Recently, an emerging research direction called Evolutionary Reinforcement Learning (ERL) has been proposed, which combines evolutionary algorithm with reinforcement learning (RL) for tackling the tasks of sequential decision making. However, the recently proposed ERL algorithms often suffer from two challenges: the inaccuracy of policy estimation caused by the overestimation bias in RL and the insufficiency of exploration caused by inefficient mutations. To alleviate these problems, we propose an Evolutionary Reinforcement Learning algorithm enhanced with Truncated variance and Distillation mutation, called ERL-TD. We utilize multiple Q-networks to evaluate state-action pairs, so that multiple networks can provide more accurate evaluations for state-action pairs, in which the variance of evaluations can be adopted to control the overestimation bias in RL. Moreover, we propose a new distillation mutation to provide a promising mutation direction, which is different from traditional mutation generating a large number of random solutions. We evaluate ERL-TD on the continuous control benchmarks from the OpenAI Gym and DeepMind Control Suite. The experiments show that ERL-TD shows excellent performance and outperforms all baseline RL algorithms on the test suites.

Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.

This paper analyzes the detailed transient process of a dual active bridge (DAB) in the internal circuits when a short-circuit fault of load occurs, which has not been considered in the existing researches. Based on the analysis results, the blocking duration of the signals after the fault and the series inductance at the output was derived to achieve the fault ride-through. The analysis results are verified by the simulations by MATLAB/Simulink and the experiments on a 500-W prototype. The results presented in this paper provide the theoretical reference for the design of strategies for overcurrent protection and fault riding-through of DABs.

The digital broadcast and mobile converged (DBMC) networks are now hot in research, but there is little research on personalized services over any kind of DBMC networks. This paper proposes a rule-based method to personalized services over DBMC networks. With the proposed approach, terminal can collect personalized service information from the broadcasted electronic service guide(ESG) and recommend these personalized services to the users for providing them with well user experience.