砖结构抗震修复加固计算

本文在分析有关单位现有试验资料的基础上,提出了用水泥砂浆抹面、钢筋网水泥砂浆抹面及外加钢筋混凝土柱修复加固砖墙,用钢筋网水泥砂浆抹面及外包角钢加固砖柱,用外包竖向和环向扁钢加固砖烟囱的计算方法。与现有的计算方法相比,本文所述方法的计算结果与试验值更为接近。     

Ajax技术在教学论坛制作中的应用

当用户访问传统的ASP技术制作的论坛,并进行多种操作时,会多次重载页面或在不同页面间跳转。造成用户视觉上的不舒服以及花费较多的等待时间。采用Ajax技术可以很好的解决这样的问题,本文给出了用Ajax技术实现教学论坛部分功能的设计方法,使访问者获得更好的Web体验。     

基于几何-语义联合约束的动态环境视觉SLAM算法

传统视觉同步定位和地图构建（Simultaneous localization and mapping, SLAM）算法建立在静态环境假设的基础之上,当场景中出现动态物体时,会影响系统稳定性,造成位姿估计精度下降。现有方法大多基于概率统计和几何约束来减轻少量动态物体对视觉SLAM系统的影响,但是当场景中动态物体较多时,这些方法失效。针对这一问题,本文提出了一种将动态视觉SLAM算法与多目标跟踪算法相结合的方法。首先采用实例语义分割网络,结合几何约束,在有效地分离静态特征点和动态特征点的同时,进一步实现多目标跟踪,改善跟踪结果,并能够获得运动物体的轨迹和速度矢量信息,从而能够更好地为机器人自主导航提供决策信息。在KITTI数据集上的实验表明,该算法在动态场景中相较ORB-SLAM2算法精度提高了28%。     

面向调度主站一体化的数据网格设计与实现

高性能电力应用致力于搭建用于调度数据的统一管理、集中处理平台。数据网格易于海量数据共享的特点使其适用于实现调度主站一体化。构建了调度主站数据网格的基本架构,利用网格软件Globus Toolkit建立网格环境,在此基础上配置数据库访问中间件OGSA-DAI来建立面向调度主站一体化的数据网格实验平台。     

具有词判别力学习能力的短文本聚类概率模型研究

社交媒体的广泛使用使短文本聚类成为一个重要的研究课题。但短文本词向量的高维、稀疏性限制了传统文本聚类方法在短文本中的效果,并且由于词的稀疏性,词对簇结构的判别能力对短文本类结构的学习显得尤为重要。本文我们提出了一种基于概率模型的具有词判别力学习能力的短文本聚类框架,并在经典文本聚类模型LDA(Ldatant Drichilet Allocation)、BTM(Biterm Topic Model)和GSDMM(Gibbs Sampling Drichilet Mutitional Mixture model)模型中验证了词判别力学习对类结构学习的有效性。通过Gibbs采样算法对模型中的参数进行求解。最后在真实数据集上的实验结果显示具有词判别力学习的概率模型可以提高已有模型的聚类效果。     

幽门螺杆菌TNF-α诱导蛋白的生物信息学分析

肿瘤坏死因子诱导蛋白Tipα是幽门螺杆菌特有的一个膜相关蛋白.它可以激活核因子κB,并诱导多种细胞因子和趋化因子的表达,导致胃炎和随后的肿瘤发生.它是幽门螺杆菌和胃癌发生之间的关键分子.本文利用生物信息学工具分析Tipα序列,预测其信号肽、跨膜区、疏水性、二级结构、三级结构等性质.结果:Tipα具有一段信号肽、脂蛋白信号肽酶切位点及脂盒模体.另外该蛋白没有跨膜区,可能是一个外周膜蛋白.Tipα的三级结构说明,该蛋白形成了一个致密的球状结构,Tipα的二级结构以α螺旋为主.     

Just-in-time defect prediction for software hunks

Just-in-time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just-in-time prediction for software files (JIT-F). JIT-F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just-in-time defect prediction for software hunks (JIT-H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT-H. Experimental results show that JIT-H is effective at predicting defects in software hunk changes. Compared with JIT-F, JIT-H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.