurement. This method of ‘divide and conquer’ has several advantages including maintaining high continuity at the two endpoints of the curve, doing multi-degree reduction only once, using explicit approximation expressions, estimating error in advance, low time cost, and high precision. More importantly, it is not only deduced simply and directly, but also can be easily extended to the degree reduction of surfaces. Finally, we present two examples to demonstrate the effectiveness of our algorithm.

The Fabry-Perot resonator (FPR) antenna has found wide applications in microwave and millimeter waves and recently attracted considerable interest. In this paper, a summary of planar and cylindrical structures, analytic models and research development is presented, and a comparison between these structures and analytic models is made, showing that such analytic models as the FP cavity mode, electromagnetic band gap (EBG) defect mode, transmission line mode, and leaky-wave mode are consistent when applied to analyze this type of resonator antenna. Some interesting topics under recent research, including dual or multi-band, improvement of gain bandwidth, low profile and beam control, are surveyed.

A displacement sensor based on the fiber Fabry-Perot (F-P) cavity was proposed in this paper. Theoretical and experimental analyses were presented. Displacement resolution was demonstrated by spectrum-domain experiments to obtain the dynamic range of the F-P sensor, and a piezoelectric crystal unit (PZT) was used as the driver. The output signal was modulated by a piezoelectric ceramic ring and demodulated by a phase-locked oscillator. The experimental results show that the displacement resolution of the F-P sensor is less than 5 nm and the dynamic range is more than 100 μm. As acceleration is the second-order differential of displacement, an accelerometer model was proposed using the finite element method (FEM) and ANSYS software.

An investigation of the properties of a LiNbO₃ photoelastic waveguide via the acceleration-induced effect is presented. A novel three-component hybrid-integrated optical accelerometer based on a Mach-Zehnder interferometer with a LiNbO₃ photoelastic waveguide has been designed, which is capable of detecting seismic acceleration in high-accuracy seismic exploration. The Mach-Zehnder interferometer was successfully fabricated and a lighting test used to check its quality. The frequency response characteristic of the accelerometer was measured. The accelerometer with a resonant frequency of 3549 Hz was demonstrated to show good linear frequency responding characteristics in the range of 100~3000 Hz. The accelerometer also shows good stability and consistency. Experimental results indicate that the outputs of the on- and cross-axis are 147 and 21.3 mV, respectively.

A deep understanding of the spectral gain characteristics of optical parametric oscillators (OPOs) and optical parametric amplifiers (OPAs) is important for a highly efficient optical parametric conversion. We numerically calculated the spectral gain characteristics of a quasi-phase-matching (QPM) parametric conversion process using the periodically poled 6% (mol/mol) MgO doped LiNbO₃ (PPMgLN) as the nonlinear crystal. In the simulation we utilized the approach of a transformative matrix of the periodically poled nonlinear medium, which results from the small-signal approximation of three-wave mixed nonlinear equations. Numerical simulation results show that: (1) The full width at half maximum (FWHM) of the spectral gain of the parametric process becomes wider with the increase of parametric wavelength and reaches the maximum at degeneration; (2) The gain coefficient decreases gradually with the increase of parametric wavelength; (3) The spectral gain bandwidth decreases correspondingly with the increase of the nonlinear material length; (4) There exists an optimal parametric wavelength band, which is most suitable for the high gain parametric conversion when pumped by a laser source with a wide wavelength band, such as the high power fiber laser.

Fourier-domain rapid scanning optical delay line (RSOD) was introduced for phase modulation and depth scanning in a time-domain optical coherence tomography (TD-OCT) system. Investigation of parameter optimization of RSOD was conducted. Experiments for RSOD characterization at different parameters of the groove pitch, focal length, galvomirror size, etc. were performed. By implementing the optimized RSOD in our established TD-OCT system with a broadband light source centered at 840 nm with 50 nm bandwidth, in vivo retina imaging of a rabbit was presented, demonstrating the feasibility of high-quality TD-OCT imaging using an RSOD-based phase modulator.

A novel light scattering technique for mapping metal surface corrosion is presented and its results on copper exposed to atmosphere are reported. The front end of the instrument is made up of a sensor module comprising a thin beam light emitting diode (LED) illuminating a small spot on the metal surface, and a matched pair of photodetectors, one for capturing the reflected light and the other for sampling the scattered light. The analog photocurrent signals are digitized and processed online by a personal computer (PC) to determine the corrosion factor defined in terms of the two current values. By scanning the sample surface using the light beam and by computing the corrosion factor values simultaneously, a three dimensional graph and a two dimensional contour map are generated in the PC using Matlab tools. The values of the corrosion factor measured in different durations of exposure to atmosphere, which obey a bilogarithmic law, testify to the validity of our mathematical model.