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The inductance of a permanent magnet-biased saturated fault current limiter (PMFCL) increases automatically with the fault current. It can quickly restrain the rising speed of direct current (dc) fault current. However, when the dc circuit breaker (DCCB) opens, the PMFCL will delay the current drop speed and generate overvoltage, which will increase the voltage stress on the DCCB. In this article, a new type of fast-response FCL (NFCL) is proposed. It has the advantage of improving the fault current drop speed. Besides, it can reduce DCCB overvoltage, thus protecting the expensive insulated gate bipolar transistors in the DCCB. In addition, it greatly reduces the energy absorption in the DCCB, thus reducing its manufacturing cost. The NFCL operates automatically without a control device, which means that it responds faster. Moreover, the diode is on the low-voltage side, meaning that insulation manufacturing cost can be reduced. First, the working principle of NFCL cooperation with the DCCB is introduced. Then, the main electromagnetic parameter design requirements of the NFCL are analyzed. Their feasibility is verified by electromagnetic circuit simulation, and a prototype is built and tested. Finally, the experimental and simulation results show that NFCL can improve current drop speed effectively.

When the fault current in high voltage direct current (HVDC) systems rises to a higher level, the DC circuit breakers will have either insufficient capacity or will require larger sizes with increased costs. Fault current limiters (FCLs) can reduce the turn-off stress and the stringent requirements on breakers, thereby also lowering their cost. The use of saturated core FCLs, which have fast response times and low power loss, is a promising approach for this issue. However, the parameter design principles for this kind of FCL has so far not been discussed in the literature. The design is often based on reactors that does not consider the transient performance. In this study, an optimized design method for permanent magnets saturated core FCLs in HVDC transmission systems, is proposed. The constraint conditions, parameter optimization and modification steps are discussed, and an optimized FCL design is obtained with the lowest cost and expected current-limiting function. Both simulations and experiments are conducted, and the results demonstrate the validation of the proposed method. Finally, a cost evaluation among main types of inductive FCLs is carried out and suggestions for scaling up saturated core FCLs to HVDC applications are given.

Lasers and especially semiconductor lasers (SCLs) are playing a major role in advanced technological and scientific tasks ranging from sensing, fundamental investigations in quantum optics and communications. The demand for ever-increasing accuracy and communication rates has driven these applications to employ phase modulation and coherent detection. The main laser attribute that comes into play is its coherence which is usually quantified by either the Schawlow-Townes (S-T) linewidth, the spectral width of the laser field, or the power spectral density (PSD) function of the laser frequency fluctuation. In this paper, we present a derivation of a general and direct relationship between these two coherence measures. We refer to the result as the Central Relation. The relation applies independently of the physical origin of the noise. Experiments are described which demonstrate the validity of the Central Relation and at the same time suggest new methods of controlling frequency noise at base band by optical filtering.

The state-of-the-art objects discovery approaches can be categorized into two: deep learning methods based on a convolutional neural network with region proposals, and conventional machine learning methods based on topic models, low-rank matrix factorization, or image processing. The deep learning methods for objects discovery are based on sacrificing computational complexity to achieve precision, and the training time can be long without GPU platforms, whereas the conventional methods usually lack detection accuracy. To effectively address the problems of the training complexity and the detection speed, we present a new objects discovery approach by proposing a two-stage (training and verification) method. In the training stage, a topic model with words co-occurrence prior is proposed on the basis of Latent Dirichlet Allocation (LDA) model, in which the co-occurrence information among the features is sufficiently utilized. In the verification stage, we propose an anchor-box polishing algorithm that fine-tunes the detection results corresponding to the pre-trained topic model from some conventional algorithms with fast detection time. The experiments on various datasets demonstrate that the proposed approach can improve the detection performance in terms of efficiency and computing costs. It is also robust to objects different in colors, lightings, scales, and so on. Interestingly, the proposed method can be combined with many fast but inaccurate detection algorithms, in which it enhances the model's flexibility.

With the rapid deployment of high speed railway (HSR) worldwide, both safety operation and comfort experience can be desired to evolve into a future era of intelligent transportation system. To eliminate boredom and provide entertainment for passengers, an intranet for internal communications among passengers named as onboard social network system (SNS) is needed. In this paper, the latest progress in HSR network architectures and technology building blocks are discussed to enable the implementation of the SNS. Meanwhile, based on the device-to-device (D2D) communication technology for proximal information interaction, SNS can be efficiently facilitated. A dynamic resource allocation algorithm is proposed to maximize the total utility of the onboard SNS, which is solved with the matching theory method. Simulation results verify the convergence and efficiency of the proposed algorithm.

We consider the problem of delivering region of interest (ROI)-coded mobile video streams using limited radio resources. Under the conditions of limited bandwidth and time-varying channel status, the goal is to optimize the transmission latency, while ensuring the quality of the ROI parts. Multi-homing support enables the terminals to establish multiple connections for transmission performance improvement. In this paper, we propose a novel framework for ROI-based video transmission in heterogeneous wireless networks with multi-homed terminals. The framework contains the modules of ROI detector and frame splitter, where macroblocks are categorized based on ROI detection and encapsulated into transforming units. It also includes a channel monitor that keeps track of the status of each communication path and sends feedback signals to the streaming controller for packet-scheduling control; a deep learning method is proposed for channel status prediction. To address the delivery problem, we propose a scheduling approach based on the formulated network model and the rate-distortion model. The scheduling method makes a tradeoff between the transmission delay and the distortion. It also guarantees that packets with ROI content are delivered on paths with sufficient bandwidths and low loss rates. Through comparisons with other scheduling methods, we find that the proposed scheme outperforms the other scheduling methods in terms of improving the quality (peak signal-to-noise ratio), balancing the end-to-end delay, and maintaining the playback fluency.