This paper presents a finite control set model predictive control (MPC) using a designed dynamic reference for bidirectional electric vehicle (EV) chargers. In the conventional MPC scheme, a PI controller is involved to generate an active power reference from the DC voltage reference. It is hard to find one fixed set of coefficients for all working conditions. In this paper, a designed dynamic reference based MPC strategy is proposed to replace the PI control loop. In the proposed method, a DC voltage dynamic reference is developed to formulate the inherent relationship between the DC voltage reference and the active power reference. Multi-objective control can be achieved in the proposed scheme, including controlling of the DC voltage, battery charging/discharging current, active power and reactive power, independently. Bidirectional power flow is operated effectively between the EV-and the grid-side. Experimental results are obtained from a laboratory three-phase two-stage bidirectional EV charger controlled by dSPACE DS1104. The results show that fast dynamic and good steady state performance of tracking the above objectives can be achieved with the proposed method. Compared with the system performance obtained by the conventional MPC method, the proposed method generates less active power ripples and produces a better grid current performance.

In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 $\mu s$ ). Fast response speed to variations in voltage reference and load resistance can be achieved t.

This paper presents the four-quadrant operation modes of bidirectional chargers for electric vehicles (EVs) framed in smart car parks. A cascaded model predictive control (MPC) scheme for the bidirectional two-stage off-board chargers is proposed. The controller is constructed in two stages. The model predictive direct power control for the grid side is applied to track the active/reactive power references. The model predictive direct current control is proposed to achieve constant current charging/discharging for the EV load side. With this MPC strategy, EV chargers are able to transmit the active and reactive powers between the EV batteries and the power grid. Apart from exchanging the active power, the vehicle-for-grid (V4G) mode is proposed, where the chargers are used to deliver the reactive power to support the grid, simultaneously combined with grid-to-vehicle or vehicle-to-grid operation modes. In the V4G mode, the EV battery functions as the static var compensator. According to the simulation results, the system can operate effectively in the full control regions of the active and reactive power (PQ) plane under the aforementioned operation modes. Fast dynamic response and great steady-state system performances can be verified through various simulation and experimental results.

This article presents a model predictive sliding control (MPSC) strategy with the dynamic reference tracking for cascaded H-bridge (CHB) multilevel converters. The nonlinear predicted sliding mode control (PSMC) and linear model predictive control (MPC) are combined as a two-level cascaded control structure. The proposed approach aims to get rid of time-consuming tuning to reach the good performance in the presence of uncertainties and disturbances, such as proportional–integral (PI). The MPC scheme is designed to predict the future optimal current and voltage vectors with a designed cost function. The high-level PSMC algorithm is proposed for the dynamic current reference with less chattering problems. The stability of the proposed strategy is analyzed with the Lyapunov direct method. The design principle of the system parameters is introduced with a step-by-step tuning procedure. With the proposed method, the response time can be reduced from 60 to 30 ms during the start-up conditions in the simulation tests. The overshoot of the cell voltages can be eliminated with low-harmonics current and dc-link voltage targets tracking. The validity and effectiveness of the proposed method are implemented by the experimental tests on a laboratory two-cell CHB converter. Compared with the PI-based MPC approach, the current total harmonic distortion (THD) can be reduced and the dc-side performance, including overshoot/undershoot and response speed, can be improved.

Recent years have witnessed the rapidly growing of the amount of semistructured documents in real-world applications. Due to the huge size of the real-world data, how to manage semistructured documents effectively is a big challenge for researchers. As a fundamental task in natural language processing field, document classification is a feasible way to handle the large-scale semistructured documents. However, existing methods fail to explicitly take advantage of the hierarchical semantics in semistructured documents. It's known that the contained semantics is beneficial for understanding the semistructured documents. Considering the hierarchical structure of a given semistructured document, we propose a semistructured document classification framework which explicitly utilizes the semantic hierarchical attention mechanism. More specifically, the hierarchical attention mechanism and graph neural network are employed to model semistructured documents, by which the multilevel semantic relationships and grammatical information are considered. Moreover, we propose an adaptive class cost learning method to treat the issue of data imbalance. Comprehensive experiments are conducted on two real-world data sets, and the results demonstrate that our framework performs better than selected baselines for semistructured document classification.

Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.