Towards accurate intrusion detection based on improved clonal selection algorithm

Artificial immune system constructs a dynamic and adaptive information defense system through a function similar to the biological immune system. In order to resist the external invasion of useless and harmful information and ensure the effectiveness and the harmlessness of received information. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we propose an improved clonal selection algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and cloning them. Experimental results show that the improved algorithm achieves very good performance when applied to intrusion detection. And it is shown that the algorithm is better than BP neural network with its 99.5 % accuracy and 0.1 % false positive rate.     

Picture or it didn’t happen: catch the truth for events

Multimedia Tools and Applications - Pictures spreading on the Internet are essential for the authenticity of events. Each day, huge amounts of data are published on social media, and many of them...     

Exploring entropic uncertainty relation in the Heisenberg <Emphasis Type="Italic">XX</Emphasis> model with inhomogeneous magnetic field

In this work, we investigate the quantum-memory-assisted entropic uncertainty relation in a two-qubit Heisenberg XX model with inhomogeneous magnetic field. It has been found that larger coupling strength J between the two spin-chain qubits can effectively reduce the entropic uncertainty. Besides, we observe the mechanics of how the inhomogeneous field influences the uncertainty, and find out that when the inhomogeneous field parameter \(b<1\), the uncertainty will decrease with the decrease of the inhomogeneous field parameter b, conversely, the uncertainty will increase with decreasing b under the condition that \(b>1\). Intriguingly, the entropic uncertainty can shrink to zero when the coupling coefficients are relatively large, while the entropic uncertainty only reduces to 1 with the increase of the homogeneous magnetic field. Additionally, we observe the purity of the state and Bell non-locality and obtain that the entropic uncertainty is anticorrelated with both the purity and Bell non-locality of the evolution state.     

Optimized design of sandwich panels for integral thermal protection systems

This paper proposes a new ITPS panel with special corrugated-core webs which are designed with cutouts for weight saving. The structural design problem was formulated with mass per unit area of the ITPS as the objective function and some functional requirements as constraints. We developed the optimizer fulfilling both thermal and structural functions for minimal areal density. The optimization problem was solved by interpolating the residual error of response surface approximation (RSA) with Radial Basis Function (RBF) to establish the improved RSA (IRSA). The 400 preliminary design points were obtained using Latin Hypercube Sampling method. The quadratic polynomial RSA of the ITPS sandwich panel performance was generated by the least squares method (LSM) based on finite element results and IRSA was used to optimize the constraints. Transient heat transfer, stress and buckling analyses were conducted using finite element method (FEM). Finally, a new ITPS panel with optimal dimensions was obtained. The optimization results show that the areal density of the new ITPS panel decreases by 26.27 % compared with the previous research, which proves the potential of this new design optimization method for the future spacecraft vehicles.     

分布式光纤振动传感系统的多级零频点定位研究

为了减少直线结构Sagnac分布式光纤传感系统的定位误差,提出一种多级零频点的优化方法,通过分析直线结构Sagnac分布式光纤传感定位原理,确定固定的采样速率下离散化是造成定位误差大的原因.并分析得出振动信号频域曲线会周期出现多个零频点,进而对振动定位的多级零频点优化方法进行了理论推导,阐明高倍零频点下的定位误差会有效减小.通过实验验证表明,在总长为11.769 km的传感光纤上,优化后的定位方法在不改变采样速率下可保证定位误差提升到±20 m范围内,可以基本满足系统对外界振动信号进行精确定位的要求.     

人群运动中的视觉显著性研究

在公共场所中人们都倾向于以分组的形式进行运动,本文把这种以分组形式运动的若干个行人称为运动群组,具有视觉显著性的人群运动群组是场景理解的重点,其对人群的整体运动也影响最大。本文对运动群组的视觉显著性展开了研究,分别从规模、速度、组内紧致度和变化度４个方面来对运动群组的视觉显著性进行度量,并基于该度量给出了视觉显著性运动群组检测方法。首先,利用光流法对运动人群进行分析得到光流向量;然后通过层次聚类算法对运动人群进行分组;最后,基于本文所给出的度量计算每个群组的视觉显著性,以检测出视觉显著性最高的运动群组。实验表明该方法能够有效地对视觉显 著性运动群组进行检测,该研究成果可应用于人群场景理解、人群运动分析和人群场景分类等计算机视觉研究领域。     

桌面虚拟交付系统在高校机房中的应用

操作系统的重构更新、软件的安装升级、数据同传以及日常计算机系统的维护管理等问题常见于各大高校机房实验室的管理当中,解决这类问题不仅费时费力,而且缺乏创新.作者以桌面虚拟交付系统为切入点,通过实际案例与数据说明桌面虚拟交付系统在实验室建设当中的应用,实际使用该技术的优劣以及需要注意的问题.     

动车组齿轮箱吊杆有限元分析及试验验证

以某动车组齿轮箱吊杆为研究对象,依据ASME V&V标准对建立的吊杆有限元模型进行网格精度校核,并在此基础上对吊杆在极限载荷下的静强度进行计算分析。通过分析结果与试验结果的对比,验证齿轮箱吊杆有限元模型的可靠性。     

Coordinated control with multiple dynamic leaders for uncertain Lagrangian systems via self‐tuning adaptive distributed observer

This paper studies coordinated control of multiple Lagrangian systems with parametric uncertainties subject to external disturbances by proposing a fully distributed continuous control law based on the improved self‐tuning adaptive observer inspired by non‐identifier‐based high‐gain adaptive control technique. Under this distributed continuous control law, a group of Lagrangian systems are driven to the convex hull spanned by multiple heterogenous dynamic leaders, which can be any combination of step signals of arbitrary unknown magnitudes, ramp signals of arbitrary unknown slopes, and sinusoidal signals of arbitrary unknown amplitudes, initial phases, and any unknown frequencies. It is also worth to mention that this control law we propose, depending neither on any information of leader systems for uninformed followers, nor on external disturbances, even independent of neighbors' velocity, can achieve asymptotic tracking of multiple leaders without any additional condition instead of ensuring the ultimate boundedness of the containment error as in the literature. Copyright © 2016 John Wiley & Sons, Ltd.