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A modulated model-free predictive control with minimum switching losses (MSL-MMFPC) is proposed to improve the steady-state performance and reduce the switching losses for a permanent magnet synchronous motor (PMSM) drive system. Firstly, two adjacent current vectors are determined based on the predefined first-level cost function, and then, make the current vector at the next control period equal to the reference current vector by modulating the selected current vectors properly. Additionally, in order to keep optimal control performance also in the over-modulation region, a new rotating coordinate frame is used to adjust the optimal voltage vector. Then, the second-level cost function is designed to select the optimal voltage vector sequence, so that the switching of a VSI leg does not happen during the phase-current maximum, which can reduce the switching losses of the inverter. The simulation and experimental results verify the effectiveness of the proposed control method.

In this paper, we theoretically investigated stress-induced magnetic anisotropy and presented a model based on a unidirectional tensile stress experiment. Firstly, the theoretical model was simulated based on the work of Jiles. Secondly, experiments were conducted, and data under unidirectional tensile stress were collected and analyzed. Finally, the model was developed by correlating the experimental data and theoretical model. Our model provides a good description of stress-induced magnetic anisotropy under unidirectional tensile stresses and lays down a foundation for the quantitative testing and evaluation of stress of ferromagnetic materials through the magnetic method.

In order to study the effect of stress/plastic deformation on electrical conductivity of various magnetic materials, the electrical conductivity of silicon steel, carbon steel and aluminum alloy of different resistance coefficients at the stage of elasticity deformation and plastic deformation were studied by measuring the resistance, fitting-models were established through theorization and measuring data, and corresponding parameters are given. The results show that: 1 At the stage of elasticity deformation, the electrical conductivity of the material increases rapidly first, and then slowly increases with the increase of stress. 2 At the stage of plastic deformation under stress unloading conditions, the electronic conductivity decreases approximately linearly with the increase of plastic deformation. 3 At the stage of plastic deformation under stress loading conditions, the electronic conductivity decreases approximately linearly with the increase of plastic deformation. As the result of stress, electrical conductivity in the superposition state is higher than that in the corresponding plastic deformation state.

A category of frequency reconfigurable antennas realized by rotating mechanically a metasurface mounted atop a source radiator has attracted growing attention. Their electromagnetic properties and range of frequency reconfigurability have, nevertheless, not been satisfactorily studied. To this end, an equivalent radially homogeneous model is firstly proposed in this paper. The retrieval process is accordingly formulated to address the polarization dependence of the retrieved effective electromagnetic parameters. A couple of typical metasurface-based frequency mechanically reconfigurable antennas are thus characterized by the proposed model. The predicted central operating frequencies are validated against the results of full-wave simulation and the measurement ones of manufactured proof-of-concept prototypes. Further comparisons to the isotropic and the anisotropic models confirm superior accuracy, robustness, and simplicity of the proposed characterization model. In addition, frequency reconfigurable range is shown to be determined by the anisotropy of the metasurface, related to the geometrical symmetry of its constitutive unit cell.

A serial support vector machine (SVM) ensemble classifier based on feature selection is designed to improve the efficiency and accuracy of unbalanced data classification so as to fully excavate the potential of features. The performance measurement indicators of the traditional SVM model “one-versus-others” are defined. With the goal of reaching higher classification precision and smaller feature set scale, the serial ensemble classifier is built using “one-versus-others” binary SVM classifier as a base classifier, and ant colony optimization (ACO) is used to determine the recognizable category and input feature subset of each level. Several experiments on vibration signals of real states of the engine are performed to contrast the classifying effects of the proposed approach, the traditional “one-versus-one” and “one-versus-others” parallel SVM classifiers, and the results demonstrate the superiority of the proposed approach.

Truth discovery methods aim to identify which piece of information is trustworthy from multi-sourced data. Most existing truth discovery methods, however, are designed for structured data and fail to meet the strong need to extract trustworthy information from raw text data. More specifically, existing methods ignore the semantic information of text answers, i.e., answers may contain multiple factors, the word usages may be diverse, and the answers may be partially correct. In addition, ubiquitous long-tail phenomenon exists in the tasks, i.e., most users provide only a few answers and only a few users provide plenty of answers, which causes the user reliability estimation for small users to be unreasonable. To tackle these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to automatically discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for answers. Then, we construct undirected graph with these vectors to capture the structural information of answers. Finally, the GCN is utilized to store and update the reliability of these answers, and sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is utilized to capture complex relationships among answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art truth discovery methods.

Collaborative filtering recommendation with implicit feedbacks (e.g., clicks, views, and plays) is regarded as one of the most challenging issues in both academia and industry. From implicit feedbacks, we can only get a small fraction of observed data (positive examples), and the massive unobserved data are the mixture of negative examples and unlabeled positive examples. However, most of the existing efforts either treat unobserved data equally by assigning a uniform weight or uniformly weight observed data while ignoring the hidden information (i.e., visit frequency) in implicit feedbacks. This assumption may not hold in real-life scenarios since they cannot distinguish the contributions of the whole data and it easily leads to prediction bias. Besides, those approaches still suffer from low-efficiency issue. To this end, we propose an all-weighted matrix factorization and fast optimization strategy for effective and efficient recommendation. We first design a frequency-aware weighting scheme for observed data and a useroriented weighting scheme for unobserved data nonuniformly. Then, the weighting schemes of both observed and unobserved data are combined in a unified way to form an all-weighted matrix factorization model. Afterwards, we present a surrogate objective function and develop a fast optimization strategy to enhance the efficiency. Extensive experimental results on real-world datasets demonstrate that our method outperforms the competitive baselines on several evaluation metrics.

A metal magnetic memory (MMM) testing technique is an extensively used qualitative method for locating the position of possible damages in ferromagnetic metal structures. The mechanism of the MMM technique is described by the magnetomechanical effect under the constant magnetizing field of the Earth. Discontinuous defect remarkably affects MMM signals. For the exploration of the influences of defect on MMM signals and the determination of a method to size the defect, experimentation and reconstruction calculation were performed in this paper. Stress was imported into a specimen fabricated with a circular defect to generate the stress-induced MMM signal. The defect size and the residual magnetic field of the specimen were measured after loading. The stress-induced magnetization inside the specimen was reconstructed with measured magnetic field signals using a reconstruction algorithm. The influences of the discontinuous defect on the stress-induced magnetization were quantitatively analyzed. A method for defect imaging and sizing using the MMM technique was proposed. Results corroborate that the reconstructed defect width is congruent with the actual defect width, thereby confirming the effectiveness and applicability of the proposed method.