Low frequency transmission technologies can significantly enhance power transfer capacity and distance of alternating current transmission by reducing transmission frequency. This paper proposes a dual bipolar modular AC-AC converter (DBMAC) topology for the frequency conversion between low-frequency (15~20 Hz) and grid-frequency transmission (50 Hz). The operation principle is elucidated, showing the principal of the frequency decoupling. The mathematical models of DBMAC are developed and the control strategies are given for both input and output sides. The simulation is conducted and its results verify the effectiveness of the proposed DBMAC.

Continuous-control-set model predictive control (CCS-MPC) has been extensively adopted in dual active bridge (DAB) converters to achieve rapid and precise output voltage regulation. However, the voltage control performance of conventional CCS-MPC exhibits significant sensitivity to parameter variations in DAB system modeling. Discrepancies between model and actual parameters may degrade voltage tracking accuracy. To address this limitation, this paper proposes a continuous-control-set model-free predictive control (CCS-MPC), where an ultra-local data-model for the DAB converter is established and the real-time updates of this data-model is implemented through the Runge-Kutta method, thereby eliminating parameter dependency in output voltage regulation and enhancing system parametric robustness. Experimental results validate the effectiveness of the proposed approach through comprehensive verification.

We propose a new algorithm called the ZGPCA algorithm for subspace estimation based on the GPCA (Generalized Principal Component Analysis) algorithm. It is formulated within an FIR filter framework so that the norm vectors of the subspaces correspond to filter coefficients. It is shown that such an approach leads to a more accurate and computationally efficient method compared to the GPCA algorithm. We extend the ZGPCA algorithm to make it recursive so that subspaces with possibly different dimensions can be obtained. We also propose a new distance measure that can be used for k-means clustering of sample points within a subspace. Experimental results on synthetic data and applications on face clustering and sports video clustering show good performance of the proposed algorithm.

From a personalized media point of view, many users favor a flexible tool to quickly browse the affective content in a video. Such affective content may cause audiences' strong reactions or special emotional experiences, such as anger, sadness, fear, joy and love. This paper attempts to extract affective content for digital videos by analyzing the subtitle files of DVD/DivX videos and utilize audio event to assist affective content detection. Firstly, videos are segmented by dialogue script partition. Compared to traditional video shot, video segmented by scripts is not affected by camera changes and shooting angles and easy to include video segments with compact content. Secondly, emotion-related vocabularies in video script are detected to locate affective video content. Using script to directly access video content avoids complex video analysis. Thirdly, audio event detection is utilized to assist affective content detection. Compared with traditional video semantic analysis, affective content analysis puts much more emphasis on the audience's reactions and emotions. Initial experiments are carried on sitcom videos because its simple video structure provides useful domain knowledge. The experimental results demonstrate that subtitle file analysis and audio event detection provides effective and efficient clues to determine the emotional content of the videos.

In this paper, we investigate optimal frame-selection algorithms based on dynamic programming for delivering stored variable bit rate (VBR) video under both bandwidth and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion values without violating any constraint. It is well known that dynamic programming has high complexity. In this research, we propose to eliminate nonoptimal intermediate frame states, which can effectively reduce the complexity of dynamic programming. Moreover, we propose a reverse frame selection (RFS) algorithm, where the selection starts from the last frame and ends at the first frame. Compared with the conventional dynamic programming-based forward frame selection, the RFS is able to find all of the optimal results for different preloads in one round. We further extend the RFS scheme to solve the problem of frame selection for VBR channels. In particular, we first perform the RFS algorithm offline, and the complexity is modest and scalable with the aids of frame stuffing and nonoptimal state elimination. During online streaming, we only need to retrieve the optimal frame-selection path from the pregenerated offline results, and it can be applied to any VBR channels as long as the VBR channels can be modeled as piecewise CBR channels. Experimental results show good performance of our proposed algorithms

A key frame extraction approach, based on change detection of DC images extracted from compressed video, is proposed in this paper. We define a simple pixel change map that captures additional information in a frame with respect to its adjacent frames. Since global motion contributes to pixel changes, falsely indicating the presence of key frames, it is compensated by adaptively filtering the pixel change map using a modified version of the least mean square (LMS) algorithm. The prediction errors thus obtained are used to subsequently select the key frames. The key frames are selected so that the cumulative prediction error is partitioned into equal amounts in each segment. The entire procedure is computationally simple and flexible. Experimental results illustrate the good performance of the proposed algorithm.

In this paper, we present a new classification scheme based on support vector machines (SVM) and a new texture feature, called texture correlogram, for high-level image classification. Originally, SVM classifier is designed for solving only binary classification problem. In order to deal with multiple classes, we present a new method to dynamically build up a hierarchical structure from the training dataset. The texture correlogram is designed to capture spatial distribution information. Experimental results demonstrate that the proposed classification scheme and texture feature are effective for high-level image classification task and the proposed classification scheme is more efficient than the other schemes while achieving almost the same classification accuracy. Another advantage of the proposed scheme is that the underlying hierarchical structure of the SVM classification tree manifests the interclass relationships among different classes.

This paper presents an algorithm for automatically extracting significant motion trajectories in sports videos. Our approach consists of four stages: global motion estimation, motion blob detection, trajectory evolution and trajectory refinement. Global motion is estimated from the motion vectors in the compressed video using an iterative algorithm with robust outlier rejection. A statistical hypothesis test is carried out within the Block Rejection Map(BRM), which is the by-product of the global motion estimation, for the detection of motion blobs. Trajectory evolution is the process in which the motion blobs are either appended to an existing trajectory or are considered to be the beginning of a new trajectory based on its distance to an adaptive trajectory description. Finally, the extracted motion trajectories are refined using a Kalman filter. Experimental results on both indoor and outdoor sports videos demonstrate the effectiveness and efficiency of the proposed method.

This paper describes a method to partition a video sequence into shots and subshots. By subshots, we mean one or a combination of the three camera motions of pan, tilt and zoom. The proposed technique detects both hard cuts and gradual transitions in MPEG compressed video using a single technique. We also present a motion estimation algorithm to compute the dominant motion represented by an affine model. The motion information is used to refine the location of dissolves as well as to subdivide the shot into subshots, thus providing a characterization of camera motion. We consider the dissimilarity between the I-, P- and B-frames with respect to the type of macroblocks used for encoding. Unlike previous algorithms reported, our method requires minimal decompression of the video sequence and uses very loose thresholds. The algorithm is evaluated on several types of video sequences to demonstrate its effectiveness.