智能超表面辅助低轨卫星通信技术综述

卫星通信能够提供超视距、大容量的通信服务,但在城市区域、峡谷森林等视线受遮蔽严重的区域,会出现视距衰落严重、多径效应显著等问题,影响通信质量。近年来出现的智能超表面(RIS)可以通过动态控制反射单元的电磁性质,构建可控电磁环境,为改善低轨(LEO)卫星通信质量提供新的思路。本文基于RIS基本原理与优势,对RIS用于LEO卫星通信中信道建模、信道估计、波束成形等关键技术进行探讨,最后分析了目前RIS在实际应用中面临的主要挑战,以期为LEO卫星场景下的RIS研究提供参考。     

实测卫星频谱占用状态拟合与预测方法

由于在不同时间、不同空间卫星接收数据底噪是动态起伏的,传统建模固定门限的方法存在缺陷。本文在时间维度上对卫星频谱感知数据的频谱占用模型进行分析,利用自适应阈值法确定噪声门限,对卫星频谱数据进行预处理,得到卫星频谱占用长度序列。为对卫星频谱的态势进行有效的统计分析,利用泊松分布和指数分布方法对频谱占用时间长度序列的概率密度曲线进行拟合,得到了适用于卫星频谱占用时间序列的概率分布模型。基于所得的卫星频谱占用状态模型,通过两状态马尔可夫链计算出卫星信道某一频点的状态转移矩阵,从而预测出信道占用和空闲的概率。利用卫星频谱感知数据构建的数据集进行反向传播(BP)神经网络训练,预测某一频点的占用长度。通过计算BP神经网络与传统的长短期记忆(LSTM)神经网络预测法的均方根误差(RMSE),得到LSTM神经网络的RMSE为2.208 1,BP神经网络的RMSE为0.172 8。评估结果表明,BP神经网络准确度高。     

基于TDD的低轨卫星跳波束资源分配算法

低轨(LEO)卫星跳波束技术可以灵活分配系统资源,适用于业务分布不均匀的场景。时分双工(TDD)方式可以减少星载和地面终端设备的天线数量,有效降低其复杂度,并有利于开展上下行非对称业务。本文提出一种基于TDD的LEO卫星跳波束资源分配算法,在满足业务需求的基础上,以最小化时域资源消耗为目标,建立支持跳波束和多频时分多址接入(MF-TDMA)机制的LEO卫星反向链路资源分配模型;综合考虑星地动态时延补偿,采取一种多层次的跳波束时隙架构设计,以最大化可用时隙为目标,建立上下行时隙切换模型,并提出一种基于TDD的跳波束时隙排布优化方法。仿真结果表明,对比于传统的MF-TDMA资源分配方法或固定多波束均分算法,本文提出的算法能有效提高系统的时隙利用率和吞吐量。     

用于太赫兹通信的双向全并行相位估计算法

在太赫兹通信的超宽带应用场景下,太赫兹载波是通过多次倍频实现,因而导致相噪严重恶化。基于太赫兹频率下的多极/零相位噪声模型,在传统的盲相位估计算法的基础上,提出了一种全并行化的相位估计算法。该算法通过在每个并行数据中插入导频,将导频相噪信息作为初始相位对并行数据进行相位扩展及旋转鉴相。借鉴传统盲相位估计算法的最佳相位估计值的判决选择思路,同时利用前一时刻导频和当前时刻导频,对当前并行数据从前后两个方向进行相位估计并对前后两个估计相位基于距离前后导频的位置进行加权求和以获得最佳相位估计信息。经过仿真验证,通过该算法后残余相噪在1 MHz和10 MHz处分别降低10 dBc/Hz和25 dBc/Hz。     

Adversarial Data Hiding in Digital Images

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting “sensitive pixels” in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.     

Conserved Control Path in Multilayer Networks

The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighted matching, to determine the paths that play the most consistent roles in controlling signal transmission in multilayer networks. As a pragmatic application, we demonstrate that the control paths detected in a multilayered pan-cancer network are statistically more consistent. Additionally, they lead to the effective identification of drug targets, thereby demonstrating their power in predicting key pathways that influence multiple cancers.     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

区域电网调度系统中维护信息对称性特征识别

为提升对区域电网调度系统维护信息对称性特征的识别效率及准确率,文中首先通过小波变换方法提取区域电网调度系统中维护信息的特征,然后运用模糊C均值算法分析特征数据的分布情况,并以此为基础,获取维护信息对称性故障特征的波形,根据Ucosθ波形特征识别结果,实现对区域电网调度系统中维护信息对称性特征的识别.实验结果表明:在不同...     

出境旅游网络关注度时空演变及影响因素研究——以泰国为例

网络关注度是测量潜在旅游者对目的地旅游关注情况及需求变化的重要手段之一。基于百度指数,以我国31个省（区、市）（不含港、澳、台）的泰国旅游网络关注度为研究对象,运用季节性强度指数、地理集中度指数、赫芬达尔-赫希曼指数、地理探测器等方法,探讨我国居民对泰国旅游网络关注度的时空演化规律及其影响因素。结果表明：从时序演化上看,2011—2019年泰国旅游网络关注度呈波动上升态势,可划分为快速上升期和平稳发展期2个阶段,地区季节性差异显著,3月、7月、12月为泰国旅游网络关注度的高峰时段;从空间分异上看,泰国旅游网络关注度空间分异变化不大,空间集聚趋于分散状态,整体呈“东高-西低”的阶梯状递减特征,高关注度地区主要集中在东部地区及四川省,低关注度地区则主要分布于除四川省外的西部省份;从影响因素上看,经济发展水平（人均可支配收入、GDP）、交通便利程度、贸易开放度以及国际旅游开放度共同影响泰国旅游网络关注度的空间分布格局。     

沈阳机床技改项目弱电智能化设计

沈阳机床（集团）有限责任公司的弱电智能化设计成功地将智能卡应用系统与MES有效的集成,为国内首创。生产物流条码化管理使其物流管理水平走在了国内机械行业的前列。系统采用了优化的制冷机群控、冷却塔群控技术,并在保持冷冻水大温差的条件下采用了变冷冻水温度的节能环保设计。全网冗余设计、弹性架构并融合安全的计算机网络充分保证厂区网络高可靠性、可扩展性和安全性。设计充分体现了信息化融合工业化的数字化工厂理念。