多重网格法在地球物理正反演应用研究中的进展

现阶段地球物理三维勘探面临越来越复杂的问题,其要求更精细的三维网格剖分,现有的一些方法随着网格节点的增加其收敛速度相应减慢。对于应用中更复杂实际模型、更细密网格剖分以及更快收敛速度要求,难于有进一步提高。要有所突破,需借助计算数学最新进展,引入新的高效算法。多重网格法是近二十年迅速发展的一种求解微分方程近乎最优的新算法。本文首先简单介绍了多重网格法基本原理和运算格式,着重介绍了当前国内外多重网格法在地球物理正反演中的应用成果和发展现状。此外,对多重网格法在地球物理正反演中的应用前景及发展趋势进行了展望。     

基于CRF的中文评论有效性挖掘产品特征

方面级意见挖掘的任务通常包括从客户评论中抽取产品的特征、与产品特征相关联的观点词识别以及观点的极性判断三个方面。围绕如何实现中文评论的方面级意见挖掘问题,提出了利用条件随机场实现中文评论的方面级意见挖掘的四个主要步骤：数据预处理、训练集准备、为条件随机场模型定义学习函数、应用模型标注新的评论数据。在此基础上,通过以五种实际产品的中文评论语料为数据集,对该方法进行了数据实验。实验结果表明,该方法针对不同类型观点元素的抽取在评估性能指标上大部分达到或超过80%。为了进一步验证所提出方法的有效性,将研究结果进行了差异显著性检验。结果显示,用CRF对中文评论进行方面级意见挖掘和对英文评论的方面意见挖掘的性能差异不大。最后,比较了三种不同方法的方面抽取精度和情感分类精度,实验结果表明,CRF方法优于词典化的隐马尔可夫模型和关联规则挖掘方法。     

基于无迹H∞滤波的锂电池SOC估计研究

新能源汽车中锂电池的荷电状态(SOC)估计是电池管理系统的关键技术,对其准确估算有重要意义.所提的算法参考无迹卡尔曼滤波(UKF),将无迹变换(UT)融入到扩展H∞滤波中,用以估计锂电池系统状态均值和协方差,避免线性误差累积、增加算法的数值稳定性.在仿真实验中,在不同动态工况下分析了该算法的估计误差,证明算法在面对噪声干扰时具有较好的鲁棒性和准确性,是一种有效可行的算法.     

A low‐profile antenna with circularly polarized reconfigurable and omnidirectional radiation patterns

A low profile circularly polarized (CP) antenna with reconfigurable polarization is designed and presented, which can radiate omnidirectional patterns that can be switched between left‐hand circularly polarized (LHCP) and right‐hand circularly polarized (RHCP). A pair of arc‐shaped complementary dipoles is acted as reconfigurable elements by bridging four pin diodes at the dipole arced arms. A meander phase shift line is employed to connected the arc dipole arms and plate cavity to adjust the phase relationship of two sources. The proposed antenna exhibits the omnidirectional radiation pattern by combining six identical elements placed in a circular array configuration. 24 p‐i‐n diodes are exploited to six elements, by manipulating the dc bias voltage across the diodes, the polarization state of the antenna can be switched. The patterns of the antenna are similar to that of a dipole, but its size is only about Φ0.87 × 0.029λ₀ at 5.8 GHz. The overlapped bandwidth of measured 3‐dB axial ratio (AR) and 10‐dB return loss is 5.724‐5.87 and 5.738‐5.91 GHz for two polarization states, which are right on the target of ISM band. It can be well adapted to medical diagnosis systems.     

A simplification method for capacitance models in AlGaN/GaN high electron mobility transistors under large drain voltage using channel analysis

This article analyzes the bias dependence of gate‐drain capacitance (C_gd) and gate‐source capacitance (C_gs) in the AlGaN/GaN high electron mobility transistors under a high drain‐to‐source voltage (V_ds) from the perspective of channel shape variation, and further simplifies C_gd and C_gs to be gate‐to‐source voltage (V_gs) dependent only at high V_ds. This method can significantly reduce the number of parameters to be fitted in C_gd and C_gs and therefore lower the difficulty of model development. The Angelov capacitance models are chosen for verifying the effectiveness of simplification. Good agreement between simulated and measured small‐signal S‐parameters, large‐signal power sweep, and power contours comprehensively proves the accuracy of this simplification method.     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

结合全局和局部特征的BiGRU-RA图像中文描述模型

针对目前基于全局特征的图像描述模型存在细节语义信息不足的问题,提出结合全局和局部特征的图像中文描述模型.该模型采用编码器-解码器框架,在编码阶段,分别使用残差网络(residual networks,ResNet)和Faster R-CNN提取图像的全局特征和局部特征,提高模型对不同尺度图像特征的利用.采用嵌入了残差连接结构和视觉注意力结构的双向门控循环单元(bi-directional gated recurrent unit,BiGRU)作为解码器(BiGRU with residual connection and attention,BiGRU-RA).模型可以自适应分配图像特征和文本权重,改善图像特征区域和上下文信息的映射关系.此外,加入基于强化学习的策略梯度对模型的损失函数进行改进,直接对评价指标CIDEr进行优化.在AI Challenger全球挑战赛图像中文描述数据集上进行训练和实验,实验结果表明,该模型获得更高的评分,生成的描述语句更准确、更详细.     

Visualizing and understanding graph convolutional network

Multimedia Tools and Applications - The graph convolutional network (GCN), which can handle graph-structured data, is enjoying great interest in recent years. However, while GCN achieved remarkable...     

Neural network-based adaptive decentralized learning control for interconnected systems with input constraints

In this paper, the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints. Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem, the decentralized control strategy can be established by a series of optimal control policies. A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem. This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function, respectively. The additional stabilizing term is introduced and an improved weight updating law is derived, which relaxes the requirement of initial admissible control policy. Besides, the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints. The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory. Finally, the effectiveness of the proposed decentralized learning control method is verified by simulation results.     

A novel spherical-ordered macroporous CuO nanocatalyst for the electrochemical reduction of carbon dioxide

Journal of Applied Electrochemistry - The electrocatalytic reduction of CO2 is a promising research direction in resource utilization and sustainable energy development. However, there is still a...