基于改进遗传模拟退火算法的WSN路径优化算法*

为了更好地解决无线传感器网络（WSN）数据传输的路径优化问题,降低数据传输的能量消耗,提出了一种基于改进遗传模拟退火算法（SAGA）的WSN路径优化算法。首先根据优化目标建立数学模型,然后设计了种群的编码方式,并对遗传算法中的适应度函数、交叉算子、变异算子进行改进,使算法能够更加有效地避免陷入局部搜索;接着根据旧种群和新种群每个对应个体的不同进化程度提出了一种新的Metropolis准则,使模拟退火算法的跳变更具有规律性。实验结果显示：与其它路径优化算法相比,该算法不仅能生成更节能的数据传输路径,而且优化时间也大大降低。所以该算法是一种高效的路径优化算法。     

基于稀疏表达和机器学习的行人检测技术研究

针对行人检测技术在智能交通系统中的应用,为了提高行人检测方法的有效性、实时性和准确性,将稀疏表达应用到图像的特征压缩中,提出一种基于HOG和LTP特征训练SVM分类器进行行人检测的方法。基于HOG和LTP特征训练SVM分类器进行行人检测的方法有效地结合了图像的梯度特征和纹理特征,利用稀疏表达进行特征数据的压缩可以有效地加速算法。实验结果表明,提出的算法具有精度高、速度快等优点。     

Space‐Time Co‐Segmentation of Articulated Point Cloud Sequences

Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose a local‐to‐global approach to co‐segment point cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space‐time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.     

代码/需求行为差异检测

为解决软件开发后期（维护/演化）程序代码与需求模型不一致的问题,本文面向逆向需求工程,重点研究检测变更代码与原始需求模型之间行为差异的算法：首先沿用模型/代码转换技术,分析模型/代码比较原理,设计比早期连续型单向串行检测算法快2N倍（N为路径数）的离散型双向并行检测算法。然后采用该算法开发图形化需求/代码比较工具RCCT,并将其集成进综合需求建模系统（RMTS）,使动画建模、特性检测、模型转换、需求/代码差异检测等功能融为一体。最后,通过电子转账案例演示该工具的使用方法,并编写测试程序证明离散型双向并行算法不但比原始算法高效,而且更加可靠。     

基于概率交易模型的线下百货推荐

该文提出了一种新颖的概率交易模型PTM,针对线下百货进行个性化的推荐。传统的推荐模型,如K-近邻算法、矩阵分解等,或者仅利用局部的数据,使得模型面临线下数据极大的稀疏性挑战,或者忽略百货数据中的交易维度,使得模型损失了同一交易中多商品共现的强相关信息,最终导致它们在面对线下百货推荐问题时性能低下。针对以上的问题,本模型从交易的维度出发,建模交易记录中的共现模式,并利用全局的交易数据来学习商品的相关分量,在此基础上推断出用户的兴趣分布,实现个性化的推荐。在真实的线下百货交易数据上的实验结果表明,该模型能够极大地提高线下百货领域个性化推荐的准确性。
     

基于颜色和纹理特征的林火烟雾识别

为了实现森林火灾的智能化预警,提出了基于颜色和纹理特征的林火烟雾识别方法.首先使用颜色特征确定烟雾疑似区域,随后采用局部二值模式方差(Local Binary Pattern Variance,LBPV)提取疑似区域纹理的不规则度特征并产生LBP图像.然后利用小波变换分解LBP图像并提取模糊度、复杂度和相关度特征.最后利用支持向量机(Support Vector Machine,SVM)进行烟雾识别.结果证明,颜色结合纹理特征方法可以有效实现林火烟雾的识别,为林火烟雾识别研究提供了一种有效方案.     

Dual graph regularized NMF model for social event detection from Flickr data

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.     

A Differential Game Based Guidance Law for an Accelerating Exoatmospheric Missile

A differential game‐based guidance law design is proposed for a class of accelerating exoatmospheric missiles. By analyzing the nonlinear dynamic equations, a class of motions that does not require maneuver effort is derived for the missile. Using this property, a two phase guidance scheme can be derived that permits a perfect interception. In Phase 1, the missile is put in collision course with the target, which means in Phase 2 the missile should intercept the target with a straight line flight course, at a specified terminal body angle. Differential game based guidance laws are derived respectively for the two phases to guarantee the implementation of the guidance scheme. Simulation results show that the methodology provides robust interception performance.     

On design optimization for structural crashworthiness and its state of the art

Optimization for structural crashworthiness and energy absorption has become an important topic of research attributable to its proven benefits to public safety and social economy. This paper provides a comprehensive review of the important studies on design optimization for structural crashworthiness and energy absorption. First, the design criteria used in crashworthiness and energy absorption are reviewed and the surrogate modeling to evaluate these criteria is discussed. Second, multiobjective optimization, optimization under uncertainties and topology optimization are reviewed from concepts, algorithms to applications in relation to crashworthiness. Third, the crashworthy structures are summarized, from generically novel structural configurations to industrial applications. Finally, some conclusions and recommendations are provided to enable academia and industry to become more aware of the available capabilities and recent developments in design optimization for structural crashworthiness and energy absorption.

     

Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Structural and Multidisciplinary Optimization - Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve...