自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.     

异构分层无线网络中基于逗留时间的动态流量均衡算法研究

为使异构分层无线网络能服务更多的移动用户,提出了一种基于逗留时间的动态流量均衡算法.该算法首先根据用户移动模型计算其在小区内的逗留时间,然后基于小区呼叫到达率和重叠覆盖小区的流量状态来确定一个周期内呼叫转移的数量,最后依据逗留时间门限值将重负载小区中满足条件的呼叫转移到轻负载的重叠覆盖小区中.为降低切换呼叫掉线率,还对异构网间的呼叫切换策略做了改进.仿真实验结果表明,本算法在新呼叫阻塞率和切换呼叫掉线率等性能指标上比传统方法有显著的提高.     

介孔-大孔结构无定形硅-铝催化剂的核磁共振研究

合成了具有介孔-大孔双模型分级结构的无定形硅-铝催化剂,利用硅和铝核的固体魔角核磁共振谱,考察了合成样品中硅和铝结构上的原子特征.     

重复压裂候选井多级模糊决策方法

重复压裂是油气田开采后期增产稳产的一个重要措施。但是，目前的重复压裂效果不很理想，具体表现在：压裂后增产幅度小，含水上升，产量递减速度快，有效期短，有效率低。因此有必要对重复压裂候选井进行优选。而影响重复压裂效果的因素众多，可以分为：地层物性、产能、原油物性、工程因素四大类。而每一大类中又可细分为很多影响因素。因此，常规的单一目标函数方法不能用于该类问题。将系统工程理论中的层次分析法和多级模糊决策方法结合起来并应用于重复压裂候选井的评判上，对中原油田的7口井进行了优选。实践表明：该方法克服了以往重复压裂候选井选层的盲目作法，并且综合考虑了候选井的多个影响指标，因而更加合理。     

美军F-22的先进航空电子系统

F-22“猛禽”战斗机将取代F-15成为美国空军的下一代空中优势战斗机。文中主要论述F-22“猛禽”战斗机的综合化航空电子系统（IAS）的分层设计、综合化体系结构以及主要设备，包括共用综合处理器（CIP）、APG-77雷达、通信、导航和识别（CNI）、电子战（EW）、外挂管理系统（SMS）、惯导系统（IRS）和控制与显示（C＆D）。最后还介绍了客错与恢复机制。