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Multiport power conversion topologies provide the capability of multiple independent converters with a single transformer having multiple windings (i.e., ports) potentially increasing power densities and enabling flexible (and bidirectional) power routing. In automotive onboard charger (OBC), the multiport approach combined with symmetrical series resonant circuits, the so-called multiport series resonant converter (MSRC), allows for a galvanic isolated connection between all ports: the grid-side converter (i.e., usually an AC/DC power factor correction (PFC) stage), vehicle’s main and the auxiliary low-voltage (LV) battery. The variation of the battery voltage significantly affects the MSRC operation, particularly for light loads at a low state-of-charge, and high losses can be experienced since zero-voltage-switching (ZVS) conditions are lost. In addition to the conventional control approach of the MSRC, where the power flow is set with a phase-shift between the individual full bridges or by changing the switching frequency, this paper proposes a novel and coordinated approach, including the manipulation of both and the additional modulation of the duty cycle as a function of the DC-link voltages, aiming to introduce a zero-voltage interval on the full bridge output voltages. A full mathematical description of the adopted converter topology is provided, including accurate simulation models that allow a comparison between the proposed duty cycle mode and the conventional control strategy. A detailed description of achieving ZVS within the connected full bridges is also included. Experimental results validate the proposal and demonstrate significant efficiency improvements compared to standard control approaches.

Solid-state power controllers (SSPCs) have been received increasing attention as they can configure the electrical system and protect the system by fast tripping mechanism at the same time. Although the high-voltage direct current (HVDC) electrical system can bring sustainable savings on the cables’ weight and losses, the protection can be considerably challenging. SSPCs have been successfully utilized for 28V DC aircraft onboard network. However, high-voltage ones are impeded by the vast over-voltage and the excessive losses generated from SSPC switching. This paper tries to fill this gap and presents the development of high-power SSPCs for a ±270VDC network of a future turboprop aircraft. Comprehensive designs of proper over-voltage suppression along with SSPC thermal management are presented in this paper. Besides, a comparative study on the SSPC device is carried out. Two prototypes, including a single MOSFET module and paralleling several discrete MOSFET devices, are built and then tested in the experiment. It has been validated that the proposed voltage clamping design cannot only effectively suppress the over-voltage, but also limit the SSPC temperature increase during switching for both candidates. After comparing the conduction losses, maximum junction temperature, power density, weight and volume, the single device solution is recommended as a preferable SSPC option for future aircraft.

This paper introduces an advanced space vector modulation technique for three-level neutral-point-clamped (3L-NPC) converters. The studied 3L-NPC converters within aircraft electric starter generator (ESG) systems normally operate at high modulation index, low pulse ratio and full power factor range for most of the operation time. Conventional modulation techniques exhibit large neutral point voltage ripple and possible voltage imbalance under such conditions. The increase of neutral point voltage ripple implies significant increase in converter capacitor size and decrease in capacitor reliability. The modulation technique introduced in this paper achieves improved neutral point voltage balancing within each switching period through enhanced applications of small vectors and restriction on medium vectors. The advanced implementation of small vectors is achieved using a sharing factor aided by a robust incremental current prediction procedure. With the predicted machine currents, the proposed method enables the optimum application of small vectors to balance the neutral point voltages. Simulation results based on a PLECS/Simulink model and experimental results obtained from a 45kVA, 32krpm aircraft electric starter generator prototype platform validate the reduction of the neutral point voltage ripple.

A novel laser self-mixing interferometry vibration measurement algorithm based on the local maximum detection technique is proposed, that can reconstruct micro vibrations rapidly and easily. In this article, the principles of the local maximum detection algorithm are analyzed in detail, and the process of solving the window function is emphasized. The major advantage of the method is that it does not involve any complicated calculations and removes the need for optical/electromechanical components. The validity of the presented method is confirmed by means of simulated signals and demonstrated via several experiments for harmonic and aleatory motion.

In recent years, multi-band perfect absorbers have great advantages in spectroscopy, infrared detection and other fields. Here we propose and use the finite-difference time-domain (FDTD) method to numerically calculate the multi-band absorber based on 1D gold grating Fabry-Perot (FP) structure. The full-wave simulation results show that under normal incident conditions, four different absorption peaks can be obtained in the near-infrared band and the absorption is all close to 1. These resonance peaks are derived from the third-, fourth-, fifth-, and sixth-order resonances of mode coupling in the FP cavity, respectively. The narrowest absorption bandwidth is 24 nm and the quality factor is 37.6. By adjusting the structural parameters of the grating, its spectrum can be adjusted and selected, and its working angle tolerance can reach 50°. In addition, numerical results show that the absorber can detect slight changes in the environment when used as a sensor. Therefore, our absorber has potential applications in the field of sensor detection.

DC traction drive systems require high-frequency switching in the power converter whose device-level switching transients have a significant impact on the accuracy of hardware-in-the-loop emulation. Real-time device-level emulation has high computation demand for calculating the switch on and off transients. This paper introduces a new method to estimate the switching transients by utilizing artificial intelligence in the hardware design. In the hybrid neural network, the k-nearest neighbors (kNN) concept and the recurrent neural network (RNN) have been employed to emulate the transient waveforms in the DC traction drive. The kNN module classifies the switching states while the RNN module predicts the transient current for a specific condition. This work also proves that the classification of the input switching states with the help of kNN can play an essential role. The hardware implementation of the study case can be executed at a time-step of 100 ns with device-level transients. The results have been validated by PSCAD/EMTDC® at system-level and SaberRD® at device-level.

Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years. Recent advancements in open-domain textual QA are mainly due to the significant developments of deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval, which allows the models to continuously refresh state-of-the-art performances. However, a comprehensive review of existing approaches and recent trends is lacked in this field. To address this issue, we present a thorough survey to explicitly give the task scope of open-domain textual QA, overview recent key advancements on deep learning based open-domain textual QA, illustrate the models and acceleration methods in detail, and introduce open-domain textual QA datasets and evaluation metrics. Finally, we summary the models, discuss the limitations of existing works and potential future research directions.

Rolling bearings are critical in industrial mining machinery. Due to strong Gaussian noise, frequent random shocks, and disordered loads in industrial settings, it is usually difficult to detect weak fault symptoms in vibration signals from a bearing. To detect incipient bearing faults, this paper proposes a new multi-domain kernel extreme learning machine (MKELM) based on variational modal decomposition (VMD) and a cyclic correntropy function. A normalized approximation algorithm for a cyclic correntropy function (NACCF) was first built to suppress the impulsive background noise. This approach is suitable for machine learning. To eliminate the Gaussian noise effectively, genetic mutation particle swarm optimization (GMPSO) with cyclic information entropy (CIE) was used to optimize the VMD parameters. The CIE was created as a fitness function in GMPSO to search for the best hyperparameters. It can be used to select effective intrinsic mode functions (IMFs) to reconstruct denoised signals. Then, statistical functions based on NACCF were used to extract the cyclic frequency-domain characteristics of the denoised signal, and the singular values of the IMFs were obtained as time-domain features of the signal. Finally, the multi-dimensional features from the two domains were input into MKELM to classify the health of the bearing. Experimental studies were carried out to investigate the proposed method in bearing fault detection and identification. The results demonstrated the effectiveness of the proposed method in motor-bearing failure detection and its robustness to noise when analyzing bearing vibration signals under different working loads.

In recent years, with the continuous growth of multimedia data on the Internet, multimodal hashing has attracted increasing attention for its efficiency in large-scale cross-modal retrieval. Typically, most existing multimodal hashing methods are batch-based methods that cannot deal with the growing streaming data. Online multimodal hashing adopts online learning strategy to learn hash models incrementally, which can process large-scale streaming data. However, existing supervised online multimodal hashing methods still suffer from several limitations: (1) most methods cannot update hash codes of old data when hash model changes, which will hinder the retrieval accuracy. (2) the discrete optimizations of most methods for learning binary hash codes are less efficient or less effective. To address the above limitations, an efficient supervised online multimodal hashing method termed Online Adaptive Supervised Hashing (OASH) is proposed in this paper. OASH regresses class labels and training data to binary hash codes to learn discrete hash codes and hash functions with an efficient online optimization scheme. It learns hash functions incrementally with newly arriving data and updates hash codes with the latest hash model. By adaptively updating hash functions and hash codes with new data rather than accessing to old data, it gains much performance enhancement and computation savings. Extensive experiments on three benchmark data sets validate the superiority of OASH over state-of-the-art methods in both accuracy and efficiency.

A resonant-type piezoelectric screw motor for one degree of freedom (1-DOF) positioning platform is proposed, fabricated, and investigated in this article. The motor comprises a driver and a screw rod. The driver is composed of four transducers, a nut seat, and a flange nut. The flange nut is bolted connected on the nut seat, and the four piezoelectric transducers are connected with the nut seat by right-angle flexible hinges. Each piezoelectric transducer comprises two piezoelectric stacks and one displacement amplifier. Driven by the four piezoelectric transducers, elliptical vibration is generated by the driver, which is used to actuate the screw rod to realize the linear movement. The driver is analyzed using the finite element analysis software ANSYS. The proposed motor and positioning platform prototype are manufactured, assembled, and tested. The performance of the developed piezoelectric screw motor shows its applicability and effectiveness for the 1-DOF positioning platform with a large force. Experiments demonstrate that the maximum velocity of this motor is 10.53 mm/s without mechanical load and the maximum output force of the platform can reach 17.19 N when the excitation voltage is 230 at 365 Hz.

The passive localization using multiple sensors is considered for emitter transmitting pulse signals with unknown start transmission time and period. Time of arrival (TOA) of a pulse is estimated at every sensor, and it is difficult for sensors to intercept all pulses during the observation time in practice, as there maybe obstacle on the line of sight or the signal is too weak to be detected, namely, incomplete measurements. In this paper, an algorithm based on a combination of a new second difference of coherent time delays (SDCTD) measurement and traditional time difference of arrival (TDOA) is proposed to improve localization performance in the presence of two kinds of TOA errors, including the independent estimation errors and common errors among multiple sensors. The Cramer-Rao lower bound for the proposed algorithm is derived, and the simulations validate that the performance of the proposed algorithm significantly outperforms existing algorithms, especially in the case of pulse loss.

More Electric Aircraft (MEA) has a great development prospect in the aviation industry thanks to the progressive power electronics technology and digital systems. Nevertheless, any fault occurring in the power electric system of MEA could be a fatal risk for the safety of the aircraft. To deal with the real-time fault detection and isolation (FDI) on the MEA, we put forward an FPGA-based neural network method which includes two stages: off-line construction using TensorFlow and real-time monitoring on the FPGA. Long Short-Term Memory (LSTM) network is applied because of its capability of learning from the long-term historical information in time series. A comprehensive MEA model based on the Boeing 787 power system created in PSCAD/EMTDC® and a commercial electric aircraft model based on Airbus E-Fan in Simscape are simulated to validate the effectiveness and generality of our proposed method. Adequate contrast experiments are conducted to acquire the most applicable architecture and configurations of the network. The results illustrate that the evaluation of the real-time condition can achieve accuracy over 99.5% within one sampling time on FPGA and reasonable hardware resource utilization.

The smart grid mainly suffers from two types of cascading failures: 1) interdependence cascading failure and 2) load propagation cascading failure. The former one happens due to the interdependence between power grid and communication networks. The latter one is caused by the load propagation in the single power grid. A tiny failure leads to the simultaneous occurrence of these two cascading failures. In this paper, we study the system robustness by considering the interdependence and load propagation. First, we develop a mathematical tool to analyze the load propagation in single network. Then, a percolation-based method is proposed to calculate the remaining fractions of survivals after the cascading failures stop. We estimate the node tolerance parameter T (the ratio of capacity to initial workload) threshold T'c, below which the entire system may suffer from the cascading failure. The effect of interdependence on Tc' is also studied, where lower Tc' is required for the less compact interdependence. We prove that the system performance approaches to the upper bound once the tolerance parameter T → ∞. Our analysis indicates that the fraction of survivals in the power grid is always greater than that in communication network, although the initial failure occurs in the power grid. The extensive simulations validate our mathematical analysis, and demonstrate that the relation between the number of initial failures and tolerance parameter threshold is super-linear.