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.     

基于RBF神经网络优化的装备保障系统效能评估

针对现有装备保障系统效能评估方法存在依赖专家经验、主观性强等问题,提出一种基于径向基函数（RBF）神经网络优化的装备保障系统效能评估方法。通过完备交叉与预变异策略对遗传算法（GA）进行改进,利用改进的GA对RBF神经网络中心、宽度及输出权值进行全局寻优,并采用优化的RBF神经网络实现装备保障系统效能评估。仿真结果表明,该方法相比AGA-RBF和GA-RBF评估方法能更准确地评估装备保障系统效能值,并且可在一定程度上降低评估过程中主观因素的影响,确保评估结果更客观真实。     

Neural network-based adaptive decentralized learning control for interconnected systems with input constraints

In this paper, the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints. Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem, the decentralized control strategy can be established by a series of optimal control policies. A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem. This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function, respectively. The additional stabilizing term is introduced and an improved weight updating law is derived, which relaxes the requirement of initial admissible control policy. Besides, the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints. The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory. Finally, the effectiveness of the proposed decentralized learning control method is verified by simulation results.     

The mass,fake news,and cognition security

The widespread fake news in social networks is posing threats to social stability, economic development, and political democracy, etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the intrinsic propagation and cognition mechanisms of fake news. Since the development of cognitive science paves a promising way for the prevention of fake news, we present a new research area called Cognition Security (CogSec), which studies the potential impacts of fake news on human cognition, ranging from misperception, untrusted knowledge acquisition, targeted opinion/attitude formation, to biased decision making, and investigates the effective ways for fake news debunking. CogSec is a multidisciplinary research field that leverages the knowledge from social science, psychology, cognition science, neuroscience, AI and computer science. We first propose related definitions to characterize CogSec and review the literature history. We further investigate the key research challenges and techniques of CogSec, including humancontent cognition mechanism, social influence and opinion diffusion, fake news detection, and malicious bot detection. Finally, we summarize the open issues and future research directions, such as the cognition mechanism of fake news, influence maximization of fact-checking information, early detection of fake news, fast refutation of fake news, and so on.     

虚拟现实全景流体绘画系统的可用性研究

虚拟现实环境给三维空间场景应用带来更强的真实感和身临其境的体验。在虚拟现实场景中经 常需要使用 360全景图像生成沉浸式环境背景,其中全景流体动力学绘画系统提供了一个新的全景图绘制方 法。在调研虚拟现实技术在流体绘画领域应用的适用性测试中,本文综合运用用户测试法、操作日志分析法、 事后问卷调查法和访谈法,对基于虚拟现实的全景式流体绘画创作系统进行可用性评估,并创意性地使用可视 化方法辅助日志分析。所有的受试者均认为系统的可用性不低于一般水平,利用该系统完成流体绘画的创作工 作不需要受试者具有专业绘画经验。受试者对系统的功能效果持积极肯定的意见,并期待尝试用其他交互形式 代替手柄类交互方式以促进艺术创作的表达。其研究方法和结果将为同类系统的开发和评估提供参考。     

机器人相机空间智能编程方法研究*

针对传统的示教编程方式存在操作复杂,效率低,危险性高等不足,严重限制了工业机器人的推广应用。基于自然的人机交互示教方式,提出了一种基于计算机视觉的相机空间工业机器人智能虚拟编程方法,本方法不需要实际操作示教盒和机器人本体,仅采用辅助示教工具在视觉相机空间示教就实现了工业机器人的虚拟编程。主要研究了实现该方案的关键技术即基于相机空间映射模型的视觉定位技术以及基于K-means聚类算法实现的相机空间映射关系自学习技术。最后,基于自主开发的机器人平台,开展基于相机空间的虚拟智能编程实验,验证了本文提出的相机空间工业机器人智能编程方法的可行性及正确性。     

传统刺绣工艺在现代服装设计中的时尚化体现

对传统刺绣在当代时尚社会的发展进行了初步的研究,从刺绣本身的材质、图案题材、针法到刺绣工艺手法的结合创新,进行了分析,总结出传统刺绣的时尚性演变与发展,旨在对现有的刺绣情况进行总结,丰富设计手法,将传统刺绣的继承与发展落到实处。     

“双储”模式下的节能控制在小区供水系统中的应用

根据城市居民对供水的可靠性、稳定性、经济节能性的需求,课题组提出了一种基于"双储"模式的供水系统,并按照本市德胜住宅小区的供水要求,设计了一套由储水池、气压罐、变频器、多台水泵机组等主要设备构成的全自动变频恒压供水系统.经过近半年的运行测试,系统有效地解决了传统供水方式中存在的问题,增强了系统的总体性能,有明显的节能效果.     

智能缝制设备嵌入实时软件平台的定制与实现*

依据智能缝制设备数字化、网络化的应用特点,首先分析了面向智能缝制设备的嵌入式系统体系结构,重点研究了基于Linux开发面向领域的网络化嵌入实时软件平台的关键技术和方法。目前,基于该方法所实现的嵌入式软件平台已被成功应用于新型的电子花样缝纫机。     

兴趣度在基于关联分析的选课指导系统中的研究与应用*

简要地介绍了数据挖掘技术,通过对关联分析的经典算法Apriori在学生选课指导系统中的应用分析,发现了Apriori不适合学生选课指导系统的缺陷.提出了增加兴趣度阈值以减少产生的无用规则,提高挖掘精度,克服原系统缺陷的新算法,为学生选课辅助决策提供了良好的理论依据和实现方法.