Adjustability is an important function of the magnetic release for modern molded case circuit breakers. Based on virtual prototype technology, an automatic prediction method is proposed to design reasonable reactive spring parameters for this kind of magnetic release. 3-D finite element method is adopted to calculate the static characteristics of the magnetic release. Then the dynamic characteristics of the magnetic release can be simulated taking into account the variation of the spring parameters with multi-dynamics method. The calculation results have been verified by the relevant experiments. It demonstrates that the proposed method is feasible to perform the design task.

To clarify the dependence of arc duration on atmosphere, experiments were conducted under conditions of air, N$_{2}$, Ar, He and CO$_{2}$ with the pressure of 0.1,MPa in a 14,V/28,V/42,V circuit respectively. A quantitative relationship between arc duration and gas parameters such as ionization potential, thermal conductivity was obtained from the experimental data. Besides, the inherent mechanism of influence of atmosphere on arc duration was discussed.

Feature representation is a key component of most visual tracking algorithms. It is difficult to deal with complex appearance changes with low-level hand-crafted features due to weak representation capacities of such features. In this paper, we propose a novel tracking algorithm through combining a joint dictionary pair learning with convolutional neural networks (CNN). We utilize CNN model that is trained on ImageNet-Vid to extract target features. The CNN includes three convolutional layers and two fully connected layers. A dictionary pair learning follows the second fully connected layer. The joint dictionary pair is learned upon extracted deep features by the trained CNN model. The temporal variations of target appearances are learned in the dictionary learning. We use the learned dictionaries to encode target candidates. A linear combination of atoms in the learned dictionary is used to represent target candidates. Extensive experimental evaluations on OTB2015 demonstrate the superior performances against SOTA trackers.

To one double-breaker model, experimental investigation on blow open force was carried out. It demonstrates that the ratio between the emerging blow open force and arc power FB/ui decreases with the arcing time, the contact gap has less effect on FB/ui, and the characteristics of the blow open force are similar when the peak value of the short circuit current is beyond 4 kA. Then, according to the experimental data and conclusions, considering the influence of blow open force, the interruption process of molded case circuit breakers (MCCBs) was investigated. It demonstrates the blow open force has significant influence on interruption process and the proposed method is effective to evaluate new design of MCCBs.

A YBCO/CeO2/Au MIS structure (YBCO:YBa2Cu3O7y) is fabricated on a MgO(100) substrate with the help of the all-in-situ electron-beam and heater coevaperation system. The current-voltage (I-V) characteristics of the deposited YBCO film under various gate voltages are examined. Small modulation of the I-V characteristics by gate voltages can be observed. Meanwhile, the surface morphology is also studied by means of an atomic force microscope (AFM). The relation between the field effect and the surface morphology of a thin YBCO film is discussed.

A novel time-series relationship among four consecutive real-valued single-tone sinusoid samples is proposed based on their linear prediction property. In order to achieve unbiased frequency estimates for a real sinusoid in white noise, based on the proposed four-point time-series relationship, a constrained least squares cost function is minimized based on the unit-norm principle. Closed-form expressions for the variance and the asymptotic expression for the variance of the proposed frequency estimator are derived, facilitating a theoretical performance comparison with the existing three-point counterpart, called as the reformed Pisarenko harmonic decomposer (RPHD). The region of performance advantage of the proposed four-point based constrained least squares frequency estimator over the RPHD is also discussed. Computer simulations are conducted to support our theoretical development and to compare the proposed estimator performance with the RPHD as well as the Cramer-Rao lower bound (CRLB).

In the age of big data, recommendation systems provide users with fast access to interesting information, resulting to a significant commercial value. However, the extreme sparseness of user assessment data is one of the key factors that lead to the poor performance of recommendation algorithms. To address this problem, we propose a spectral clustering recommendation scheme with low-rank matrix completion and spectral clustering. Our scheme exploits spectral clustering to achieve the division of a similar user group. Meanwhile, the low-rank matrix completion is used to effectively predict un-rated items in the sub-matrix of the spectral clustering. With the real dataset experiment, the results show that our proposed scheme can effectively improve the prediction accuracy of un-rated items.

Developing a robust appearance model is a challenging task due to appearance variations of objects such as partial occlusion, illumination variation, rotation and background clutter. Existing tracking algorithms employ linear combinations of target templates to represent target appearances, which are not accurate enough to deal with appearance variations. The underlying relationship between target candidates and the target templates is highly nonlinear because of complicated appearance variations. To address this, this paper presents a regularized kernel representation for visual tracking. Namely, the feature vectors of target appearances are mapped into higher dimensional features, in which a target candidate is approximately represented by a nonlinear combination of target templates in a dimensional space. The kernel based appearance model takes advantage of considering the non-linear relationship and capturing the nonlinear similarity between target candidates and target templates. l2-regularization on coding coefficients makes the approximate solution of target representations more stable. Comprehensive experiments demonstrate the superior performances in comparison with state-of-the-art trackers.