无双线性对的无证书安全签密方案

已有的无双线性对的无证书签密方案,大都存在着机密性漏洞和签名选择性伪造问题。针对此问题,基于有限乘法交换群上的离散对数难题,提出一种无双线性对的无证书签密方案,并进行了安全分析和证明。指出采用随机预言模型的证明方法可能忽略不解决DL难题时对签密方案的攻击,不能证明破解签密方案的难度与解决DL难题相等价。通过对参数的数学分析,证明了破解本签密方案的必要条件是解决DL难题,不存在不解决DL难题攻击本签密方案的多项式时间算法。与已知所有方案相比,所提方案安全性更高。     

强化两相混合的超临界技术制备聚乳酸微粒

针对商品化聚乳酸微球粒径分布较宽难于适用气溶胶给药要求的不足,采用水力空化混合强化超临界流体辅助雾化技术(SAA-HCM)制备聚乳酸(PLA)超细微粒.该技术主要特点是通过在超临界流体和液相进料处引入水力空化混合器,强化两相间的混合.考察SAA-HCM过程混合器压力、温度、沉淀器温度、进料中CO2与液体溶液质量流量比和溶液质量浓度等操作参数对微粒形态和粒径分布的影响,成功制备出球形度较好,粒径分布较窄(1～3μm)的PLA微球.经X射线衍射(XRD)分析和差示扫描量热(DSC)分析显示,与原料PLA相比,微球晶型及热曲线变化不大,但结晶度下降.同时把操作参数与相行为进行关联,探讨了影响颗粒形貌的机理.对比超临界流体辅助雾化法(SAA)的实验结果表明:水力空化的引入能有效强化混合器内的两相传质,混合更好,能制备出适用于气溶胶给药要求的超细微粒.     

GJB151A/152A—1997亟待修订

从复杂电磁环境客观要求、标准化要求、军民用技术融合、标准自身存在不合规范问题等4个方面论述GJB151A/152A—1997修订的必要性和紧迫性。明确提出军事电磁兼容概念,针对标准化发展趋势、我军电磁兼容特殊情况和标准本身等方面的问题,提出了修订GJB151A/152A—1997的建议。     

基于现场试验的海上风电大直径单桩三维水平承载力研究

p－y 曲线法是计算分析水平受荷桩常采用的方法。但现场多为 3 m 以内直径试桩,对于海上风电大直径单桩( 4～8 m) 结构的桩土相互作用计算分析,其适用性值得商榷。结合江苏某海上风电大直径钢管桩水平静载荷试验,得到桩身挠度曲线和弯矩曲线; 采用 ABAQUS 建立单桩三维数值模型,将数值模拟结果与试验结果进行了对比分析,并对桩土接触法向刚度进行初步的敏感性分析,验证数值模型的有效性和适用性。随后对大直径单桩进行水平静载数值模拟,与规范推荐的 p－y 曲线法计算结果进行对比,结果表明,规范推荐的 p－y 曲线法过于保守,规范推荐的 p－y 曲线法不适于直接应用在大直径钢管桩的水平承载力分析; 所得结论对该区域海上风电大直径钢管桩设计优化具有指导意义。     

自适应斥力系数的无人机路径规划

使用人工势场法进行无人机路径规划时,往往存在目标不可达、运动轨迹迂回反复和路径长度过长等问题.传统的人工势场法不能根据环境具体信息对斥力系数进行调整,而现有的改进方法不能在自适应调整斥力系数的同时兼顾规划效果和规划时长.针对以上问题,提出了一种基于深度学习的无人机自适应斥力系数路径规划方法.首先通过融合遗传算法与人工势场法找出在特定环境下最合适的斥力系数样本集,其次利用该样本集训练残差神经网络,最后通过残差神经网络计算适应环境的斥力系数,进而使用人工势场法进行路径规划.仿真实验表明,该方法在一定程度上解决了人工势场法规划中目标不可达、运动轨迹迂回反复和路径长度过长等问题,规划效果和规划时长方面均有优异表现,能很好地满足无人机路径规划中对当前环境的自适应要求和快速规划的要求.     

Bi-directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL-C model with Rayleigh fading distribution

In this work, a deep learning (DL)-based massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL-C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi-directional long short-term memory (Bi-LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi-LSTM-based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function-trained Bi-LSTM-based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi-LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short-term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.     

基于对抗深度学习的无人机航拍违建场地识别

对违建场地的检测方法主要是通过人工对无人机航拍视频进行检查,存在检测精度低、识别性能差、工作效率低的问题。提出一种结合空间变换网络与Fast RCNN的生成对抗网络ASTN-Fast RCNN,通过深度学习与无人机航拍视频相结合自动识别检测处在建设初期的违建场地。将空间变换网络作为生成器,生成Fast RCNN目标检测器难以识别的旋转形变样本,并通过目标检测器与生成器的对抗式训练,提高检测器的鲁棒性。实验结果表明,该方法能够有效提高对无人机航拍违建场地的识别性能。     

一种基于YOLOv4-TIA的林业害虫实时检测方法

针对现有基于深度学习的林业昆虫图像检测方法存在检测精度低和检测速度慢的问题,提出一种结合改进PANet结构与三分支注意力机制的目标检测方法YOLOv4-TIA。通过对样本数量较少的昆虫类别进行数据增强,实现样本均衡分布。利用三分支注意力机制改进YOLOv4中的CSPDarkNet53骨干网络,同时通过旋转操作和残差变换建立维度间的依存关系,以提高有效的特征通道权重,在PANet结构上增加将跳跃连接与跨尺度连接相结合的特征融合方式,从而获取更丰富的语义信息和位置信息。在此基础上,采用Focal loss函数优化分类损失,解决正负样本不均衡的问题。实验结果表明,该方法的精确率和召回率分别达到85.9%和91.2%,相比SSD、Faster R-CNN、YOLOv4方法,其在保证检测速度的同时,能够有效提高检测精度,且实现对林业害虫的实时精确监测。     

A formally specified ontology management API as a registry for ubiquitous computing systems

Recently, several standards have emerged for ontology markup languages that can be used to formalize all kinds of knowledge. However, there are no widely accepted standards yet that define APIs to manage ontological data. Processing ontological information still suffers from the heterogeneity imposed by the plethora of available ontology management systems. Moreover, ubiquitous computing environments usually comprise software components written in a variety of different programming languages, which makes it particularly difficult to establish a common ontology management API with programming language agnostic semantics. We implemented an ontological Knowledge Base Server, which can expose the functionality of arbitrary off-the-shelf ontology management systems via a formally specified and well defined API. A case study was carried out in order to demonstrate the feasibility of our approach to use a formally specified ontology management API to implement a registry for ubiquitous computing systems.     

异构环境下实时多媒体信息组播技术

该文先介绍了组播骨干网(Mbone)和组播的相关概念,然后讨论了Internet和Mbone的异构性,在此基础上论述了如何有效地在异构环境下传输实时多媒体信息,并比较了两种对异构性的不同解决方案.