Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.     

Studying the needed effort for identifying duplicate issues

Many recent software engineering papers have examined duplicate issue reports. Thus far, duplicate reports have been considered a hindrance to developers and a drain on their resources. As a result, prior research in this area focuses on proposing automated approaches to accurately identify duplicate reports. However, there exists no studies that attempt to quantify the actual effort that is spent on identifying duplicate issue reports. In this paper, we empirically examine the effort that is needed for manually identifying duplicate reports in four open source projects, i.e., Firefox, SeaMonkey, Bugzilla and Eclipse-Platform. Our results show that: (i) More than 50 % of the duplicate reports are identified within half a day. Most of the duplicate reports are identified without any discussion and with the involvement of very few people; (ii) A classification model built using a set of factors that are extracted from duplicate issue reports classifies duplicates according to the effort that is needed to identify them with a precision of 0.60 to 0.77, a recall of 0.23 to 0.96, and an ROC area of 0.68 to 0.80; and (iii) Factors that capture the developer awareness of the duplicate issue’s peers (i.e., other duplicates of that issue) and textual similarity of a new report to prior reports are the most influential factors in our models. Our findings highlight the need for effort-aware evaluation of approaches that identify duplicate issue reports, since the identification of a considerable amount of duplicate reports (over 50 %) appear to be a relatively trivial task for developers. To better assist developers, research on identifying duplicate issue reports should put greater emphasis on assisting developers in identifying effort-consuming duplicate issues.     

基于灰关联混合蛙跳算法的雷达波形设计

提出了一种基于灰关联混合蛙跳算法的雷达波形设计方法,以混合蛙跳算法为主体,在局部更 新算子中引入遗传算法的遗传算子,并改进原始蛙跳算法的分组方法,丰富了种群的多样性,同时引入灰关联综合评价法则对适应度函数值加以关联度分析。文中以设计具有低自相关旁瓣和互相关特性的正交多相编码为例,将该算法用于雷达波形设计中。仿真结果表明使用本文算法产生 的波形具备较好的低自相关特性和互相关特性,表明了该算法是有效和可行的。     

基于余弦距离的局部敏感哈希的KNN算法在中文文本上的快速分类

文本分类是文本挖掘中最重要的研究内容之一。为了克服目前以距离衡量的近似分类算法在海量数据下耗费大量时间的缺陷,提出了结合基于余弦距离的局部敏感哈希的方式将KNN算法在TF-IDF下对中文文本进行快速分类。同时结合文本数据的特性给出了不同的哈希函数级联方式分别进行实验。在实验过程采用了布尔向量的方式规避重复访问,使分类的结果在可以允许的范围内,分类速度比原始KNN提高了许多。     

水利水电工程和周围地区发生地震概率高低的研讨

目前,水利水电工程正在加速发展,水利水电工程在国家发展中占有重要地位,地震发生的强度和时间的长短直接影响着水利水电工程的建设。为此,对水利水电工程和周围地区发生地震的概率等问题进行了研究。     

关于雾霾污染的现状及治理方案的探讨＊

雾霾是雾与霾的混合物,雾霾湿度相对较大时,雾所占的成分居多;而在相对湿度为80%～90%时,霾所占的比例较大。在雾霾天气下,人们在室外的能见度特别低,给行车出行带来很大的不便,同时,还会给人们的健康带来严重的威胁。主要对雾霾污染的现状进行了分析,并重点提出治理雾霾污染的有效方案。     

基于ARMA NExT和稳定图方法的飞行器工作模态指示研究

研究了基于飞行遥测数据,使用环境激励模态辨识方法辨识系统的模态参数时,挑选真实模态的方法.首先,详细介绍了ARMA-NEx T环境激励模态辨识方法的理论.接着,给出了模态指示因素,并详细分析了基于稳定图方法、频域和时频分析方法的真实模态筛选的方法.最后通过算例研究了飞行模态筛选的过程.研究发现,通过该方法得出结果与频域和时频分析结果基本一致.     

光伏提水技术研究与应用

光伏提水是涉及太阳能采集与变换、电力电子、电机、水泵等多门学科专业综合配套的一门新兴技术,因其符合新能源战略发展的需要而备受关注和重视。基于研究和推广应用需要,在回顾了光伏提水技术发展历程的基础上,介绍了近年国内出现的几种有代表性的光伏提水装置,对它们的类型和系统结构进行了分析,探讨了光伏提水技术在我国特别是西部干旱缺水地区推广应用的前景和价值。     

水电站半地下厂房结构抗震设计方案比选

赤道几内亚吉部洛水电站厂房为半地下式厂房,原设计方案考虑在机组间设永久变形缝,但由于该厂房上游地下水位很高,厂房底部水头超过30 m,所以在上游边墙和楼板的分缝处会出现局部应力和变形过大。为减小应力和变形,另考虑后浇带方案,以增强其整体性。此方案下厂房成为一个整体结构,不存在变形缝,但同时也形成了超过50 m的大跨度混凝土高边墙和楼板结构,超出了厂房设计规范中允许的最大跨度,有必要对其进行论证。采用地震反应谱法并考虑动静叠加,对两方案在设计地震作用下的地震响应作了对比分析。结果表明：后浇带方案不仅能减小厂房局部应力和变形,还能使厂房满足抗震要求,优于原设缝方案。为类似存在高水头压力问题的水电站厂房设计提供可靠参考和借鉴。