分布式电源多点接入配电系统的集成保护

针对分布式电源多点接入的配电系统,提出了一种基于暂态极性保护原理的集成保护新方案。暂态极性比较保护是运用小波变换提取故障暂态高频信号的某一频段信息作为故障判断的依据,通过比较暂态高频信号的极性,迅速准确判断出故障位置。将暂态极性比较保护原理应用于配电网集成保护方案中,以母线作为载体构建集成保护单元,利用本地故障信息和相邻保护单元故障信息对母线各出线以及线路两端信号的极性进行比较,从而对母线故障和线路故障进行判断。与此同时,集成网络保护单元提取多点信息,对整体区域暂态信号极性进行综合比较,实现对故障的快速定位与隔离,为本地保护提供后备保护。最后,利用MATLAB软件对其进行了仿真分析,仿真结果验证了此保护方案的正确性和可行性。     

基于词性标注序列特征提取的微博情感分类

传统的n-gram文本特征提取方法会产生高维度的特征向量,高维数据不但增大了分类的难度,同时也会增加分类的时间。针对这一问题,提出了一种基于词性（POS）标注序列的特征提取方法,根据词性序列能够代表一类文本的这一个特点,利用词性序列组作为文本的特征以达到降低特征维度的效果。在实验中,词性序列特征提取方法比n-gram特征提取方法至少提高了9%的分类精度,降低4816个维度。实验结果表明,该方法能够适用于微博情感分类。     

网络产品评论挖掘研究

以有效分析和挖掘网络产品评论中的用户观点从而为消费者和商家均提供有价值的信息为目的,提出了网络产品评论挖掘的步骤和方法,并在用户产品评论分析的基础上,进一步对产品特征词的关注度和极性进行分析,实现了更加全面地产品评论挖掘．最后以iphone 4s为例对所提出的方法进行了实验,验证了该方法的可行性．     

基于语言结构和情感极性的虚假评论识别

随着电子商务的发展,识别网络中的虚假评论意义重大。传统的启发式策略或全监督学习算法不能有效地解决该问题。虚假评论与真实评论在语言结构和情感极性上存在差异,提出基于遗传算法对语言结构及情感极性特征进行优化选择,并利用选取的特征结合无监督硬、软聚类算法对虚假评论进行识别。实验结果验证了所提算法的有效性。     

煤矿谐振接地系统暂态脉冲特征研究

通过推导煤矿单变电所和多变电所模式下谐振接地系统发生单相接地故障时各支路故障时刻的零序电流表达式,分析了故障线路与非故障线路的暂态脉冲极性关系,以及暂态脉冲的幅度及脉冲宽度与接地电阻、线路对地分布电容和故障时刻之间的关系,并给出了相应的表达式。故障线路的暂态脉冲幅度最大且极性与非故障线路的暂态脉冲极性相反;故障时刻暂态脉冲的幅度与接地电阻成反比,与故障时刻故障相的相位角正弦值成正比;所有非故障线路的暂态脉冲宽度一致,且小于故障线路的暂态脉冲宽度,暂态脉冲宽度与接地电阻成正比。Matlab/Simulink仿真实验验证了理论分析的正确性。     

After-effects of TFT-LCD display polarity and display colour on the detection of low-contrast objects

Participants performed a word–non-word discrimination task within a car control display emulated on a thin film transistor liquid-crystal display (TFT-LCD). The task simulated an information read-out from a TFT-LCD-based instrument panel. Subsequently, participants performed a low-contrast object detection task that simulated the detection of objects during night-time driving. In experiment 1, words/non-words were presented black-on-white (positive polarity) or white-on-black (negative polarity). In experiments 2 and 3, display colour was additionally manipulated. A positive polarity advantage in the discrimination task was consistently observed. In contrast, positive displays interfered more than negative displays with subsequent detection. The detrimental after-effect of positive polarity displays was strong with white and blue, reduced with amber and absent with red displays. Subjective measures showed a preference for blue over red, but a slight advantage for amber over blue. Implications for TFT-LCD design are derived from the results.

Statement of Relevance: When using TFT-LCDs as car instrument panels, positive polarity red TFT-LCDs are very likely to lead to good instrument readability while at the same time minimising – relative to other colours – the negative effects of an illuminated display on low-contrast object detection during night-time driving.     

基于连词的维吾尔语情感词库扩展研究

极性情感词是准确分析维吾尔文倾向性的基础资源。该文在前期构建的维吾尔语褒贬情感词典基础上进行网络情感词的自动扩展研究。首先分析维吾尔语情感表达的语言特征,总结了连词、程度副词与情感词的搭配规律,并基于此规律设计从情感语料库中获取候选情感词的算法,形成候选情感词库;最后再利用维吾尔语连词的特性,结合已创建的情感词典和维吾尔语反义词词典,以互联网作为超大规模语料库,设计基于搜索引擎的情感词极性判别算法,根据算法得分判别候选情感词的极性,再将其扩展到已构建的褒贬情感词库。实验结果表明,与扩展前的情感词库相比,使用互联网文本语料扩展后的情感词库后进行维吾尔语句子倾向性测评的准确率和召回率均有明显提高。     

基于深度记忆网络的特定目标情感分类

只基于注意力机制的深度记忆网络不能有效处理目标上下文情感依赖于具体目标的情况,为了解决该问题,提出了一个对目标敏感的深度记忆网络模型。该模型利用注意力机制来获取决定目标上下文情感的信息,然后通过交互模块将上下文情感表示和上下文与目标之间的交互信息融合成分类特征,最后分类得到目标的情感极性。在SemEval 2014 task4的两个数据集上进行实验,实现了比只基于注意力机制的DMN模型明显更好的◢F▼◣▽1值。实验结果表明,在解决上下文情感依赖于具体目标的问题时,考虑上下文与目标之间的交互信息是有效的。     

基于整体退火遗传算法的低功耗最佳极性搜索

针对n变量逻辑函数在不同极性下所对应的XNOR/OR电路功耗和面积不同的特点,首先用信号概率传递算法和多输入XNOR/OR(同或/或)门的低功耗分解算法建立了XNOR/OR电路的功耗估计模型.在此基础上,将基于列表技术的极性转换算法和整体退火遗传算法相结合,提出了一种针对大规模XNOR/OR电路的低功耗最佳极性搜索算法.对8个较大规模MCNC Benchmark电路测试表明,该算法搜索到的最佳极性所对应的XNOR/OR电路与极性0时的XNOR/OR电路相比,平均节省功耗和面积分别达到了84.4%和65.2%.     

A multi-layer approach to opinion polarity classification using augmented semantic tree kernels

Tremendous increase in user-generated content (UGC) published over the web in the form of natural language has posed a formidable challenge to automated information extraction (IE) and content analysis (CA). Techniques based on tree kernels (TK) have been successfully used for modelling semantic compositionality in many natural language processing (NLP) applications. Essentially, these techniques obtain the similarity of two production rules based on exact string comparison between the peer nodes. However, semantically identical tree fragments are forbidden even though they can contribute to the similarity of two trees. A mechanism needs to be addressed that accounts for the similarity of rules with varied syntax and vocabulary holding knowledge that are relatively analogous. In this paper, a hierarchical framework based on document object model (DOM) tree and linguistic kernels that jointly address subjectivity detection, opinion extraction and polarity classification is addressed. The model proceeds in three stages: during first stage, the contents of each DOM tree node is analysed to estimate the complexity of vocabulary and syntax using readability test. In second stage, the semantic tree kernels extended with word embeddings are used to classify nodes containing subjective and objective content. Finally, the content returned to be subjective is further examined for opinion polarity classification using fine-grained linguistic kernels. The efficiency of the proposed model is demonstrated through a series of experiments being conducted. The results reveal that the proposed polarity-enriched tree kernel (PETK) results in better prediction performance compared to the conventional tree kernels.