北京冬奥会五环标志塔——延庆“海陀塔”设计

第24届冬奥会在北京成功举办,北京延庆“海陀塔”作为北京冬奥会标志性景观,已成为本届冬奥会的重要文化遗产被永久保留。“海陀塔”的设计为造型与照明一体化设计,使得设计可以综合考虑造型、材料和照明之间的整体关系,并通过技术和工程手段使三个方面得以高度契合、相互辅佐,更好地实现节能理念,塑造设计意象。     

小电流接地故障仿真分析及选线方法研究

基于小电流接地故障种类繁多、定位困难的现状,提出一种新的单相接地故障定位方法.通过 MATLAB仿 真分析故障特点,探讨现有选线方法的优劣以及适用范围,通过信息融合技术将多种选线方法结合,用综合选线的方式提高选线精度。     

缓减电压暂降影响的电-气综合能源系统储气装置选址定容方法

针对电力系统故障导致的电压暂降通过电-气耦合设备对系统造成多次影响的问题,提出了一种缓减电压暂降影响的电-气综合能源系统储气装置选址定容方法。分析了电力系统发生电压暂降事件对电-气综合能源系统的影响;基于灵敏度分析提出了天然气系统储气装置选址方法;综合考虑投资成本与效益,以安装储气装置后效益的最大值为目标函数,确定储气装置最佳配置方案。通过对由IEEE 14节点电力系统和11节点天然气系统构成的电-气综合能源系统进行算例分析,验证了该缓减方法的正确性与有效性。     

5G毫米波通信用低插损高隔离GaAs PHEMT单刀双掷开关

基于0.15μm GaAs赝配高电子迁移率晶体管(PHEMT)工艺,实现了一款用于5G毫米波通信的低插损高隔离单刀双掷(SPDT)开关芯片。为了降低插损,每个开关支路通过四分之一波长阻抗变换器连接到天线端,并通过优化传输线和器件总栅宽实现了良好的端口匹配;为了提高隔离度,采用了三并联多节枝的分布式架构形成高的输入阻抗状态,实现信号的全反射。芯片面积为2.1 mm×1.1 mm。在片测试结果显示,在24.25~29.5 GHz的5G毫米波频段内该SPDT开关实现了小于1.1 dB的极低插损和大于32 dB的高隔离度,1 dB压缩点输入功率大于26 dBm。     

基于分段电容阵列的改进型逐次逼近型ADC

为缩短高速模数转换器(ADC)中高位(MSB)电容建立时间以及减小功耗,提出了一种基于分段式电容阵列的改进型逐次逼近型(SAR)ADC结构,通过翻转小电容阵列代替翻转大电容阵列以产生高位数字码,并利用180 nm CMOS工艺实现和验证了此ADC结构。该结构一方面可以缩短产生高位数码字过程中的转换时间,提高量化速度;另一方面其可以延长大电容的稳定时间,减小参考电压的负载。通过缩小比较器输入对管的面积以减小寄生电容带来的误差,提升高位数字码的准确度。同时,利用一次性校准技术减小比较器的失配电压。最终,采用180 nm CMOS工艺实现该10 bit SAR ADC,以验证该改进型结构。结果表明,在1.8 V电源电压、780μW功耗、有电路噪声和电容失配情况下,该改进型SAR ADC得到了58.0 dB的信噪失真比(SNDR)。     

Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization

Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.

     

Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

Privacy protection is the key to maintaining the Internet of Things (IoT) communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key to checking steganography security, and its ultimate goal is to extract embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.     

紫外光通信协作蜂群无人机网络动态能效均衡算法

针对蜂群无人机在复杂战场环境下的能效均衡需求,提出一种紫外光通信协作蜂群无人机网络动态能效均衡算法。该算法结合紫外光抗干扰、高保密的优点,为蜂群无人机提供一种可靠的通信方式,并通过引入自适应节点度、无人机能效和距离稳定度参数加权分簇来选举簇首,使算法更加适用于动态场景。最后结合无线紫外光通信及能量消耗模型,在高斯马尔可夫移动模型下对蜂群无人机进行了仿真分析,结果表明,本算法相比LEACH算法可以将网络生存周期提升16%,同时可将平均簇首数量降低57%,数据传输量提升近两倍。     

基于动态贝叶斯网络的低慢小目标威胁评估北大核心CSCD

针对复杂地形下低慢小目标难以防御的难题,本文分析并研究对于与低慢小目标威胁评估具有影响作用的因素,提出一种基于动态贝叶斯网络的威胁评估方法。基于低慢小目标的威胁属性,构建动态贝叶斯网络拓扑,利用贝叶斯理论推理出威胁值;并通过仿真试验验证该种基于动态贝叶斯网络的方法在对低慢小目标进行威胁评估是可行的。     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.