Towards evolvable Internet architecture-design constraints and models analysis

There is a general consensus about the success of Internet architecture in academia and industry. However, with the development of diversified application, the existing Internet architecture is facing more and more challenges in scalability, security, mobility and performance. A novel evolvable Internet architecture framework is proposed in this paper to meet the continuous changing application requirements. The basic idea of evolvability is relaxing the constraints that limit the development of the architecture while adhering to the core design principles of the Internet. Three important design constraints used to ensure the construction of the evolvable architecture, including the evolvability constraint, the economic adaptability constraint and the manageability constraint, are comprehensively described. We consider that the evolvable architecture can be developed from the network layer under these design constraints. What＇s more, we believe that the address system is the foundation of the Internet. Therefore, we propose a general address platform which provides a more open and efficient network environment for the research and development of the evolvable architecture.     

Fixed‐time synchronization for complex‐valued BAM neural networks with time delays

In this paper, the fixed‐time synchronization for complex‐valued bidirectional associative memory (BAM) neural networks with time delays is studied. Based on the fixed‐time stability, the Lyapunov functional method and some inequality techniques, a new criterion is presented to guarantee that the addressed systems achieve synchronization in fixed time and a more accurate estimation independent of the initial conditions is given for the settling time. Meanwhile, a new nonlinear delayed controller different from the existing ones is designed. In the end, two numerical examples are provided to illustrate the effectiveness of the obtained result.     

全景相机与PTZ相机联动监控系统及位置关联模型

为实现大范围监视和指定区域精细观测与分析,提出一种全景相机与PTZ相机组合的联合监控系统.通过分析两相机安装的各种位置关系,建立起了统一位置关联模型.利用所建立的位置关联模型可方便地对监视场景的目标位置从全景图像到PTZ相机图像转换,为实现场景目标的联合监控奠定基础.实验结果表明,利用该模型所计算的水平、俯仰坐标相对于其实际测量值的平均误差均小于云台相机的实际控制误差,满足系统的联动控制要求.     

认知任务驱动的fMRI脑功能网络构建方法

针对脑功能网络的构建受到特定大脑图谱对兴趣点描述准确度及覆盖度的限制,提出了基于认知任务信息和神经影像数据的脑功能网络构建方法。首先计算fMRI体素对于认知任务的敏感度,然后在此基础上选取兼顾分布均衡和去中心化的大脑兴趣点,从而构建任务驱动的脑功能网络。实验通过在人脸情绪识别认知任务相关的梭状回构造任务驱动的脑功能网络,其度中心性、聚类系数、全局效率、局部效率这四个复杂网络指标均优于典型大脑图谱梭状回中兴趣点构成的网络。结果表明,计算得到的大脑兴趣点具有更强的整合性,更适合用于表征特定认知任务下的脑功能网络。     

基于深度特征增广的跨域小样本人脸欺诈检测算法

随着人脸识别技术的发展,人脸欺诈攻击已经成为一项实际的安全问题,人脸欺诈检测算法用于及早发现该类攻击,保护系统安全.文中将一种经典域自适应算法扩展到深度神经网络中,首先定义了基于深度特征增广的域自适应层,提出了一种基于深度特征增广的跨域小样本人脸欺诈检测算法.该算法在已有的基于全卷积神经网络的人脸欺诈检测深度神经网络的中部嵌入域自适应层将卷积特征图增广,来适配源域和目标域的差异,随后根据增广后的特征图进行像素级分类,最后将像素级概率图从空间上融合为帧级决策.文中在CASIA-FASD,Replay-Attack和OULU-NPU 3个数据集和6个常见测评协议(2个CASIA-FASD与Replay-Attack跨库协议和4个OULU-NPU标准协议)下进行实验,验证了算法在不同背景、不同攻击设备、不同相机等跨域情况下的性能.实验表明,基准FCN人脸欺诈检测算法已经能够达到较好的性能,在此基础上,借助小样本目标域数据学习域自适应模型,可进一步显著提升性能,将错误率减半(CASIA-FASD训练+Replay-Attack测试的HTER指标从27.31％降至11.23％,Replay-Attack训练+CASIA-FASD测试的HTER指标从37.33％降至21.83％,OULU-NPU标准协议IV的ACER指标从9.45％降至5.56％),实验结果验证了基于深度特征增广的跨域小样本人脸欺诈检测算法的有效性.     

A novel ADRC‑based design for a kind of fexible aerocraft

The paper presents a novel control design, which is based on the idea of active disturbance rejection control (ADRC), for a kind of fexible aerocraft whose controlled variable cannot be measured directly. Since the original frame of ADRC cannot be directly applied, the paper puts forward a new extended state observer (ESO) and the corresponding ADRC law. In order to assign the poles of the closed-loop system to ideal positions such that the vibration can be quickly suppressed, an elastic damping term is added into feedback law. The advantages of the new ESO for efectively estimating both the rigid mode and elastic mode from the measurements are discussed. Moreover, the analysis on the stability, the relative stability and the steady state of the closed-loop system is given. Finally, the efectiveness and robustness of the proposed ADRC are verifed by simulations.     

任务感知双原型网络的人物交互少样本识别

人物交互(HOI)识别是计算机视觉领域的重要研究热点.随着深度学习在图像分类任务中的巨大成功,人物交互识别任务也取得重大进展,但样本不平衡和组合爆炸问题仍是制约当前人物交互识别任务性能的关键挑战.由此,将人物交互识别任务与少样本学习相结合,将人物交互识别任务定义为一个少样本任务,并提出了任务感知双原型网络(TDP-Net)来解决少样本人物交互任务.具体地,首先使用图方法为每个任务生成语义感知的任务表示作为任务的先验信息,并使用语义图注意力模块(SGA-Module)生成注意力权重,对特征图中不同区域进行不同重要程度的关注,以适应不同任务条件下的映射关系,实现在新任务中自动推理.此外,还设计了一个双路原型模块(DP-Module)以分别产生交互类别的动作类原型和物体类原型,并分别对动词和名词进行分类.通过分别为动作和物体建立类原型,有效地分离了动作和物体间复杂的视觉关系.同时由于人物交互类别之间具有相似性,可通过重新组合动作和物体类别将知识迁移到新的交互类别中.实验结果表明,该模型在人物交互少样本任务上的平均准确率比基线方法在两个实验设置上分别提高了3.2个百分点和15.7个百分点,验证了TDP-Net在少样本人物交互任务中的有效性.     

Fe_(40)Co_(40)Zr_(10)B_9M_1(M=Cu,Ge,Ag)合金的热稳定性及微观结构研究

采用单辊快淬法制备Fe40Co40Zr10B9Cu1、Fe40Co40Zr10B9Ge1和Fe40Co40Zr10B9Ag1非晶合金薄带,并对3种合金在不同温度下进行热处理.利用差热分析仪(DTA)和X射线衍射仪(XRD)等测试手段对样品的热稳定性及微观结构进行研究.结果表明:Fe40Co40Zr10B9Cu1、Fe40Co40Zr10B9Ge1和Fe40Co40Zr10B9Ag13种合金的激活能分别为262.4、267.6和252.3 k J/mol,说明Cu元素、Ge元素和Ag元素的添加对合金的热稳定性影响不大,Ge元素的添加最有利于提高合金的热稳定性.3种合金的晶化过程相似:非晶→非晶+α-Fe(Co)→α-Fe(Co)+Zr Co3B2+Fe(Co)3Zr.     

用双层辉光等离子法在钛表面制备的Ti-Pd合金层性能研究

采用双层辉光等离子冶金技术在纯钛表面制备了Ti—Pd合金层。其深度大约为90μm，Pd含量呈梯度变化，并出现了TiPd3，TiPd2，Ti2Pd3，Ti3Pd5，TiPd，Ti4Pd等6种化合物相和Pd相。合金层在100℃的NaCl饱和溶液＋HCl溶液以及40℃的8．6％H2SO4溶液中的耐缝隙腐蚀性能优于Ti0．2Pd合金；在室温80％H2SO4的溶液中，腐蚀速率仅为0．682mm／a，是Ti0．2Pd合金的18．2％：在室温30％HCl的溶液中，表面Ti—Pd的腐蚀速率仅为0．004mm／a，是Ti0．2Pd合金的12．5％。