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For the conventional control strategy of the three-phase pulse width modulation (PWM) rectifier, sinusoidal symmetrical ac currents and ripple free dc voltage cannot be achieved simultaneously under unbalanced grid voltages. For this reason, a model predictive control (MPC) for three-phase PWM rectifier with active power decoupling (APD) circuit is proposed. Specifically, sinusoidal symmetrical ac currents without negative sequence components can be obtained by means of MPC for the main circuit of PWM rectifier; meanwhile, MPC of the APD circuit contributes to the double-line-frequency ripple power decoupling for the ripple free dc voltage regulation even with a reduced dc link capacitor. Thanks to the high flexibility of MPC, the control strategy for both aforementioned circuits can be integrated in a unified model predictive controller, which avoids the separate controls for the two circuits and simplifies the control implementation. In addition, combined with direct power control in an $\beta-\alpha$ frame, the proposed MPC strategy tracks several associated instantaneous power values rather than voltage or current waveforms, so that it has the advantages of simple principle, fast response, strong robustness and easy implementation. The simulation results have verified the effectiveness of the proposed MPC strategy for PWM rectifier without bulky capacitor.

The author analyzes different micro areas of the chip carrier board with scanning electron microscope (SEM) and X-ray energy dispersive spectrometer(EDS) to find out the reasons why a type of chip is detached from the carrier board and the device does not work properly. The results show that the phenomenon of chip shedding due to the presence of contaminants between the chip and the carrier board.

Surveillance video is increasingly becoming the "biggest big data". This presents an unprecedented challenge for analyzing and mining the meaningful information (e.g., Rare actions or events) in such a huge amount of videos. Recent studies have shown that feature-trajectories-based methods are effective to encode motion information in video, consequently demonstrating superior performance in action and event detection. However, in existing methods, distance between two trajectories is often measured by linear models, which may be not robust enough when the lengths of trajectories are variable. Moreover, due to the rare distribution of target actions or events, the traditional classifier often tends to identify all samples as negative, consequently producing heavy performance bias. To address both two issues, this paper proposes a trajectory descriptor, BoDT (Bag of Dynamic Trajectories), and a multi-channel uneven SVM. By utilizing the DTW (dynamic time warping) algorithm to measure the similarity between two trajectories, BoDT is robust for variable-length trajectory representation. Meanwhie, as an extension of SVM with uneven margins, the proposed multi-channel uneven SVM can successfully identify rare events by adjusting a margin parameter to make the classification boundary properly moved away from the positive training examples. Extensive experiments on several benchmark datasets including KTH, YouTube, Olympic, MIT, QMUL and TRECVid demonstrate that our approach is feasible and effective.

Event detection in crowded surveillance videos is a challenging yet important problem. In this paper, we present our eSur (Event detection system on SURveillance video) system, which is derived from TRECVid'12 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) pair-wise events (e.g., PeopleMeet, PeopleSplitUp and Embrace); 2) action-like events (e.g., ObjectPut, CellToEar, PersonRuns and Pointing). In eSur system, we first employ people detection and tracking algorithms to locate target persons in 3D space-time domain. Then the video sequences in which target persons occur are partitioned into several spatio-temporal cubes. Visual features (i.e. cubic feature and MoSIFT) are computed over these cubes. After that, a sequence learning method, (namely SVM with dynamic time alignment kernel), is employed to infer the existence of an event for the video sequence. According to the TRECVid SED formal evaluation, eSur has yielded fairly encouraging results on TRECVid'12 dataset.

Event detection in crowded surveillance videos is a challenging yet important problem. This paper focuses on pair-wise events that involve the interaction of two persons (e.g., people embrace, meet or split) in crowded videos. To detect such an event accurately, we should build an effective representation model that can characterize the sequential properties of two persons' interaction. Towards this end, we propose a novel pair-wise event detection approach using cubic features and sequence discriminant learning. A video sequence is first partitioned into several spatio-temporal cubes, and multiple features (e.g., statistics of trajectories, bag of spatio-temporal interest points) are extracted on these cubes and then fused to form a cubic feature descriptor under multiple kernel learning (MKL) framework. After that, the SVM with dynamic time alignment kernel is used to infer the existence of an event in the video sequence. Experimental results show that the proposed approach achieves the encouraging performance on TRECVid SED dataset.

In this paper, we present our detection, tracking and event recognition methods and the results for PETS 2012. First, ROIs (Regions of Interest) based on geometric constraints are utilized in single view detection to eliminate the negative influence of clutter environment. Then, an optimized observation model is applied to address the ID switching or tracking drifting problem in single view tracking. Third, we introduce the multi-view Bayesian network (MBN) to reduce the "phantom" phenomena which frequently happen in general multi-view detection tasks. At last, a motion-based event recognition method is proposed to handle the event recognition task. Experimental results on the PETS 2012 dataset indicate that our methods are very promising.

In this paper, an integrated methodology of wavelet transform and ACO-SVM is applied to predicting share price. On the one hand, the instability of the time series could lead to decrease of prediction accuracy, so wavelet transform (WT) is employed as a preprocessor of SVM to eliminate the fluctuant component of original data. On the other hand, two parameters of SVM must be carefully predetermined in establishing an efficient LS-SVM model, in order to solve this problem, the ant colony optimization algorithm (ACO) is used to optimize the parameters of SVM. A practical prediction of Huaneng Guoji show that the integrated methodology of wavelet transform and ACO-SVM can serve as a promising alternative for share price prediction.