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The parameters of a harmonic source change with the fluctuating background voltage and current in electric power system, which makes it difficult to establish accurate harmonic models. To solve this problem, a dynamic parameter model (DPM) for harmonic source networks is proposed in this paper. By equating the parameters of a harmonic source as a polynomial of voltage and current, the proposed model can adjust parameters accordingly. The model is not only suitable for a single harmonic source but also for complex harmonic source networks. The process of modeling need no internal information of the harmonic source. The performance of the model is, for different configurations of the harmonic source, compared to the performance of other harmonic models. The results show that the proposed model has higher accuracy and is more suitable for practical power system. [ABSTRACT FROM AUTHOR]

Abstract: Chin contour is an important facial feature to build a 3D morphable model, the core step of which is to establish feature points correspondence between each face in the training set and the reference face. In this paper, robust face detection is implemented firstly using probabilistic method. A probability of detection is obtained for each image of different position and at several scales and poses. Then, the chin contours are extracted accurately using the active shape model (ASM), which depends on the parameters obtained from the face detection. From frontal (0°) to profile (90°) faces that are equally divided into 10 parts, we train 10 flexible models. Then, different flexible models are used to extract the face chin contour according to the corresponding face pose. Experimental results show that the proposed approach can extract the chin contours of different people across different poses with good accuracy. [Copyright &y& Elsevier]

With the miniaturization and integration of electronic components, there has been widespread attention on yttrium iron garnet ferrite (YIG) with high saturation magnetization, high dielectric constant, and low ferromagnetic resonance linewidth. In this work, In–Sn–Cu co-doped YIG ferrite with the chemical formula Y 3 Fe 4.8-x Cu 0.1 Sn 0.1 In x O 12 (x = 0.05, 0.10, 0.20, 0.30) was prepared. The properties of the samples, including their microstructure, crystal structure, magnetic attributes, and dielectric properties, were adjusted through the regulation of the indium doping levels. The microscopic and crystal structures confirm the effective doping of ions in YIG, resulting in an expansion of the lattice parameters. Both the saturation magnetization ( M s) and the ferromagnetic resonance linewidth (Δ H ) are influenced by the level of doping. The maximum value of M s is observed at x = 0.10 (30.86 emu/g), whereas the minimum value of Δ H occurs at x = 0.20 (35 Oe). Additionally, increasing doping concentration also leads to an upward trend in both resistivity and dielectric constant, with the lowest dielectric loss occurring at x = 0.15. This study will provide an effective approach for developing high-performance YIG ferrite that meets the requirements of miniaturization and integration. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a novel framework that is capable of dealing with small sample size problem posed to subspace analysis methods for face representation and recognition. In the proposed framework, three aspects are presented. The first is the proposal of an iterative sampling technique. The second is adopting divide–conquer–merge strategy to incorporate the iterative sampling technique and subspace analysis method. The third is that the essence of 2D PCA is further explored. Experiments show that the proposed algorithm outperforms the traditional algorithms. [Copyright &y& Elsevier]

In this paper, linear local tangent space alignment (LLTSA), as a novel linear dimensionality reduction algorithm, is proposed. It uses the tangent space in the neighborhood of a data point to represent the local geometry, and then aligns those local tangent spaces in the low-dimensional space which is linearly mapped from the raw high-dimensional space. Since images of faces often belong to a manifold of intrinsically low dimension, we develop LLTSA algorithm for effective face manifold learning and recognition. Comprehensive comparisons and extensive experiments show that LLTSA achieves much higher recognition rates than a few competing methods. [Copyright &y& Elsevier]