微型无人机群空空数据链协议建模与仿真

为较为准确、客观地评价微型无人机群（UAV：Unmanned Aerial Vehicles）空空数据链系统的各项性能指标,根据ZigBee（Zig＋Bee无线数传网络）协议,通过建立时隙轮询表完成时隙分配。在简要分析ZigBee协议栈各层的功能及其网络拓扑结构的基础上,重点阐述了空空数据链协议的时隙分配机制及网络地址分配机制,讨论了利用网络仿真工具OPNET（Optimized Network Engineering Tools）建立该无线通信协议模型的过程,并通过构建一个网络仿真平台对信道接入延时、吞吐量等进行仿真分析。验证了该空空数据链采用的通信协议的有效性,同时也为其他网络协议仿真提供了参考。     

基于IPv6对等网技术的资源共享方法

为了改善P2P（Peer-to-Peer）软件带宽消耗高、网络利用率低的问题,提出了将资源发现过程与资源共享过程分开执行的方法,并在IPv6环境下定义了两个私有协议P2PIEP（Peert-to-Peert Information Exchchange Protocol）和P2PDEP（Peer-to-Peer Data Exchange Protocol）。这两个协议分别服务于资源发现过程和资源共享过程。P2PIEP使用小世界模型,用于发现资源信息;P2PDEP封装在UDP（User Datagram Protocol）数据报中且具有较小的协议首部,从而达到提高网络利用率的目的。     

基于动态算法的序列分析

为了获得2009年新型甲型H1N1流感病毒与2008流感病毒的基因序列及氨基酸序列的一致性,以便对一个基因家族的生物学特征有一个简明扼要的了解,针对目前流行的新型甲型H1N1流感病毒的基因序列及其所编码的氨基酸序列,采用动态规划算法对其一级序列进行序列相似度分析,获得了2009年新型甲型H1N1流感病毒的NA和M基因片段以及2008年猪源性甲型H1N1流感病毒的相应基因片段同源性高、在有些位点发生了基因突变增添和突变缺失等重要基因信息。为此次新型甲型H1N1流感病毒的研究提供了依据。     

摩擦式提升机安全性能的初步分析

介绍了多绳摩擦式提升机的工作原理,对摩擦式提升机的安全性能进行了分析,探讨了摩擦式提升机的防滑安全系数和具体的防滑安全措施,并对摩擦提升机的主要优缺点进行了比较．     

Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Multimedia Tools and Applications - A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10782-7     

A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN

Point of interest (POI) recommendation problem in location based social network (LBSN) is of great importance and the challenge lies in the data sparsity, implicit user feedback and personalized preference. To improve the precision of recommendation, a tensor decomposition based collaborative filtering (TDCF) algorithm is proposed for POI recommendation. Tensor decomposition algorithm is utilized to fill the missing values in tensor (user-category-time). Specifically, locations are replaced by location categories to reduce dimension in the first phase, which effectively solves the problem of data sparsity. In the second phase, we get the preference rating of users to POIs based on time and user similarity computation and hypertext induced topic search (HITS) algorithm with spatial constraints, respectively. Finally the user’s preference score of locations are determined by two items with different weights, and the Top-N locations are the recommendation results for a user to visit at a given time. Experimental results on two LBSN datasets demonstrate that the proposed model gets much higher precision and recall value than the other three recommendation methods.

     

一种基于深度学习的视网膜病变图像识别方法

为解决医学上糖尿病性视网膜病变图像人工识别困难、精度差等问题,提出一种基于多特征融合的卷积神经网络识别方法。在VGG-16模型的基础上,通过融合每层网络上的局部特征,增强模型的特征提取能力。选用Softmax分类器,使病变图像识别更加准确。使用OpenCV图像处理工具采用加噪、上下左右不同角度翻转、调节对比度等5种方式扩充训练集。实验结果表明,基于多特征融合的深度学习框架图像识别系统在数据集上的平均识别精度达到94.23%,相较于Alex-Net、Google-Net、Compact-Net、ResNet-101等模型分别提高了10.56%、7.80%、6.01%、0.02%,验证了该方法的有效性。该模型具有很好的鲁棒性。     

手指静脉图像感兴趣区域提取研究综述

近年全球的疫情促使生物识别技术进一步发展,指静脉识别作为第二代生物识别技术因其具有极高的安全性等优点而被应用于各个领域。指静脉图像感兴趣区域提取作为指静脉识别系统至关重要的一部分,是识别系统性能与适应不同应用场景的保证。分别介绍了商用指静脉识别产品与科研用指静脉采集设备,对其公开数据集进行了整理与分析;重点论述了指静脉图像预处理中感兴趣区域提取的主要工作,对各个步骤的代表性方法进行了深入梳理与分析,并对指静脉特征提取与匹配认证进行了概述;最后对其今后的发展与应用进行了总结与展望。     

考场环境下考生视线估计方法

考生异常行为的监测容易使监考人员产生视觉疲劳。借鉴监考人员发现异常的过程,提出一种可用于考场异常行为分析的视线估计模型。为了减少图像中视线的信息损失,采用注视向量表示视线的大小和方向。该模型分为生成器、视线合成模块、鉴别器,先将考生头部图像输入生成器生成注视向量,再将头部位置和注视位置输入到合成模块得到真实注视向量。将头部图像与上述所得的两种向量输入鉴别器中,其生成对抗模式达到最优时,可得到生成真实值的生成器模型。实验结果表明,在多个考场环境中,该方法的性能优于所对比的几种方法。其中与Lian等人方法相比AUC（Area Under Curve）提高了2.6%,Ang（Angular error）和Dist（Euclidean distance）分别有效降低了20.3%和8.0%。     

Structural Reliability Analysis for Implicit Performance with Legendre Orthogonal Neural Network Method

In order to evaluate the failure probability of a complicated structure, the structural responses usually need to be estimated by some numerical analysis methods such as finite element method (FEM). The response surface method (RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However, the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network (LONN)-based RSM to estimate the structural reliability. In this method, the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method, i.e. first-order reliability methods (FORM) to calculate the failure probability of the structure. Numerical examples show that the proposed approach is applicable to structural reliability analysis, as well as the structure with implicit performance functions.