Video shot boundary detection (SBD) is the first and essential step for content-based video management and structural analysis. Great efforts have been paid to develop SBD algorithms for years. However, the high computational cost in the SBD becomes a block for further applications such as video indexing, browsing, retrieval, and representation. Motivated by the requirement of the real-time interactive applications, a unified fast SBD scheme is proposed in this paper. We adopted a candidate segment selection and singular value decomposition (SVD) to speed up the SBD. Initially, the positions of the shot boundaries and lengths of gradual transitions are predicted using adaptive thresholds and most non-boundary frames are discarded at the same time. Only the candidate segments that may contain the shot boundaries are preserved for further detection. Then, for all frames in each candidate segment, their color histograms in the hue-saturation-value) space are extracted, forming a frame-feature matrix. The SVD is then performed on the frame-feature matrices of all candidate segments to reduce the feature dimension. The refined feature vector of each frame in the candidate segments is obtained as a new metric for boundary detection. Finally, cut and gradual transitions are identified using our pattern matching method based on a new similarity measurement. Experiments on TRECVID 2001 test data and other video materials show that the proposed scheme can achieve a high detection speed and excellent accuracy compared with recent SBD schemes.

This paper investigates the synthesis of lossless snubbers and passive soft-switching converters, as well as their general properties, including topological properties and electrical properties. The aim is to find the possible simplest snubbers and soft-switching converters and to give suggestions on applications of the snubbers and the soft-switching converters. On the one hand, the synthesis and the topological properties of the snubbers and the converters determine the complexity of the topologies and help find the least requirement that passive soft switching needs to fulfill. The topological properties are investigated by addressing the basic issues induced by the inductor/capacitor added for passive soft switching. Based on the topological properties, the snubbers and the converters are synthesized. On the other hand, the electrical properties determine the performance of the snubbers and converters, help understand the limitations and are investigated by two case studies accompanied with experimental verification. By investigation of the general properties, the possible simplest snubber is given, the tradeoff between the complexity and performance is discussed in detail, the suggestion of snubber applications is presented, and three kinds of reported passive soft-switching converters which are believed to have the most potential of wide application are discussed.

Most current proton exchange membrane fuel cell (PEMFC) fault diagnosis models are not updated after offline training is completed, so they cannot learn new data features. For this problem, a PEMFC fault diagnosis model based on an equivalent circuit and an online sequential extreme learning machine (OS-ELM) framework is proposed. Under different operating conditions, the range of equivalent circuit element values is different, which is conducive to fault diagnosis. Element values constitute a dataset, from which OS-ELM is used to learn data features, and PEMFC fault diagnosis and isolation can be accomplished. OS-ELM has online learning capability. During the operation of PEMFC, equivalent circuit fitting is performed on multiple sets of sample data obtained using electrical impedance spectroscopy (EIS). Optimized fuzzy C-means (FCM) is used to cluster and partition sample data for online updating of OS-ELM models. The experimental results show that the proposed PEMFC fault diagnosis model not only has the speed and accuracy of fault diagnosis but also has the ability to learn fault characteristics online.

The growing demand for high-efficiency power con-verters with silicon carbide (SiC) devices at high switching frequencies has fueled an interest in non-auxiliary circuit-based soft switching techniques, particularly the variable switching frequency control for zero-voltage switching (VF-ZVS). Besides increasing the Total Harmonic Distortion (THD), applying the full ZVS range to three-phase SiC/Si Hybrid Active Neutral Point Clamped (HANPC) converters leads to a rise in conduction loss. This issue is exacerbated by the use of Si IGBT, as it generates significantly more conduction loss than SiC MOSFET in medium and small power HANPC. Consequently, a power loss optimized VF-ZVS (OVF-ZVS), based on a precise loss model, is proposed to enhance efficiency by considering the effect of the current ripple on device loss. This OVF-ZVS optimizes the switching frequency to minimize the total loss of three-phase switching devices across all load ranges. Meanwhile, the variable switching frequency range and LCL parameters are optimized to strike a balance between superior output current quality, power density, etc. Simulation and exper-imentation using a 10kW HANPC converter prototype illustrate the effectiveness of the proposed comprehensive optimization, further highlighting the reduction in THD and device loss.

The frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.