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The transient performance during power reversal in a bidirectional dual active bridge (DAB) dc–dc converter usually suffers from the issues of current deviation, hard switching, and other induced problems. In this article, a novel phase-shift method for fast power reversal in a DAB converter is proposed, which can achieve transient zero voltage switching (ZVS) without dc current bias, and therefore, shows advanced transient performance. The detailed algorithms and the effective phase-shift ranges based on different voltage conversion ratios are elaborated. An error analysis of the performance of dc bias elimination is provided to evaluate probable errors within allowed tolerances. Extensive and comparative experimental results are provided to support the validity of the proposed phase-shift method. It is shown that smooth, fast, and reliable power reversal can be effectively achieved with the novel method, and there is no need to add snubbers into the circuit by virtue of transient ZVS. [ABSTRACT FROM AUTHOR]

Precise real-time tidal prediction is essential for management of marine activities. Note that the tidal change is a complex time-varying nonlinear process, which is not only generated by periodic configurations of celestial bodies but also influenced by various time-varying meteorological factors. To achieve precise real-time tidal prediction, an ensemble tidal prediction mechanism is established by combining harmonic analysis and variable neural networks which are constructed by discrete wavelet transform (DWT). In the ensemble prediction mechanism, a conventional harmonic analysis method is used for representing the effects of celestial factors, while a DWT-based variable neural network is used for representing the nonlinear time-varying influences of meteorological factors and other unmodeled factors. The decomposition of tidal residual time series enables the precise prediction of time-varying dynamics by using a variable neural network whose dimensions and parameters are both adaptively tuned online. High accuracy of the proposed ensemble real-time tidal prediction mechanism is demonstrated by simulation studies on the actual tidal measurements collected from the Old Port Tampa tidal station and other four tidal stations in the USA. [ABSTRACT FROM AUTHOR]

In this paper, a new method to estimate the mutual inductance and load resistance in a series–series compensated wireless power transfer system is presented. Reasonably accurate estimations can be obtained from measurements of the input voltage and current obtained at one operating frequency only. The proposal can be used to dynamically monitor both the coupling relationship between the transmitter and receiver coils and the load conditions without any direct measurement on the receiver side. It can also be used as a simple method to measure the mutual inductance of any pair of coupled coils. A novel impedance spectrum analysis method is further presented to show that series–series compensation has special characteristics in its input impedance spectrum. Experimental results with acceptable tolerance are included to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes a method for monitoring multiple loads from the front end of a wireless power transfer system without using any wireless communication systems. A mathematical approach based on scanning the frequency around the resonant frequency has been developed for deriving the load conditions. The proposal requires only information of the input voltage and current, thereby eliminating the requirements of using wireless communication systems for feedback control. The proposal has been practically confirmed in hardware prototype with good results. [ABSTRACT FROM PUBLISHER]

The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines. [ABSTRACT FROM AUTHOR]

Hierarchical Dirichlet Process (HDP) has attracted much attention in the research community of natural language processing. Given a corpus, HDP is able to determine the number of topics automatically, possessing an important feature dubbed nonparametric that overcomes the challenging issue of manually specifying a suitable topic number in parametric topic models, such as Latent Dirichlet Allocation (LDA). Nevertheless, HDP requires a much higher computational cost than LDA for parameter estimation. By taking the advantage of multi-threading, a parallel Gibbs sampling algorithm is proposed to estimate parameters for HDP based on the equivalence between HDP and Gamma-Gamma Poisson Process (G2PP) in terms of the generative process. Unfortunately, the above parallel Gibbs sampling algorithm requires to apply the finite approximation on the number of topics manually (i.e., predefine the topic number), thus can not retain the nonparametric feature of HDP. Another drawback of the above models is the lack of capturing the semantic dependencies between words, because the topic assignment of words is independent with each other. Although some works have been done in phrase-based topic modelling, these existing methods are still limited by either enforcing the entire phrase to share a common topic or requiring much complex and time-consuming phrase mining methods. In this paper, we aim to develop a copula guided parallel Gibbs sampling algorithm for HDP which can adjust the number of topics dynamically and capture the latent semantic dependencies between words that compose a coherent segment. Extensive experiments on real-world datasets indicate that our method achieves low perplexities and high topic coherence scores with a small time cost. In addition, we validate the effectiveness of our method on the modelling of word semantic dependencies by comparing the extracted topical phrases with those learned by state-of-the-art phrase-based baselines. [ABSTRACT FROM AUTHOR]

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be effective for hashing retrieval. However, most of the triplet-based deep networks treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations of the binary codes and ignore the hash encoding when learning the feature representations. To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets via the rank lists of the binary codes. First, we present the order-aware weighting factors to indicate the importance of the triplets, which depend on the rank order of binary codes. Then, we reshape the triplet loss to the squared triplet loss such that the loss function will put more weights on the important triplets. The extensive evaluations on several benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Similarity-preserving hashing is a widely used method for nearest neighbor search in large-scale image retrieval tasks. Considerable research has been conducted on deep-network-based hashing approaches to improve the performance. However, the binary codes generated from deep networks may be not uniformly distributed over the Hamming space, which will greatly increase the retrieval time. To this end, we propose a deep-network-based multi-index hashing (MIH) for retrieval efficiency. We first introduce the MIH mechanism into the proposed deep architecture, which divides the binary codes into multiple substrings. Each substring corresponds to one hash table. Then, we add the two balanced constraints to obtain more uniformly distributed binary codes: 1) balanced substrings, where the Hamming distances of each substring are equal for any two binary codes and 2) balanced hash buckets, where the sizes of each bucket are equal. Extensive evaluations on several benchmark image retrieval data sets show that the learned balanced binary codes bring dramatic speedups and achieve comparable performance over the existing baselines. [ABSTRACT FROM AUTHOR]

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]